| Title: | Pretest Probability for Coronary Artery Disease |
|---|---|
| Description: | An application to calculate a patient's pretest probability (PTP) for obstructive Coronary Artery Disease (CAD) from a collection of guidelines or studies. Guidelines usually comes from the American Heart Association (AHA), American College of Cardiology (ACC) or European Society of Cardiology (ESC). Examples of PTP scores that comes from studies are the 2020 Winther et al. basic, Risk Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery Calcium Score-weighted Clinical Likelihood (CACS-CL) models <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>. As diagnosis of CAD involves a costly and invasive coronary angiography procedure for patients, having a reliable PTP for CAD helps doctors to make better decisions during patient management. This ensures high risk patients can be diagnosed and treated early for CAD while avoiding unnecessary testing for low risk patients. |
| Authors: | Jeremy Selva [aut, cre] (ORCID: <https://orcid.org/0000-0002-4498-2662>) |
| Maintainer: | Jeremy Selva <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.0 |
| Built: | 2026-05-13 05:47:45 UTC |
| Source: | https://github.com/jauntyjjs/pretestcad |
NA
This is equivalent to arg_match but skip NA
arg_match0_allow_na( arg, values, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )arg_match0_allow_na( arg, values, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )
arg |
A symbol referring to an argument accepting strings. |
values |
A character vector of possible values that |
arg_nm |
Same as |
error_call |
The execution environment of a currently
running function, e.g. |
The string supplied to arg.
# No error input = "male" arg_match0_allow_na(input, values = c("female","male")) # Allow NA input = NA arg_match0_allow_na(input, values = c("female","male")) # Error as M is not female or male input = "emale" try(arg_match0_allow_na(input, values = c("female","male")))# No error input = "male" arg_match0_allow_na(input, values = c("female","male")) # Allow NA input = NA arg_match0_allow_na(input, values = c("female","male")) # Error as M is not female or male input = "emale" try(arg_match0_allow_na(input, values = c("female","male")))
NA
This is equivalent to arg_match but an
integer variable is needed and skip NA.
arg_match0_integer( arg, values, allow_na = TRUE, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )arg_match0_integer( arg, values, allow_na = TRUE, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )
arg |
A symbol referring to an argument accepting strings. |
values |
A character vector of possible values that |
allow_na |
Input boolean to determine if |
arg_nm |
Same as |
error_call |
The execution environment of a currently
running function, e.g. |
The integer supplied to arg.
# No error input = 5 arg_match0_integer(input, values = c(0:5)) # Allow NA input = NA arg_match0_integer(input, values = c(0:5)) # Error as 0 is not within 0 and 5 input = 6 try(arg_match0_integer(input, values = c(0:5))) # Error as NULL is not within 0 and 5 input = NULL try(arg_match0_integer(input, values = c(0:5))) # Error as NA is not within 0 and 5 and allow_na is FALSE input = NA try(arg_match0_integer(input, values = c(0:5), allow_na = FALSE))# No error input = 5 arg_match0_integer(input, values = c(0:5)) # Allow NA input = NA arg_match0_integer(input, values = c(0:5)) # Error as 0 is not within 0 and 5 input = 6 try(arg_match0_integer(input, values = c(0:5))) # Error as NULL is not within 0 and 5 input = NULL try(arg_match0_integer(input, values = c(0:5))) # Error as NA is not within 0 and 5 and allow_na is FALSE input = NA try(arg_match0_integer(input, values = c(0:5), allow_na = FALSE))
NA Argument For Non-missing ListProvides an error message if the argument provided is NA
if a non-missing list is provided
arg_match0_no_na_error_message( arg, values, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )arg_match0_no_na_error_message( arg, values, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )
arg |
A symbol referring to an argument accepting strings. |
values |
A character vector of possible values that |
arg_nm |
Same as |
error_call |
The execution environment of a currently
running function, e.g. |
An error message if the argument provided is NA
if a non-missing list is provided. Else if will return NULL invisibly,
regardless if arg has a match with the elements in values or not.
# Error as input is NA but value list provided has no NA input = NA try(arg_match0_no_na_error_message(input, values = c("female","male"))) # No error as value list provided has NA input = NA arg_match0_allow_na(input, values = c("female","male", NA)) # No error as input is not NA input = "male" arg_match0_allow_na(input, values = c("female","male", NA))# Error as input is NA but value list provided has no NA input = NA try(arg_match0_no_na_error_message(input, values = c("female","male"))) # No error as value list provided has NA input = NA arg_match0_allow_na(input, values = c("female","male", NA)) # No error as input is not NA input = "male" arg_match0_allow_na(input, values = c("female","male", NA))
TRUE or FALSE vector but skip NA
This is equivalent to arg_match but a
boolean variable is needed and skip NA.
arg_match0_true_or_false( arg, allow_na = TRUE, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )arg_match0_true_or_false( arg, allow_na = TRUE, arg_nm = rlang::caller_arg(arg), error_call = rlang::caller_env() )
arg |
A symbol referring to an argument accepting strings. |
allow_na |
Input boolean to determine if |
arg_nm |
Same as |
error_call |
The execution environment of a currently
running function, e.g. |
The TRUE or FALSE value supplied to arg.
# No error input = TRUE arg_match0_true_or_false(input) # Allow NA input = NA arg_match0_true_or_false(input) # Error as 0 is not TRUE or FALSE input = 0 try(arg_match0_true_or_false(input)) # Error as 1 is not TRUE or FALSE input = 1 try(arg_match0_true_or_false(input)) # Error as NULL is not TRUE or FALSE input = NULL try(arg_match0_true_or_false(input)) # Error as NA is not TRUE or FALSE and allow_na is FALSE input = NA try(arg_match0_true_or_false(input, allow_na = FALSE))# No error input = TRUE arg_match0_true_or_false(input) # Allow NA input = NA arg_match0_true_or_false(input) # Error as 0 is not TRUE or FALSE input = 0 try(arg_match0_true_or_false(input)) # Error as 1 is not TRUE or FALSE input = 1 try(arg_match0_true_or_false(input)) # Error as NULL is not TRUE or FALSE input = NULL try(arg_match0_true_or_false(input)) # Error as NA is not TRUE or FALSE and allow_na is FALSE input = NA try(arg_match0_true_or_false(input, allow_na = FALSE))
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the American College of Cardiology Foundation, American Heart Association, American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons 2012 guidelines.
calculate_aha_2012_tbl_9_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_aha_2012_tbl_9_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
output |
Input text to indicate the how pre-test probability results be expressed Default: c("numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
The predictive model used to create the guidelines are based on patients from the Diamond and Forrester and the Coronary Artery Surgery Study.
An integer or percentage representing the patient's PTP for obstructive CAD based on the ACCF/AHA/ACP/AATS/PCNA/SCAI/STS 2012 guidelines.
# 35 year old female with typical chest pain calculate_aha_2012_tbl_9_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_aha_2012_tbl_9_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )# 35 year old female with typical chest pain calculate_aha_2012_tbl_9_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_aha_2012_tbl_9_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the American Heart Association/American College of Cardiology (AHA/ACC) 2021 guidelines.
calculate_aha_2021_ptp( age, sex, have_dyspnoea, have_chest_pain, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_have_chest_pain_no = c("no"), label_have_chest_pain_yes = c("yes"), label_have_chest_pain_unknown = c(NA, NaN) )calculate_aha_2021_ptp( age, sex, have_dyspnoea, have_chest_pain, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_have_chest_pain_no = c("no"), label_have_chest_pain_yes = c("yes"), label_have_chest_pain_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
have_chest_pain |
The value of variable in the parameters |
output |
Input text to indicate the how pre-test probability results be expressed Default: c("grouping", "numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_have_chest_pain_no |
Label(s) for patient not having chest pain symptoms.
Default: |
label_have_chest_pain_yes |
Label(s) for patient having chest pain symptoms.
Default: |
label_have_chest_pain_unknown |
Label(s) for patient with unknown chest pain symptoms.
Default: |
The predictive model used to create the guidelines are based on patients from European countries with low cardiovascular disease (CVD) risk.
If the patient has both dyspnoea and a particular chest pain type (typical, atypical, nonanginal), The chest pain type will take precedence over dyspnoea
An integer, percentage or category representing the patient's PTP for obstructive CAD
based on the AHA/ACC 2021 guidelines.
See parameter option output for more information.
# 35 year old female with chest pain calculate_aha_2021_ptp( age = 35, sex = "female", have_dyspnoea = "no", have_chest_pain = "yes", output = "percentage" ) # 75 year old male with only dyspnoea calculate_aha_2021_ptp( age = 75, sex = "male", have_dyspnoea = "yes", have_chest_pain = "no", output = "percentage" )# 35 year old female with chest pain calculate_aha_2021_ptp( age = 35, sex = "female", have_dyspnoea = "no", have_chest_pain = "yes", output = "percentage" ) # 75 year old male with only dyspnoea calculate_aha_2021_ptp( age = 75, sex = "male", have_dyspnoea = "yes", have_chest_pain = "no", output = "percentage" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2011 CAD Consortium 1 (CAD1) basic model.
calculate_cad1_2011_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_cad1_2011_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
The predictive model is based on patients from 14 hospitals in Europe and the United States.
This model is also called the updated Diamond-Forrester model.
A numeric value representing the patient's PTP for obstructive CAD based on the 2011 CAD Consortium 1 (CAD1) basic model.
# 40 year old female with typical chest pain calculate_cad1_2011_ptp( age = 40, sex = "female", chest_pain_type = "typical" )# 40 year old female with typical chest pain calculate_cad1_2011_ptp( age = 40, sex = "female", chest_pain_type = "typical" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2012 CAD Consortium 2 (CAD2) basic model.
calculate_cad2_2012_basic_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_cad2_2012_basic_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
The predictive model is based on patients from 18 hospitals in Europe and the United States.
A numeric value representing the patient's PTP for obstructive CAD based on the 2012 CAD Consortium 2 (CAD2) basic model.
# 40 year old female with typical chest pain calculate_cad2_2012_basic_ptp( age = 40, sex = "female", chest_pain_type = "typical" )# 40 year old female with typical chest pain calculate_cad2_2012_basic_ptp( age = 40, sex = "female", chest_pain_type = "typical" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2012 CAD Consortium 2 (CAD2) clinical and coronary calcium score (CCS) model.
calculate_cad2_2012_clinical_ccs_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, coronary_calcium_score, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )calculate_cad2_2012_clinical_ccs_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, coronary_calcium_score, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
coronary_calcium_score |
Input non-negative numeric to indicate the total coronary calcium score of the patient. |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
The predictive model is based on patients from 18 hospitals in Europe and the United States.
A numeric value representing the patient's PTP for obstructive CAD based on the 2012 CAD Consortium 2 (CAD2) clinical and coronary calcium score (CCS) model.
# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia, # a non-smoker and a coronary calcium score of 0 calculate_cad2_2012_clinical_ccs_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", coronary_calcium_score = 0 )# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia, # a non-smoker and a coronary calcium score of 0 calculate_cad2_2012_clinical_ccs_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", coronary_calcium_score = 0 )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2012 CAD Consortium 2 (CAD2) clinical model.
calculate_cad2_2012_clinical_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )calculate_cad2_2012_clinical_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
The predictive model is based on patients from 18 hospitals in Europe and the United States.
A numeric value representing the patient's PTP for obstructive CAD based on the 2012 CAD Consortium 2 (CAD2) clinical model.
# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia # and a non-smoker calculate_cad2_2012_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no" )# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia # and a non-smoker calculate_cad2_2012_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no" )
A function used to calculate the number of risk factors the patient has. This is used to calculate the pretest probability of coronary artery disease (CAD) based on the 2015 CONFIRM Risk Score.
calculate_confirm_2015_num_of_rf( have_typical_chest_pain, have_diabetes, have_hypertension, have_family_history, is_current_smoker, max_na = 0, label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_is_current_smoker_no = c("no"), label_is_current_smoker_yes = c("yes"), label_is_current_smoker_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_confirm_2015_num_of_rf( have_typical_chest_pain, have_diabetes, have_hypertension, have_family_history, is_current_smoker, max_na = 0, label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_is_current_smoker_no = c("no"), label_is_current_smoker_yes = c("yes"), label_is_current_smoker_unknown = c(NA, NaN), error_call = rlang::caller_env() )
have_typical_chest_pain |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_family_history |
The value of variable in the parameters
|
is_current_smoker |
The value of variable in the parameters
|
max_na |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
label_have_typical_chest_pain_no |
Label(s) for patient not having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_yes |
Label(s) for patient having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_unknown |
Label(s) for patient having unknown typical chest pain symptom. |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_is_current_smoker_no |
Label(s) for patients who are not current smokers.
Default: |
label_is_current_smoker_yes |
Label(s) for patients who are current smokers.
Default: |
label_is_current_smoker_unknown |
Label(s) for patient with unknown smoking status. |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the number of risk factors the patient has.
It can also be NA if the number of missing risk factors exceeds the max_na
input value
calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "yes", have_diabetes = "yes", have_hypertension = "yes", have_family_history = "yes", is_current_smoker = "no" ) calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "no", have_diabetes = "no", have_hypertension = "no", have_family_history = NA, is_current_smoker = "no", max_na = 0 ) calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "no", have_diabetes = "no", have_hypertension = "no", have_family_history = NA, is_current_smoker = "no", max_na = 1 )calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "yes", have_diabetes = "yes", have_hypertension = "yes", have_family_history = "yes", is_current_smoker = "no" ) calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "no", have_diabetes = "no", have_hypertension = "no", have_family_history = NA, is_current_smoker = "no", max_na = 0 ) calculate_confirm_2015_num_of_rf( have_typical_chest_pain = "no", have_diabetes = "no", have_hypertension = "no", have_family_history = NA, is_current_smoker = "no", max_na = 1 )
This function returns a patient's risk score for obstructive coronary artery disease based on the 2015 CONFIRM Risk Score.
calculate_confirm_2015_ptp( age, sex, have_typical_chest_pain, have_diabetes, have_hypertension, have_family_history, is_current_smoker, max_na_num_of_rf = 0, output = c("text", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_is_current_smoker_no = c("no"), label_is_current_smoker_yes = c("yes"), label_is_current_smoker_unknown = c(NA, NaN) )calculate_confirm_2015_ptp( age, sex, have_typical_chest_pain, have_diabetes, have_hypertension, have_family_history, is_current_smoker, max_na_num_of_rf = 0, output = c("text", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_is_current_smoker_no = c("no"), label_is_current_smoker_yes = c("yes"), label_is_current_smoker_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
have_typical_chest_pain |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_family_history |
The value of variable in the parameters
|
is_current_smoker |
The value of variable in the parameters
|
max_na_num_of_rf |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
output |
Input text to indicate the how pre-test probability results be expressed Default: c("text", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_typical_chest_pain_no |
Label(s) for patient not having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_yes |
Label(s) for patient having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_unknown |
Label(s) for patient having unknown typical chest pain symptom. |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_is_current_smoker_no |
Label(s) for patients who are not current smokers.
Default: |
label_is_current_smoker_yes |
Label(s) for patients who are current smokers.
Default: |
label_is_current_smoker_unknown |
Label(s) for patient with unknown smoking status. |
The predictive model is based on CCTA images from 9093 patients from Phase I of the Coronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter (CONFIRM) registry.
A numeric value representing the patient's risk score for obstructive CAD based on the 2015 CONFIRM Risk Score.
# 30 years old male current smoker with typical chest pain calculate_confirm_2015_ptp( age = 30, sex = "male", have_typical_chest_pain = "yes", have_diabetes = "no", have_hypertension = "no", have_family_history = "no", is_current_smoker = "yes", max_na_num_of_rf = 0, output = "percentage" )# 30 years old male current smoker with typical chest pain calculate_confirm_2015_ptp( age = 30, sex = "male", have_typical_chest_pain = "yes", have_diabetes = "no", have_hypertension = "no", have_family_history = "no", is_current_smoker = "yes", max_na_num_of_rf = 0, output = "percentage" )
This function returns a patient's pre-test probability (PTP) of severe (>75% luminal diameter narrowing of the left main coronary artery) coronary artery disease based on the 1993 Duke Clinical Score.
calculate_dcs_1993_lm_cad_ptp( age, sex, have_typical_chest_pain, have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, duration_of_cad_symptoms_year, max_na_vascular_disease_index = 0, max_age = 65, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN) )calculate_dcs_1993_lm_cad_ptp( age, sex, have_typical_chest_pain, have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, duration_of_cad_symptoms_year, max_na_vascular_disease_index = 0, max_age = 65, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
have_typical_chest_pain |
The value of variable in the parameters
|
have_peripheral_vascular_disease |
The value of variable in the parameters
|
have_cerebrovascular_disease |
The value of variable in the parameters
|
have_carotid_bruits |
The value of variable in the parameters
|
duration_of_cad_symptoms_year |
Input integer to indicate the duration of coronary artery disease symptoms in years. |
max_na_vascular_disease_index |
Input integer 0 to 3 to indicate the maximum number of
missing disease history to tolerate before outputting an |
max_age |
Input positive integer to
indicate the maximum age to tolerate before outputting an |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_typical_chest_pain_no |
Label(s) for patient not having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_yes |
Label(s) for patient having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_unknown |
Label(s) for patient having unknown typical chest pain symptom. |
label_have_pvd_no |
Label(s) for patient not having peripheral vascular disease.
Default: |
label_have_pvd_yes |
Label(s) for patient having peripheral vascular disease.
Default: |
label_have_pvd_unknown |
Label(s) for patient
having unknown peripheral vascular disease.
Default: |
label_have_cvd_no |
Label(s) for patient not having cerebrovascular disease.
Default: |
label_have_cvd_yes |
Label(s) for patient having cerebrovascular disease.
Default: |
label_have_cvd_unknown |
Label(s) for patient
having unknown cerebrovascular disease.
Default: |
label_have_carotid_bruits_no |
Label(s) for patient not having carotid bruits.
Default: |
label_have_carotid_bruits_yes |
Label(s) for patient having carotid bruits.
Default: |
label_have_carotid_bruits_unknown |
Label(s) for patient
having unknown carotid bruits.
Default: |
The predictive model is based on patients referred for cardiac catheterisation between 1969 and 1983.
A numeric value representing the patient's PTP for left main disease (>75% luminal diameter narrowing of the left main coronary artery) based on the 1993 Duke Clinical Score.
# 40 year old female with typical chest pain for one year, # She has peripheral vascular and cerebrovascular disease. calculate_dcs_1993_lm_cad_ptp( age = 40, sex = "female", have_typical_chest_pain = "yes", have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", duration_of_cad_symptoms_year = 1, )# 40 year old female with typical chest pain for one year, # She has peripheral vascular and cerebrovascular disease. calculate_dcs_1993_lm_cad_ptp( age = 40, sex = "female", have_typical_chest_pain = "yes", have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", duration_of_cad_symptoms_year = 1, )
A function used to calculate the patient's pain index. This is used to calculate the likelihood of severe coronary artery disease in the Duke Clinical Score 1993 paper.
calculate_dcs_1993_pain_index( have_typical_chest_pain, frequency_of_angina_pain_per_week, have_progressive_angina, have_nocturnal_angina, have_q_waves, have_st_t_changes, max_na = 0, max_frequency_of_angina_pain_per_week = 35, label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_progressive_angina_no = c("no"), label_have_progressive_angina_yes = c("yes"), label_have_progressive_angina_unknown = c(NA, NaN), label_have_nocturnal_angina_no = c("no"), label_have_nocturnal_angina_yes = c("yes"), label_have_nocturnal_angina_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_dcs_1993_pain_index( have_typical_chest_pain, frequency_of_angina_pain_per_week, have_progressive_angina, have_nocturnal_angina, have_q_waves, have_st_t_changes, max_na = 0, max_frequency_of_angina_pain_per_week = 35, label_have_typical_chest_pain_no = c("no"), label_have_typical_chest_pain_yes = c("yes"), label_have_typical_chest_pain_unknown = c(NA, NaN), label_have_progressive_angina_no = c("no"), label_have_progressive_angina_yes = c("yes"), label_have_progressive_angina_unknown = c(NA, NaN), label_have_nocturnal_angina_no = c("no"), label_have_nocturnal_angina_yes = c("yes"), label_have_nocturnal_angina_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN), error_call = rlang::caller_env() )
have_typical_chest_pain |
The value of variable in the parameters
|
frequency_of_angina_pain_per_week |
Input integer to indicate the patient's frequency of angina per week. |
have_progressive_angina |
The value of variable in the parameters
|
have_nocturnal_angina |
The value of variable in the parameters
|
have_q_waves |
The value of variable in the parameters
|
have_st_t_changes |
The value of variable in the parameters
|
max_na |
Input integer 0 to 6 to indicate the maximum number of
missing symptoms to tolerate before outputting an |
max_frequency_of_angina_pain_per_week |
Input non-negative integer to
indicate the maximum frequency angina per week to tolerate before outputting an |
label_have_typical_chest_pain_no |
Label(s) for patient not having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_yes |
Label(s) for patient having
typical chest pain symptom.
Default: |
label_have_typical_chest_pain_unknown |
Label(s) for patient having unknown typical chest pain symptom. |
label_have_progressive_angina_no |
Label(s) for patient not having progressive angina.
Default: |
label_have_progressive_angina_yes |
Label(s) for patient having progressive angina.
Default: |
label_have_progressive_angina_unknown |
Label(s) for patient
having unknown progressive angina.
Default: |
label_have_nocturnal_angina_no |
Label(s) for patient not having nocturnal angina.
Default: |
label_have_nocturnal_angina_yes |
Label(s) for patient having nocturnal angina.
Default: |
label_have_nocturnal_angina_unknown |
Label(s) for patient
having unknown nocturnal angina.
Default: |
label_have_q_waves_no |
Label(s) for patient not having Q waves on ECG.
Default: |
label_have_q_waves_yes |
Label(s) for patient having Q waves on ECG.
Default: |
label_have_q_waves_unknown |
Label(s) for patient with unknown Q waves on ECG.
Default: |
label_have_st_t_changes_no |
Label(s) for patient not having ST-T changes on ECG.
Default: |
label_have_st_t_changes_yes |
Label(s) for patient having ST-T changes on ECG.
Default: |
label_have_st_t_changes_unknown |
Label(s) for patient with unknown ST-T changes on ECG.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the patient's pain index.
It can also be NA if the number of missing symptoms exceeds the max_na
input value or the frequency of angina per week exceed the
max_frequency_of_angina_pain_per_week input value.
calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = NA, have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = NA, have_q_waves = "no", have_st_t_changes = "no", max_na = 1, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 40, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 40, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = NA )calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = NA, have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 10, have_progressive_angina = "yes", have_nocturnal_angina = NA, have_q_waves = "no", have_st_t_changes = "no", max_na = 1, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 40, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = 35 ) calculate_dcs_1993_pain_index( have_typical_chest_pain = "yes", frequency_of_angina_pain_per_week = 40, have_progressive_angina = "yes", have_nocturnal_angina = "no", have_q_waves = "no", have_st_t_changes = "no", max_na = 0, max_frequency_of_angina_pain_per_week = NA )
A function used to calculate the patient's risk factor index. This is used to calculate the likelihood of severe coronary artery disease in the Duke Clinical Score 1993 paper.
calculate_dcs_1993_risk_factor_index( have_hypertension, have_dyslipidemia, have_diabetes, max_na = 0, label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_dcs_1993_risk_factor_index( have_hypertension, have_dyslipidemia, have_diabetes, max_na = 0, label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), error_call = rlang::caller_env() )
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
max_na |
Input integer 0 to 3 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the patient's risk factor index.
It can also be NA if the number of missing risk factors exceeds the max_na
input value.
calculate_dcs_1993_risk_factor_index( have_hypertension = "yes", have_dyslipidemia = "yes", have_diabetes = "no" ) calculate_dcs_1993_risk_factor_index( have_hypertension = NA, have_dyslipidemia = "yes", have_diabetes = "no", max_na = 0 ) calculate_dcs_1993_risk_factor_index( have_hypertension = NA, have_dyslipidemia = "yes", have_diabetes = "no", max_na = 1 )calculate_dcs_1993_risk_factor_index( have_hypertension = "yes", have_dyslipidemia = "yes", have_diabetes = "no" ) calculate_dcs_1993_risk_factor_index( have_hypertension = NA, have_dyslipidemia = "yes", have_diabetes = "no", max_na = 0 ) calculate_dcs_1993_risk_factor_index( have_hypertension = NA, have_dyslipidemia = "yes", have_diabetes = "no", max_na = 1 )
This function returns a patient's pre-test probability (PTP) of severe (>75% luminal diameter narrowing of all three major coronary arteries or of the left main coronary artery) coronary artery disease based on the 1993 Duke Clinical Score.
calculate_dcs_1993_severe_cad_ptp( age, sex, chest_pain_type, have_progressive_angina, have_nocturnal_angina, have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, have_hypertension, have_dyslipidemia, have_diabetes, have_q_waves, have_st_t_changes, frequency_of_angina_pain_per_week, duration_of_cad_symptoms_year, max_na_risk_factor_index = 0, max_na_pain_index = 0, max_na_vascular_disease_index = 0, max_frequency_of_angina_pain_per_week = 35, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_progressive_angina_no = c("no"), label_have_progressive_angina_yes = c("yes"), label_have_progressive_angina_unknown = c(NA, NaN), label_have_nocturnal_angina_no = c("no"), label_have_nocturnal_angina_yes = c("yes"), label_have_nocturnal_angina_unknown = c(NA, NaN), label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )calculate_dcs_1993_severe_cad_ptp( age, sex, chest_pain_type, have_progressive_angina, have_nocturnal_angina, have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, have_hypertension, have_dyslipidemia, have_diabetes, have_q_waves, have_st_t_changes, frequency_of_angina_pain_per_week, duration_of_cad_symptoms_year, max_na_risk_factor_index = 0, max_na_pain_index = 0, max_na_vascular_disease_index = 0, max_frequency_of_angina_pain_per_week = 35, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_progressive_angina_no = c("no"), label_have_progressive_angina_yes = c("yes"), label_have_progressive_angina_unknown = c(NA, NaN), label_have_nocturnal_angina_no = c("no"), label_have_nocturnal_angina_yes = c("yes"), label_have_nocturnal_angina_unknown = c(NA, NaN), label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_progressive_angina |
The value of variable in the parameters
|
have_nocturnal_angina |
The value of variable in the parameters
|
have_peripheral_vascular_disease |
The value of variable in the parameters
|
have_cerebrovascular_disease |
The value of variable in the parameters
|
have_carotid_bruits |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_q_waves |
The value of variable in the parameters
|
have_st_t_changes |
The value of variable in the parameters
|
frequency_of_angina_pain_per_week |
Input integer to indicate the patient's frequency of angina per week. |
duration_of_cad_symptoms_year |
Input integer to indicate the duration of coronary artery disease symptoms in years. |
max_na_risk_factor_index |
Input integer 0 to 3 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
max_na_pain_index |
Input integer 0 to 5 to indicate the maximum number of
missing symptoms to tolerate before outputting an |
max_na_vascular_disease_index |
Input integer 0 to 3 to indicate the maximum number of
missing disease history to tolerate before outputting an |
max_frequency_of_angina_pain_per_week |
Input non-negative integer to
indicate the maximum frequency angina per week to tolerate before outputting an |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_progressive_angina_no |
Label(s) for patient not having progressive angina.
Default: |
label_have_progressive_angina_yes |
Label(s) for patient having progressive angina.
Default: |
label_have_progressive_angina_unknown |
Label(s) for patient
having unknown progressive angina.
Default: |
label_have_nocturnal_angina_no |
Label(s) for patient not having nocturnal angina.
Default: |
label_have_nocturnal_angina_yes |
Label(s) for patient having nocturnal angina.
Default: |
label_have_nocturnal_angina_unknown |
Label(s) for patient
having unknown nocturnal angina.
Default: |
label_have_pvd_no |
Label(s) for patient not having peripheral vascular disease.
Default: |
label_have_pvd_yes |
Label(s) for patient having peripheral vascular disease.
Default: |
label_have_pvd_unknown |
Label(s) for patient
having unknown peripheral vascular disease.
Default: |
label_have_cvd_no |
Label(s) for patient not having cerebrovascular disease.
Default: |
label_have_cvd_yes |
Label(s) for patient having cerebrovascular disease.
Default: |
label_have_cvd_unknown |
Label(s) for patient
having unknown cerebrovascular disease.
Default: |
label_have_carotid_bruits_no |
Label(s) for patient not having carotid bruits.
Default: |
label_have_carotid_bruits_yes |
Label(s) for patient having carotid bruits.
Default: |
label_have_carotid_bruits_unknown |
Label(s) for patient
having unknown carotid bruits.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_q_waves_no |
Label(s) for patient not having Q waves on ECG.
Default: |
label_have_q_waves_yes |
Label(s) for patient having Q waves on ECG.
Default: |
label_have_q_waves_unknown |
Label(s) for patient with unknown Q waves on ECG.
Default: |
label_have_st_t_changes_no |
Label(s) for patient not having ST-T changes on ECG.
Default: |
label_have_st_t_changes_yes |
Label(s) for patient having ST-T changes on ECG.
Default: |
label_have_st_t_changes_unknown |
Label(s) for patient with unknown ST-T changes on ECG.
Default: |
The predictive model is based on patients referred for cardiac catheterisation between 1969 and 1983.
A numeric value representing the patient's PTP for severe (>75% luminal diameter narrowing of all three major coronary arteries or of the left main coronary artery) CAD based on the 1993 Duke Clinical Score.
# 40 year old female with typical chest pain for one year, # She has progressive angina but no nocturnal angina. # Angina pain lasted at most five times a week. # She has peripheral vascular and cerebrovascular disease. # She has hypertension but has no dyslipidemia and not diabetic. # She has Q waves and ST-T changes on ECG. calculate_dcs_1993_severe_cad_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_progressive_angina = "yes", have_nocturnal_angina = "no", have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", have_hypertension = "yes", have_dyslipidemia = "no", have_diabetes = "no", have_q_waves = "yes", have_st_t_changes = "yes", frequency_of_angina_pain_per_week = 5, duration_of_cad_symptoms_year = 1, )# 40 year old female with typical chest pain for one year, # She has progressive angina but no nocturnal angina. # Angina pain lasted at most five times a week. # She has peripheral vascular and cerebrovascular disease. # She has hypertension but has no dyslipidemia and not diabetic. # She has Q waves and ST-T changes on ECG. calculate_dcs_1993_severe_cad_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_progressive_angina = "yes", have_nocturnal_angina = "no", have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", have_hypertension = "yes", have_dyslipidemia = "no", have_diabetes = "no", have_q_waves = "yes", have_st_t_changes = "yes", frequency_of_angina_pain_per_week = 5, duration_of_cad_symptoms_year = 1, )
This function returns a patient's pre-test probability (PTP) of significant (>75% luminal diameter narrowing of at least one major coronary artery) coronary artery disease based on the 1993 Duke Clinical Score.
calculate_dcs_1993_sig_cad_ptp( age, sex, chest_pain_type, have_mi, have_smoking_history, have_dyslipidemia, have_diabetes, have_q_waves, have_st_t_changes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_mi_no = c("no"), label_have_mi_yes = c("yes"), label_have_mi_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )calculate_dcs_1993_sig_cad_ptp( age, sex, chest_pain_type, have_mi, have_smoking_history, have_dyslipidemia, have_diabetes, have_q_waves, have_st_t_changes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_mi_no = c("no"), label_have_mi_yes = c("yes"), label_have_mi_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_mi |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_q_waves |
The value of variable in the parameters
|
have_st_t_changes |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_mi_no |
Label(s) for patient not having a previous history of MI.
Default: |
label_have_mi_yes |
Label(s) for patient having a previous history of MI.
Default: |
label_have_mi_unknown |
Label(s) for patient with unknown previous history of MI.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_q_waves_no |
Label(s) for patient not having Q waves on ECG.
Default: |
label_have_q_waves_yes |
Label(s) for patient having Q waves on ECG.
Default: |
label_have_q_waves_unknown |
Label(s) for patient with unknown Q waves on ECG.
Default: |
label_have_st_t_changes_no |
Label(s) for patient not having ST-T changes on ECG.
Default: |
label_have_st_t_changes_yes |
Label(s) for patient having ST-T changes on ECG.
Default: |
label_have_st_t_changes_unknown |
Label(s) for patient with unknown ST-T changes on ECG.
Default: |
The predictive model is based on patients referred for cardiac catheterisation between 1969 and 1983.
A numeric value representing the patient's PTP for significant (>75% luminal diameter narrowing of at least one major coronary artery) CAD based on the 1993 Duke Clinical Score.
# 40 year old female with typical chest pain, # previous history of MI, # has diabetes but no dyslipidemia and a non-smoker. # She has Q waves but no ST-T changes on ECG. calculate_dcs_1993_sig_cad_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_mi = "yes", have_smoking_history = "no", have_dyslipidemia = "no", have_diabetes = "yes", have_q_waves = "yes", have_st_t_changes = "no" )# 40 year old female with typical chest pain, # previous history of MI, # has diabetes but no dyslipidemia and a non-smoker. # She has Q waves but no ST-T changes on ECG. calculate_dcs_1993_sig_cad_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_mi = "yes", have_smoking_history = "no", have_dyslipidemia = "no", have_diabetes = "yes", have_q_waves = "yes", have_st_t_changes = "no" )
A function used to calculate the patient's vascular disease index. This is used to calculate the likelihood of severe coronary artery disease in the Duke Clinical Score 1993 paper.
calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, max_na = 0, label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease, have_cerebrovascular_disease, have_carotid_bruits, max_na = 0, label_have_pvd_no = c("no"), label_have_pvd_yes = c("yes"), label_have_pvd_unknown = c(NA, NaN), label_have_cvd_no = c("no"), label_have_cvd_yes = c("yes"), label_have_cvd_unknown = c(NA, NaN), label_have_carotid_bruits_no = c("no"), label_have_carotid_bruits_yes = c("yes"), label_have_carotid_bruits_unknown = c(NA, NaN), error_call = rlang::caller_env() )
have_peripheral_vascular_disease |
The value of variable in the parameters
|
have_cerebrovascular_disease |
The value of variable in the parameters
|
have_carotid_bruits |
The value of variable in the parameters
|
max_na |
Input integer 0 to 3 to indicate the maximum number of
missing disease history to tolerate before outputting an |
label_have_pvd_no |
Label(s) for patient not having peripheral vascular disease.
Default: |
label_have_pvd_yes |
Label(s) for patient having peripheral vascular disease.
Default: |
label_have_pvd_unknown |
Label(s) for patient
having unknown peripheral vascular disease.
Default: |
label_have_cvd_no |
Label(s) for patient not having cerebrovascular disease.
Default: |
label_have_cvd_yes |
Label(s) for patient having cerebrovascular disease.
Default: |
label_have_cvd_unknown |
Label(s) for patient
having unknown cerebrovascular disease.
Default: |
label_have_carotid_bruits_no |
Label(s) for patient not having carotid bruits.
Default: |
label_have_carotid_bruits_yes |
Label(s) for patient having carotid bruits.
Default: |
label_have_carotid_bruits_unknown |
Label(s) for patient
having unknown carotid bruits.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the patient's vascular disease index.
It can also be NA if the number of missing disease history exceeds the max_na
input value.
calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no" ) calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = NA, have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", max_na = 0 ) calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = NA, have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", max_na = 1 )calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = "yes", have_cerebrovascular_disease = "yes", have_carotid_bruits = "no" ) calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = NA, have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", max_na = 0 ) calculate_dcs_1993_vascular_disease_index( have_peripheral_vascular_disease = NA, have_cerebrovascular_disease = "yes", have_carotid_bruits = "no", max_na = 1 )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on Diamond-Forrester 1979 model.
calculate_diamond_forrester_1979_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_diamond_forrester_1979_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
output |
Input text to indicate the how pre-test probability results be expressed Default: c("numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
A numeric or percentage representing the patient's PTP for obstructive CAD based on Diamond-Forrester 1979 model.
# 35 year old female with typical chest pain calculate_diamond_forrester_1979_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_diamond_forrester_1979_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )# 35 year old female with typical chest pain calculate_diamond_forrester_1979_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_diamond_forrester_1979_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the European Society of Cardiology (ESC) 2013 guidelines.
calculate_esc_2013_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage") )calculate_esc_2013_ptp( age, sex, chest_pain_type, output = c("numeric", "percentage") )
age |
Input integer value to indicate the age of the patient. |
sex |
Input characters (female, male) to indicate the sex of the patient.
|
chest_pain_type |
Input characters (typical, atypical, nonanginal) to indicate the chest pain characteristics of the patient.
|
output |
Input text to indicate the how pre-test probability results be expressed Default: c("numeric", "percentage")
|
The predictive model used to create the guidelines are based on the journal A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension by 2011 Genders et. al.
An integer or percentage representing the patient's PTP for obstructive CAD based on the ESC 2013 guidelines.
# 35 year old female with typical chest pain calculate_esc_2013_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_esc_2013_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )# 35 year old female with typical chest pain calculate_esc_2013_ptp( age = 35, sex = "female", chest_pain_type = "typical", output = "percentage" ) # 65 year old male with nonanginal chest pain calculate_esc_2013_ptp( age = 65, sex = "male", chest_pain_type = "nonanginal", output = "percentage" )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the European Society of Cardiology (ESC) 2019 guidelines.
calculate_esc_2019_ptp( age, sex, have_dyspnoea, chest_pain_type, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_esc_2019_ptp( age, sex, have_dyspnoea, chest_pain_type, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters |
output |
Input text to indicate the how pre-test probability results be expressed Default: c("grouping", "numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
The predictive model used to create the guidelines are based on patients from European countries with low cardiovascular disease (CVD) risk.
If the patient has both dyspnoea and a particular chest pain type (typical, atypical, nonanginal), The chest pain type will take precedence over dyspnoea.
An integer, percentage or category representing the patient's PTP for obstructive CAD
based on the ESC 2019 guidelines.
See parameter option output for more information.
# 35 year old female with typical chest pain calculate_esc_2019_ptp( age = 35, sex = "female", have_dyspnoea = "no", chest_pain_type = "typical", output = "percentage" ) # 75 year old male with only dyspnoea calculate_esc_2019_ptp( age = 75, sex = "male", have_dyspnoea = "yes", chest_pain_type = "no chest pain", output = "percentage" )# 35 year old female with typical chest pain calculate_esc_2019_ptp( age = 35, sex = "female", have_dyspnoea = "no", chest_pain_type = "typical", output = "percentage" ) # 75 year old male with only dyspnoea calculate_esc_2019_ptp( age = 75, sex = "male", have_dyspnoea = "yes", chest_pain_type = "no chest pain", output = "percentage" )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the European Society of Cardiology (ESC) 2024 guidelines.
calculate_esc_2024_fig_4_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )calculate_esc_2024_fig_4_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
have_family_history |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
allow_na_symptom_score |
A logical evaluating to |
max_na_num_of_rf |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
output |
Input text to indicate the how pre-test probability results be expressed Default: c("grouping", "numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
An integer, percentage or category representing the patient's PTP for obstructive CAD
based on the ESC 2024 guidelines.
See parameter option output for more information.
# 30 female with symptom score of 0 and 0 risk factors calculate_esc_2024_fig_4_ptp( age = 30, sex = "female", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, output = "percentage" )# 30 female with symptom score of 0 and 0 risk factors calculate_esc_2024_fig_4_ptp( age = 30, sex = "female", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, output = "percentage" )
This function returns a patient's pre-test Probability (PTP) of obstructive coronary artery disease (CAD) based on the European Society of Cardiology (ESC) 2024 guidelines.
calculate_esc_2024_fig_4_ptp_simplfied( age, sex, symptom_score, num_of_rf, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_esc_2024_fig_4_ptp_simplfied( age, sex, symptom_score, num_of_rf, output = c("grouping", "numeric", "percentage"), label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), error_call = rlang::caller_env() )
age |
Input integer value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
symptom_score |
An integer indicating the symptom score of the patient.
This value can be calculated via the |
num_of_rf |
An integer indicating the number of risk factors the patient has.
This value can be calculated via the
|
output |
Input text to indicate the how pre-test probability results be expressed Default: c("grouping", "numeric", "percentage")
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer, percentage or category representing the patient's PTP for obstructive CAD
based on the ESC 2024 guidelines.
See parameter option output for more information.
# 30 female with symptom score of 0 and 0 risk factors calculate_esc_2024_fig_4_ptp_simplfied( age = 30, sex = "female", symptom_score = 0, num_of_rf = 0, output = "percentage" )# 30 female with symptom score of 0 and 0 risk factors calculate_esc_2024_fig_4_ptp_simplfied( age = 30, sex = "female", symptom_score = 0, num_of_rf = 0, output = "percentage" )
A function used to calculate the number of risk factors the patient has. This is used to calculate the pretest probability of coronary artery disease (CAD) based on the ESC 2024 guidelines.
calculate_esc_2024_num_of_rf( have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, max_na = 0, label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_esc_2024_num_of_rf( have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, max_na = 0, label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), error_call = rlang::caller_env() )
have_family_history |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
max_na |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the number of risk factors the patient has.
It can also be NA if the number of missing risk factors exceeds the max_na
input value.
calculate_esc_2024_num_of_rf( have_family_history = "yes", have_smoking_history = "yes", have_dyslipidemia = "yes", have_hypertension = "yes", have_diabetes = "no" ) calculate_esc_2024_num_of_rf( have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = NA, have_diabetes = "no", max_na = 0 ) calculate_esc_2024_num_of_rf( have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = NA, have_diabetes = "no", max_na = 1 )calculate_esc_2024_num_of_rf( have_family_history = "yes", have_smoking_history = "yes", have_dyslipidemia = "yes", have_hypertension = "yes", have_diabetes = "no" ) calculate_esc_2024_num_of_rf( have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = NA, have_diabetes = "no", max_na = 0 ) calculate_esc_2024_num_of_rf( have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = NA, have_diabetes = "no", max_na = 1 )
A function used to calculate the symptom score of the patient. This is used to calculate the pretest probability of coronary artery disease (CAD) based on the ESC 2024 guidelines.
calculate_esc_2024_symptom_score( chest_pain_type, have_dyspnoea, allow_na = TRUE, label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), error_call = rlang::caller_env() )calculate_esc_2024_symptom_score( chest_pain_type, have_dyspnoea, allow_na = TRUE, label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), error_call = rlang::caller_env() )
chest_pain_type |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
allow_na |
A logical evaluating to |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
error_call |
The execution environment of a currently
running function, e.g. |
An integer indicating the symptom score of the patient.
It can also be NA if both chest_pain_type and have_dyspnoea are NA.
Patients with both nonanginal chest pain and dyspnoea will be given a score of 2
calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = "yes", allow_na = TRUE ) calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = NA, allow_na = FALSE ) calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = NA, allow_na = TRUE )calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = "yes", allow_na = TRUE ) calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = NA, allow_na = FALSE ) calculate_esc_2024_symptom_score( chest_pain_type = "nonanginal", have_dyspnoea = NA, allow_na = TRUE )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2022 Local Assessment of the Heart (LAH) clinical model.
calculate_lah_2022_clinical_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )calculate_lah_2022_clinical_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
The predictive model is based on patients a mixed Asian cohort within Singapore with stable chest pain.
A numeric value representing the patient's PTP for obstructive CAD based on the 2022 Local Assessment of the Heart (LAH) clinical model.
# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia # and a non-smoker calculate_lah_2022_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no" )# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia # and a non-smoker calculate_lah_2022_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2022 Local Assessment of the Heart (LAH) extended model.
calculate_lah_2022_extended_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, coronary_calcium_score, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )calculate_lah_2022_extended_ptp( age, sex, chest_pain_type, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, coronary_calcium_score, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
coronary_calcium_score |
Input non-negative numeric to indicate the total coronary calcium score of the patient. |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
The predictive model is based on patients a mixed Asian cohort within Singapore with stable chest pain.
A numeric value representing the patient's PTP for obstructive CAD based on the 2022 Local Assessment of the Heart (LAH) extended model.
# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia, # a non-smoker and a coronary calcium score of 0 calculate_lah_2022_extended_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", coronary_calcium_score = 0 )# 40 year old female with typical chest pain, # diabetes but no hypertension, dyslipidemia, # a non-smoker and a coronary calcium score of 0 calculate_lah_2022_extended_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", coronary_calcium_score = 0 )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2021 Predictive Risk scorE for CAD In Southeast Asians with chEst pain (PRECISE) clinical model.
calculate_precise_2021_clinical_ptp( age, sex, chest_pain_type, have_neck_radiation, have_diabetes, have_hypertension, smoking_history_type, have_q_waves, have_st_t_changes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_neck_radiation_no = c("no"), label_have_neck_radiation_yes = c("yes"), label_have_neck_radiation_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_smoking_history_type_current = c("current"), label_smoking_history_type_past = c("past"), label_smoking_history_type_none = c("none"), label_smoking_history_type_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )calculate_precise_2021_clinical_ptp( age, sex, chest_pain_type, have_neck_radiation, have_diabetes, have_hypertension, smoking_history_type, have_q_waves, have_st_t_changes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_neck_radiation_no = c("no"), label_have_neck_radiation_yes = c("yes"), label_have_neck_radiation_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_smoking_history_type_current = c("current"), label_smoking_history_type_past = c("past"), label_smoking_history_type_none = c("none"), label_smoking_history_type_unknown = c(NA, NaN), label_have_q_waves_no = c("no"), label_have_q_waves_yes = c("yes"), label_have_q_waves_unknown = c(NA, NaN), label_have_st_t_changes_no = c("no"), label_have_st_t_changes_yes = c("yes"), label_have_st_t_changes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_neck_radiation |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
smoking_history_type |
The value of variable in the parameters
|
have_q_waves |
The value of variable in the parameters
|
have_st_t_changes |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_neck_radiation_no |
Label(s) for patient without
chest pain radiating to the neck.
Default: |
label_have_neck_radiation_yes |
Label(s) for patient with
chest pain radiating to the neck.
Default: |
label_have_neck_radiation_unknown |
Label(s) for patient with unknown
chest pain radiating to the neck
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_smoking_history_type_current |
Label(s) for patient who is a current smoker.
Default: |
label_smoking_history_type_past |
Label(s) for patient who is a past smoker.
Default: |
label_smoking_history_type_none |
Label(s) for patient who is a non-smoker.
Default: |
label_smoking_history_type_unknown |
Label(s) for patient with unknown smoking history.
Default: |
label_have_q_waves_no |
Label(s) for patient not having Q waves on ECG.
Default: |
label_have_q_waves_yes |
Label(s) for patient having Q waves on ECG.
Default: |
label_have_q_waves_unknown |
Label(s) for patient with unknown Q waves on ECG.
Default: |
label_have_st_t_changes_no |
Label(s) for patient not having ST-T changes on ECG.
Default: |
label_have_st_t_changes_yes |
Label(s) for patient having ST-T changes on ECG.
Default: |
label_have_st_t_changes_unknown |
Label(s) for patient with unknown ST-T changes on ECG.
Default: |
The predictive model is based on patients a mixed Asian cohort within Singapore with stable chest pain.
A numeric value representing the patient's PTP for obstructive CAD based on the 2021 Predictive Risk scorE for CAD In Southeast Asians with chEst pain (PRECISE) clinical model.
# 40 year old female with typical chest pain # radiating to the neck, has diabetes # but no hypertension and a non-smoker. # She has Q waves but no ST-T changes on ECG. calculate_precise_2021_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_neck_radiation = "yes", have_diabetes = "yes", have_hypertension = "no", smoking_history_type = "none", have_q_waves = "yes", have_st_t_changes = "no" )# 40 year old female with typical chest pain # radiating to the neck, has diabetes # but no hypertension and a non-smoker. # She has Q waves but no ST-T changes on ECG. calculate_precise_2021_clinical_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_neck_radiation = "yes", have_diabetes = "yes", have_hypertension = "no", smoking_history_type = "none", have_q_waves = "yes", have_st_t_changes = "no" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2021 Predictive Risk scorE for CAD In Southeast Asians with chEst pain (PRECISE) simple model.
calculate_precise_2021_simple_ptp( age, sex, chest_pain_type, have_neck_radiation, have_diabetes, have_hypertension, smoking_history_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_neck_radiation_no = c("no"), label_have_neck_radiation_yes = c("yes"), label_have_neck_radiation_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_smoking_history_type_current = c("current"), label_smoking_history_type_past = c("past"), label_smoking_history_type_none = c("none"), label_smoking_history_type_unknown = c(NA, NaN) )calculate_precise_2021_simple_ptp( age, sex, chest_pain_type, have_neck_radiation, have_diabetes, have_hypertension, smoking_history_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_neck_radiation_no = c("no"), label_have_neck_radiation_yes = c("yes"), label_have_neck_radiation_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_smoking_history_type_current = c("current"), label_smoking_history_type_past = c("past"), label_smoking_history_type_none = c("none"), label_smoking_history_type_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters,
|
have_neck_radiation |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
smoking_history_type |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_neck_radiation_no |
Label(s) for patient without
chest pain radiating to the neck.
Default: |
label_have_neck_radiation_yes |
Label(s) for patient with
chest pain radiating to the neck.
Default: |
label_have_neck_radiation_unknown |
Label(s) for patient with unknown
chest pain radiating to the neck
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_smoking_history_type_current |
Label(s) for patient who is a current smoker.
Default: |
label_smoking_history_type_past |
Label(s) for patient who is a past smoker.
Default: |
label_smoking_history_type_none |
Label(s) for patient who is a non-smoker.
Default: |
label_smoking_history_type_unknown |
Label(s) for patient with unknown smoking history.
Default: |
The predictive model is based on patients a mixed Asian cohort within Singapore with stable chest pain.
A numeric value representing the patient's PTP for obstructive CAD based on the 2021 Predictive Risk scorE for CAD In Southeast Asians with chEst pain (PRECISE) simple model.
# 40 year old female with typical chest pain # radiating to the neck, has diabetes # but no hypertension and a non-smoker calculate_precise_2021_simple_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_neck_radiation = "yes", have_diabetes = "yes", have_hypertension = "no", smoking_history_type = "none" )# 40 year old female with typical chest pain # radiating to the neck, has diabetes # but no hypertension and a non-smoker calculate_precise_2021_simple_ptp( age = 40, sex = "female", chest_pain_type = "typical", have_neck_radiation = "yes", have_diabetes = "yes", have_hypertension = "no", smoking_history_type = "none" )
This function returns a symptomatic (have chest pain or dyspnoea) patient's minimal risk score for obstructive coronary artery disease based on the 2017 PROMISE Minimal-Risk Score.
calculate_prms_2017_ptp( age, sex, hdl_mg_dl, is_minority_ethnicity, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, have_family_history, have_stress_symptoms = NA, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_is_minority_ethnicity_no = c("no"), label_is_minority_ethnicity_yes = c("yes"), label_is_minority_ethnicity_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_stress_symptoms_no = c("no"), label_have_stress_symptoms_yes = c("yes"), label_have_stress_symptoms_unknown = c(NA, NaN) )calculate_prms_2017_ptp( age, sex, hdl_mg_dl, is_minority_ethnicity, have_diabetes, have_hypertension, have_dyslipidemia, have_smoking_history, have_family_history, have_stress_symptoms = NA, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_is_minority_ethnicity_no = c("no"), label_is_minority_ethnicity_yes = c("yes"), label_is_minority_ethnicity_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_stress_symptoms_no = c("no"), label_have_stress_symptoms_yes = c("yes"), label_have_stress_symptoms_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
hdl_mg_dl |
Input positive numeric value to indicate the
patient's high-density lipoprotein (HDL) in |
is_minority_ethnicity |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_family_history |
The value of variable in the parameters
|
have_stress_symptoms |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_is_minority_ethnicity_no |
Label(s) for patient not from a
racial or minority ethnicity (or patient is a non-Hispanic/Latino White).
Default: |
label_is_minority_ethnicity_yes |
Label(s) for patient from a
racial or minority ethnicity (or patient is not a non-Hispanic/Latino White).
E.g. Blacks, Asians, etc.
Default: |
label_is_minority_ethnicity_unknown |
Label(s) for patient from an unknown ethnicity
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_stress_symptoms_no |
Label(s) for patient with
no symptoms (negative results) related to physical or mental stress.
Default: |
label_have_stress_symptoms_yes |
Label(s) for patient with
symptoms (positive results) related to physical or mental stress.
Default: |
label_have_stress_symptoms_unknown |
Label(s) for patient with
inconclusive results or patient has not taken any stress test
Default: |
The predictive model is based on CCTA images from 4632 patients in the Prospective Multicenter imaging Study for Evaluation of Chest Pain (PROMISE) trial.
A numeric value representing the patient's minimal risk score for obstructive CAD based on the 2017 PROMISE Minimal-Risk Score.
# 50 year old white female with chest pain # a medical history of hypertension, and a # high-density lipoprotein cholesterol level of 70 mg/dL calculate_prms_2017_ptp( age = 50, sex = "female", hdl_mg_dl = 70, is_minority_ethnicity = "no", have_diabetes = "no", have_hypertension = "yes", have_dyslipidemia = "no", have_smoking_history = "no", have_family_history = "no", have_stress_symptoms = "no" ) # 40 year old non-white male with chest pain # a medical history of diabetes, unknown stress symptoms and a # high-density lipoprotein cholesterol level of 70 mg/dL calculate_prms_2017_ptp( age = 40, sex = "male", hdl_mg_dl = 70, is_minority_ethnicity = "yes", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", have_family_history = "no", have_stress_symptoms = NA )# 50 year old white female with chest pain # a medical history of hypertension, and a # high-density lipoprotein cholesterol level of 70 mg/dL calculate_prms_2017_ptp( age = 50, sex = "female", hdl_mg_dl = 70, is_minority_ethnicity = "no", have_diabetes = "no", have_hypertension = "yes", have_dyslipidemia = "no", have_smoking_history = "no", have_family_history = "no", have_stress_symptoms = "no" ) # 40 year old non-white male with chest pain # a medical history of diabetes, unknown stress symptoms and a # high-density lipoprotein cholesterol level of 70 mg/dL calculate_prms_2017_ptp( age = 40, sex = "male", hdl_mg_dl = 70, is_minority_ethnicity = "yes", have_diabetes = "yes", have_hypertension = "no", have_dyslipidemia = "no", have_smoking_history = "no", have_family_history = "no", have_stress_symptoms = NA )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2019 Reeh et. al. basic model.
calculate_reeh_2019_basic_ptp( age, sex, symptom_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_symptom_type_typical = c("typical"), label_symptom_type_atypical = c("atypical"), label_symptom_type_nonanginal = c("nonanginal"), label_symptom_type_dyspnoea = c("dyspnoea"), label_symptom_type_unknown = c(NA, NaN) )calculate_reeh_2019_basic_ptp( age, sex, symptom_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_symptom_type_typical = c("typical"), label_symptom_type_atypical = c("atypical"), label_symptom_type_nonanginal = c("nonanginal"), label_symptom_type_dyspnoea = c("dyspnoea"), label_symptom_type_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
symptom_type |
Input characters (typical, atypical, nonanginal, dyspnoea) to indicate the symptom characteristics of the patient.
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_symptom_type_typical |
Label(s) for patient having typical chest pain.
Default: |
label_symptom_type_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_symptom_type_nonanginal |
Label(s) for patient having nonanginal
or non-specific chest pain.
Default: |
label_symptom_type_dyspnoea |
Label(s) for patient having dyspnoea.
Default: |
label_symptom_type_unknown |
Label(s) for patient having unknown symptoms.
Default: |
The predictive model is based on 3903 patients free of CAD and heart failure and suspected of angina, who were referred to a single, large, urban university hospital for assessment in 2012–15.
A numeric value representing the patient's PTP for obstructive CAD based on the 2019 Reeh et. al. basic model.
# 40 year old female with typical chest pain calculate_reeh_2019_basic_ptp( age = 40, sex = "female", symptom_type = "typical" )# 40 year old female with typical chest pain calculate_reeh_2019_basic_ptp( age = 40, sex = "female", symptom_type = "typical" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2019 Reeh et. al. clinical model.
calculate_reeh_2019_clinical_ptp( age, sex, symptom_type, have_dyslipidemia, have_family_history, have_diabetes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_symptom_type_typical = c("typical"), label_symptom_type_atypical = c("atypical"), label_symptom_type_nonanginal = c("nonanginal"), label_symptom_type_dyspnoea = c("dyspnoea"), label_symptom_type_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )calculate_reeh_2019_clinical_ptp( age, sex, symptom_type, have_dyslipidemia, have_family_history, have_diabetes, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_symptom_type_typical = c("typical"), label_symptom_type_atypical = c("atypical"), label_symptom_type_nonanginal = c("nonanginal"), label_symptom_type_dyspnoea = c("dyspnoea"), label_symptom_type_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
symptom_type |
Input characters (typical, atypical, nonanginal, dyspnoea) to indicate the symptom characteristics of the patient.
|
have_dyslipidemia |
The value of variable in the parameters
|
have_family_history |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_symptom_type_typical |
Label(s) for patient having typical chest pain.
Default: |
label_symptom_type_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_symptom_type_nonanginal |
Label(s) for patient having nonanginal
or non-specific chest pain.
Default: |
label_symptom_type_dyspnoea |
Label(s) for patient having dyspnoea.
Default: |
label_symptom_type_unknown |
Label(s) for patient having unknown symptoms.
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
The predictive model is based on 3903 patients free of CAD and heart failure and suspected of angina, who were referred to a single, large, urban university hospital for assessment in 2012–15.
A numeric value representing the patient's PTP for obstructive CAD based on the 2019 Reeh et. al. clinical model.
# 40 year old female with typical chest pain calculate_reeh_2019_clinical_ptp( age = 40, sex = "female", symptom_type = "typical", have_dyslipidemia = "no", have_family_history = "no", have_diabetes = "no" )# 40 year old female with typical chest pain calculate_reeh_2019_clinical_ptp( age = 40, sex = "female", symptom_type = "typical", have_dyslipidemia = "no", have_family_history = "no", have_diabetes = "no" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2020 Winther et. al. basic model (Basic_PTP).
calculate_winther_2020_basic_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )calculate_winther_2020_basic_ptp( age, sex, chest_pain_type, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
The predictive model is based on > 40000 symptomatic patients from 2008 to 2017 from 13 hospitals in Western Denmark. These patients are registered under the Western Denmark Heart Registry.
A numeric value representing the patient's PTP for obstructive CAD based on the 2020 Winther et. al. basic model (Basic_PTP).
# 40 year old Male with typical chest pain calculate_winther_2020_basic_ptp( age = 40, sex = "male", chest_pain_type = "typical" ) # 40 year old Male with nonanginal chest pain calculate_winther_2020_basic_ptp( age = 40, sex = "male", chest_pain_type = "nonanginal" )# 40 year old Male with typical chest pain calculate_winther_2020_basic_ptp( age = 40, sex = "male", chest_pain_type = "typical" ) # 40 year old Male with nonanginal chest pain calculate_winther_2020_basic_ptp( age = 40, sex = "male", chest_pain_type = "nonanginal" )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on 2020 Winther et. al. Coronary Artery Calcium Score-Weighted Clinical Likelihood (CACS-CL) model.
calculate_winther_2020_cacs_cl_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, coronary_calcium_score, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )calculate_winther_2020_cacs_cl_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, coronary_calcium_score, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
have_family_history |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
coronary_calcium_score |
Input non-negative numeric to indicate the total coronary calcium score of the patient. |
allow_na_symptom_score |
A logical evaluating to |
max_na_num_of_rf |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
The predictive model is based on > 40000 symptomatic patients from 2008 to 2017 from 13 hospitals in Western Denmark. These patients are registered under the Western Denmark Heart Registry.
A numeric value representing the patient's PTP for obstructive CAD based on the 2020 Winther et. al. Coronary Artery Calcium Score-Weighted Clinical Likelihood (CACS-CL) model.
# 40 year old Male with nonanginal chest pain and coronary calcium score of 0 calculate_winther_2020_cacs_cl_ptp( age = 40, sex = "male", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", coronary_calcium_score = 0, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0 )# 40 year old Male with nonanginal chest pain and coronary calcium score of 0 calculate_winther_2020_cacs_cl_ptp( age = 40, sex = "male", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", coronary_calcium_score = 0, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0 )
This function returns a patient's pre-test probability (PTP) of obstructive coronary artery disease based on the 2020 Winther et. al. Risk Factor-Weighted Clinical Likelihood (RF-CL) model.
calculate_winther_2020_rf_cl_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )calculate_winther_2020_rf_cl_ptp( age, sex, chest_pain_type, have_dyspnoea, have_family_history, have_smoking_history, have_dyslipidemia, have_hypertension, have_diabetes, allow_na_symptom_score = TRUE, max_na_num_of_rf = 0, label_sex_male = c("male"), label_sex_female = c("female"), label_sex_unknown = c(NA, NaN), label_have_dyspnoea_no = c("no"), label_have_dyspnoea_yes = c("yes"), label_have_dyspnoea_unknown = c(NA, NaN), label_cpt_no_chest_pain = c("no chest pain"), label_cpt_nonanginal = c("nonanginal"), label_cpt_atypical = c("atypical"), label_cpt_typical = c("typical"), label_cpt_unknown = c(NA, NaN), label_have_family_history_no = c("no"), label_have_family_history_yes = c("yes"), label_have_family_history_unknown = c(NA, NaN), label_have_smoking_history_no = c("no"), label_have_smoking_history_yes = c("yes"), label_have_smoking_history_unknown = c(NA, NaN), label_have_dyslipidemia_no = c("no"), label_have_dyslipidemia_yes = c("yes"), label_have_dyslipidemia_unknown = c(NA, NaN), label_have_hypertension_no = c("no"), label_have_hypertension_yes = c("yes"), label_have_hypertension_unknown = c(NA, NaN), label_have_diabetes_no = c("no"), label_have_diabetes_yes = c("yes"), label_have_diabetes_unknown = c(NA, NaN) )
age |
Input numeric value to indicate the age of the patient in years. |
sex |
The value of variable in the parameters |
chest_pain_type |
The value of variable in the parameters |
have_dyspnoea |
The value of variable in the parameters |
have_family_history |
The value of variable in the parameters
|
have_smoking_history |
The value of variable in the parameters
|
have_dyslipidemia |
The value of variable in the parameters
|
have_hypertension |
The value of variable in the parameters
|
have_diabetes |
The value of variable in the parameters
|
allow_na_symptom_score |
A logical evaluating to |
max_na_num_of_rf |
Input integer 0 to 5 to indicate the maximum number of
missing risk factors to tolerate before outputting an |
label_sex_male |
Label(s) for definition(s) of male sex.
Default: |
label_sex_female |
Label(s) for definition(s) of female sex.
Default: |
label_sex_unknown |
Label(s) for definition(s) of missing sex.
Default: |
label_have_dyspnoea_no |
Label(s) for patient having no dyspnoea symptoms.
Default: |
label_have_dyspnoea_yes |
Label(s) for patient having dyspnoea symptoms.
Default: |
label_have_dyspnoea_unknown |
Label(s) for patient having unknown dyspnoea symptoms.
Default: |
label_cpt_no_chest_pain |
Label(s) for patient having no chest pain.
Default: |
label_cpt_nonanginal |
Label(s) for patient having nonanginal or non-specific chest pain.
Default: |
label_cpt_atypical |
Label(s) for patient having atypical chest pain.
Default: |
label_cpt_typical |
Label(s) for patient having typical chest pain.
Default: |
label_cpt_unknown |
Label(s) for patient having unknown chest pain type symptoms.
Default: |
label_have_family_history_no |
Label(s) for patient with no family history of CAD.
Default: |
label_have_family_history_yes |
Label(s) for patient having family history of CAD.
Default: |
label_have_family_history_unknown |
Label(s) for patient
having unknown family history of CAD.
Default: |
label_have_smoking_history_no |
Label(s) for patient with
no smoking history (current or past).
Default: |
label_have_smoking_history_yes |
Label(s) for patient having
smoking history (current or past).
Default: |
label_have_smoking_history_unknown |
Label(s) for patient
having unknown smoking history (current or past).
Default: |
label_have_dyslipidemia_no |
Label(s) for patient with no dyslipidemia.
Default: |
label_have_dyslipidemia_yes |
Label(s) for patient having dyslipidemia.
Default: |
label_have_dyslipidemia_unknown |
Label(s) for patient
having unknown dyslipidemia.
Default: |
label_have_hypertension_no |
Label(s) for patient with no hypertension.
Default: |
label_have_hypertension_yes |
Label(s) for patient having hypertension.
Default: |
label_have_hypertension_unknown |
Label(s) for patient
having unknown hypertension.
Default: |
label_have_diabetes_no |
Label(s) for patient with no diabetes.
Default: |
label_have_diabetes_yes |
Label(s) for patient having diabetes.
Default: |
label_have_diabetes_unknown |
Label(s) for patient
having unknown diabetes.
Default: |
The predictive model is based on > 40000 symptomatic patients from 2008 to 2017 from 13 hospitals in Western Denmark. These patients are registered under the Western Denmark Heart Registry.
A numeric value representing the patient's PTP for obstructive CAD based on the 2020 Winther et. al. Risk Factor-Weighted Clinical Likelihood (RF-CL) model.
# 40 year old Male with nonanginal chest pain calculate_winther_2020_rf_cl_ptp( age = 40, sex = "male", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", allow_na_symptom_score = TRUE, max_na_num_of_rf = 0 )# 40 year old Male with nonanginal chest pain calculate_winther_2020_rf_cl_ptp( age = 40, sex = "male", chest_pain_type = "no chest pain", have_dyspnoea = "no", have_family_history = "no", have_smoking_history = "no", have_dyslipidemia = "no", have_hypertension = "no", have_diabetes = "no", allow_na_symptom_score = TRUE, max_na_num_of_rf = 0 )
Check if the four input categories are mutually exclusive from each other
check_if_four_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_3, label_cat_4, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_3 = rlang::caller_arg(label_cat_3), arg_cat_4 = rlang::caller_arg(label_cat_4), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )check_if_four_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_3, label_cat_4, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_3 = rlang::caller_arg(label_cat_3), arg_cat_4 = rlang::caller_arg(label_cat_4), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )
label_cat_1 |
First vector to check for mutually exclusiveness with another vector. |
label_cat_2 |
Second vector to check for mutually exclusiveness with another vector. |
label_cat_3 |
Third vector to check for mutually exclusiveness with another vector |
label_cat_4 |
Fourth vector to check for mutually exclusiveness with another vector |
label_cat_missing |
Missing values vector to check for mutually exclusiveness with another vector if needed. Default: NULL |
arg_cat_1 |
An argument name as a string for the first vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_2 |
An argument name as a string for the second vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_3 |
An argument name as a string for the third vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_4 |
An argument name as a string for the fourth vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_missing |
An argument name as a string for the missing values vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
An error message if the four input categories are not mutually exclusive
caller_arg, stack
cli_vec, cli_abort
# No error cat_1 <- c("no chest pain") cat_2 <- c("typical") cat_3 <- c("atypical") cat_4 <- c("nonanginal") cat_missing <- c("NA") check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4) check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4, cat_missing) # Common labels found cat_1 <- c("no chest pain","typical", "atypical", "nonanginal") cat_2 <- c("no chest pain","typical", "atypical", "nonanginal") cat_3 <- c("no chest pain","typical", "atypical", "nonanginal") cat_4 <- c("no chest pain","typical", "atypical", "nonanginal") cat_missing <- c("no chest pain","typical", "atypical", "nonanginal") try(check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4)) try(check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4, cat_missing))# No error cat_1 <- c("no chest pain") cat_2 <- c("typical") cat_3 <- c("atypical") cat_4 <- c("nonanginal") cat_missing <- c("NA") check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4) check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4, cat_missing) # Common labels found cat_1 <- c("no chest pain","typical", "atypical", "nonanginal") cat_2 <- c("no chest pain","typical", "atypical", "nonanginal") cat_3 <- c("no chest pain","typical", "atypical", "nonanginal") cat_4 <- c("no chest pain","typical", "atypical", "nonanginal") cat_missing <- c("no chest pain","typical", "atypical", "nonanginal") try(check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4)) try(check_if_four_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_4, cat_missing))
Check if the input variable is an integer
check_if_integer( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )check_if_integer( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )
x |
Input variable to check if it is an integer |
allow_na |
Input boolean to determine if |
arg |
An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
The variable itself or an error message if variable is not non-negative
# No error input = 0 try(check_if_integer(input)) # Error as 5.5 is not an integer input = 5.5 try(check_if_integer(input))# No error input = 0 try(check_if_integer(input)) # Error as 5.5 is not an integer input = 5.5 try(check_if_integer(input))
Check if the input variable is a non-negative number
check_if_non_negative( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )check_if_non_negative( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )
x |
Input variable to check if it is non-negative number |
allow_na |
Input boolean to determine if |
arg |
An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
The variable itself or an error message if variable is not non-negative
# No error input = 0 try(check_if_non_negative(input)) # Error as -5 is not a non-neagtive number input = -5 try(check_if_non_negative(input))# No error input = 0 try(check_if_non_negative(input)) # Error as -5 is not a non-neagtive number input = -5 try(check_if_non_negative(input))
Check if the input variable is numeric
check_if_numeric( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )check_if_numeric( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )
x |
Input variable to check if it is numeric |
allow_na |
Input boolean to determine if |
arg |
An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
The variable itself or an error message if variable is not numeric
# No error input = 1 try(check_if_numeric(input)) # Error as "5" is not numeric input = "5" try(check_if_numeric(input)) # Error as NULL is not numeric input = NULL try(check_if_numeric(input)) # Error as NA is not numeric and allow_na is FALSE input = NA try(check_if_numeric(input, allow_na = FALSE))# No error input = 1 try(check_if_numeric(input)) # Error as "5" is not numeric input = "5" try(check_if_numeric(input)) # Error as NULL is not numeric input = NULL try(check_if_numeric(input)) # Error as NA is not numeric and allow_na is FALSE input = NA try(check_if_numeric(input, allow_na = FALSE))
Check if the input variable is a positive number
check_if_positive( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )check_if_positive( x, allow_na = TRUE, arg = rlang::caller_arg(x), error_call = rlang::caller_env() )
x |
Input variable to check if it is positive number |
allow_na |
Input boolean to determine if |
arg |
An argument name as a string. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
The variable itself or an error message if variable is not positive
# No error input = 1 try(check_if_positive(input)) # Error as 0 is not a positive number input = 0 try(check_if_positive(input)) # Error as -5 is not a positive number input = -5 try(check_if_positive(input))# No error input = 1 try(check_if_positive(input)) # Error as 0 is not a positive number input = 0 try(check_if_positive(input)) # Error as -5 is not a positive number input = -5 try(check_if_positive(input))
Check if the three input categories are mutually exclusive from each other
check_if_three_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_3, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_3 = rlang::caller_arg(label_cat_3), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )check_if_three_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_3, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_3 = rlang::caller_arg(label_cat_3), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )
label_cat_1 |
First vector to check for mutually exclusiveness with another vector. |
label_cat_2 |
Second vector to check for mutually exclusiveness with another vector. |
label_cat_3 |
Third vector to check for mutually exclusiveness with another vector |
label_cat_missing |
Missing values vector to check for mutually exclusiveness with another vector if needed. Default: NULL |
arg_cat_1 |
An argument name as a string for the first vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_2 |
An argument name as a string for the second vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_3 |
An argument name as a string for the third vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_missing |
An argument name as a string for the missing values vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
An error message if the three input categories are not mutually exclusive
caller_arg, stack
cli_vec, cli_abort
# No error cat_1 <- c("typical") cat_2 <- c("atypical") cat_3 <- c("nonanginal") cat_missing <- c("NA") check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3) check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_missing) # Common labels found cat_1 <- c("typical", "atypical", "nonanginal", "NA") cat_2 <- c("typical", "atypical", "nonanginal", "NA") cat_3 <- c("typical", "atypical", "nonanginal", "NA") cat_missing <- c("typical", "atypical", "nonanginal", "NA") try(check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3)) try(check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_missing))# No error cat_1 <- c("typical") cat_2 <- c("atypical") cat_3 <- c("nonanginal") cat_missing <- c("NA") check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3) check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_missing) # Common labels found cat_1 <- c("typical", "atypical", "nonanginal", "NA") cat_2 <- c("typical", "atypical", "nonanginal", "NA") cat_3 <- c("typical", "atypical", "nonanginal", "NA") cat_missing <- c("typical", "atypical", "nonanginal", "NA") try(check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3)) try(check_if_three_categories_are_mutually_exclusive(cat_1, cat_2, cat_3, cat_missing))
Check if the two input categories are mutually exclusive from each other
check_if_two_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )check_if_two_categories_are_mutually_exclusive( label_cat_1, label_cat_2, label_cat_missing = NULL, arg_cat_1 = rlang::caller_arg(label_cat_1), arg_cat_2 = rlang::caller_arg(label_cat_2), arg_cat_missing = rlang::caller_arg(label_cat_missing), error_call = rlang::caller_env() )
label_cat_1 |
First vector to check for mutually exclusiveness with another vector. |
label_cat_2 |
Second vector to check for mutually exclusiveness with another vector. |
label_cat_missing |
Missing values vector to check for mutually exclusiveness with another vector if needed. Default: NULL |
arg_cat_1 |
An argument name as a string for the first vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_2 |
An argument name as a string for the second vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
arg_cat_missing |
An argument name as a string for the missing values vector. This argument will be mentioned in error messages as the input that is at the origin of a problem. |
error_call |
The execution environment of a currently
running function, e.g. |
An error message if the two input categories are not mutually exclusive
caller_arg, stack
cli_vec, cli_abort
# No error cat_1 <- c("male") cat_2 <- c("female") cat_missing <- c("not saying") check_if_two_categories_are_mutually_exclusive(cat_1, cat_2) check_if_two_categories_are_mutually_exclusive(cat_1, cat_2, cat_missing) # Common labels found cat_1 <- c("male", "female", "not saying") cat_2 <- c("male", "female", "not saying") cat_missing <- c("male", "female", "not saying") try(check_if_two_categories_are_mutually_exclusive(cat_1, cat_2)) try(check_if_two_categories_are_mutually_exclusive(cat_1, cat_2, cat_missing))# No error cat_1 <- c("male") cat_2 <- c("female") cat_missing <- c("not saying") check_if_two_categories_are_mutually_exclusive(cat_1, cat_2) check_if_two_categories_are_mutually_exclusive(cat_1, cat_2, cat_missing) # Common labels found cat_1 <- c("male", "female", "not saying") cat_2 <- c("male", "female", "not saying") cat_missing <- c("male", "female", "not saying") try(check_if_two_categories_are_mutually_exclusive(cat_1, cat_2)) try(check_if_two_categories_are_mutually_exclusive(cat_1, cat_2, cat_missing))
Add a quote around characters.
chr_quoted(chr, type = "`")chr_quoted(chr, type = "`")
chr |
A character vector to add quotes in each element. (e.g. c("a", "b", "c")). |
type |
Character to be used as a quote. Default: ''' |
DETAILS
A character vector with quotes added in each element. (e.g. c("'a'", "'b'", "'c'")).
chr_quoted(c("a","b", "c"))chr_quoted(c("a","b", "c"))
Function to map an input from four different list into one of the four standardise labels
harmonise_four_labels( arg, label_one, label_two, label_three, label_four, label_unknown, harmonise_label_one = "group_1", harmonise_label_two = "group_2", harmonise_label_three = "group_3", harmonise_label_four = "group_4", harmonise_label_unknown = NA, error_call = rlang::caller_env() )harmonise_four_labels( arg, label_one, label_two, label_three, label_four, label_unknown, harmonise_label_one = "group_1", harmonise_label_two = "group_2", harmonise_label_three = "group_3", harmonise_label_four = "group_4", harmonise_label_unknown = NA, error_call = rlang::caller_env() )
arg |
Input argument, in characters to be harmonised |
label_one |
Input character vector representing the ways to identify |
label_two |
Input character vector representing the ways to identify |
label_three |
Input character vector representing the ways to identify |
label_four |
Input character vector representing the ways to identify |
label_unknown |
Input character vector representing the ways to identify |
harmonise_label_one |
Input character representing the harmonised label for |
harmonise_label_two |
Input character representing the harmonised label for |
harmonise_label_three |
Input character representing the harmonised label for |
harmonise_label_four |
Input character representing the harmonised label for |
harmonise_label_unknown |
Input character representing the harmonised label for |
error_call |
The execution environment of a currently
running function, e.g. |
Character representing one of the four standardise labels.
label_cpt_no_chest_pain <- c("no chest pain", "normal") label_cpt_nonanginal <- c("nonanginal", "unspecified") label_cpt_atypical <- c("atypical", "Atypical") label_cpt_typical <- c("typical", "angina") label_cpt_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of chest_pain_type chest_pain_type <- "normal" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of chest_pain_type chest_pain_type <- "unspecified" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_three if there is valid input of chest_pain_type chest_pain_type <- "Atypical" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_four if there is valid input of chest_pain_type chest_pain_type <- "angina" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- NaN harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- "NIL" label_cpt_unknown <- c("NIL") harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives error of invalid typical chest pain input with partial match chest_pain_type <- "Typical" label_cpt_unknown <- c(NA, NaN) try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA )) # Gives error of invalid typical chest pain input without partial match chest_pain_type <- "Something" label_cpt_unknown <- c(NA, NaN) try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA )) # Gives error of invalid missing input of chest_pain_type chest_pain_type <- NA label_cpt_unknown <- c("NIL") try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ))label_cpt_no_chest_pain <- c("no chest pain", "normal") label_cpt_nonanginal <- c("nonanginal", "unspecified") label_cpt_atypical <- c("atypical", "Atypical") label_cpt_typical <- c("typical", "angina") label_cpt_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of chest_pain_type chest_pain_type <- "normal" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of chest_pain_type chest_pain_type <- "unspecified" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_three if there is valid input of chest_pain_type chest_pain_type <- "Atypical" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_four if there is valid input of chest_pain_type chest_pain_type <- "angina" harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- NaN harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- "NIL" label_cpt_unknown <- c("NIL") harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ) # Gives error of invalid typical chest pain input with partial match chest_pain_type <- "Typical" label_cpt_unknown <- c(NA, NaN) try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA )) # Gives error of invalid typical chest pain input without partial match chest_pain_type <- "Something" label_cpt_unknown <- c(NA, NaN) try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA )) # Gives error of invalid missing input of chest_pain_type chest_pain_type <- NA label_cpt_unknown <- c("NIL") try(harmonise_four_labels( arg = chest_pain_type, label_one = label_cpt_no_chest_pain, label_two = label_cpt_nonanginal, label_three = label_cpt_atypical, label_four = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "no chest pain", harmonise_label_two = "nonanginal", harmonise_label_three = "atypical", harmonise_label_four = "typical", harmonise_label_unknown = NA ))
Function to map an input from three different list into one of the three standardise labels
harmonise_three_labels( arg, label_one, label_two, label_three, label_unknown, harmonise_label_one = "group_1", harmonise_label_two = "group_2", harmonise_label_three = "group_3", harmonise_label_unknown = NA, error_call = rlang::caller_env() )harmonise_three_labels( arg, label_one, label_two, label_three, label_unknown, harmonise_label_one = "group_1", harmonise_label_two = "group_2", harmonise_label_three = "group_3", harmonise_label_unknown = NA, error_call = rlang::caller_env() )
arg |
Input argument, in characters to be harmonised |
label_one |
Input character vector representing the ways to identify |
label_two |
Input character vector representing the ways to identify |
label_three |
Input character vector representing the ways to identify |
label_unknown |
Input character vector representing the ways to identify |
harmonise_label_one |
Input character representing the harmonised label for |
harmonise_label_two |
Input character representing the harmonised label for |
harmonise_label_three |
Input character representing the harmonised label for |
harmonise_label_unknown |
Input character representing the harmonised label for |
error_call |
The execution environment of a currently
running function, e.g. |
Character representing one of the three standardise labels.
label_cpt_nonanginal <- c("nonanginal", "unspecified") label_cpt_atypical <- c("atypical", "Atypical") label_cpt_typical <- c("typical", "angina") label_cpt_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of chest_pain_type chest_pain_type <- "unspecified" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of chest_pain_type chest_pain_type <- "Atypical" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_three if there is valid input of chest_pain_type chest_pain_type <- "angina" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- NaN harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- "NIL" label_cpt_unknown <- c("NIL") harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives error of invalid typical chest pain input with partial match chest_pain_type <- "Typical" label_cpt_unknown <- c(NA, NaN) try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA )) # Gives error of invalid typical chest pain input without partial match chest_pain_type <- "Something" label_cpt_unknown <- c(NA, NaN) try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA )) # Gives error of invalid missing input of chest_pain_type chest_pain_type <- NA label_cpt_unknown <- c("NIL") try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ))label_cpt_nonanginal <- c("nonanginal", "unspecified") label_cpt_atypical <- c("atypical", "Atypical") label_cpt_typical <- c("typical", "angina") label_cpt_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of chest_pain_type chest_pain_type <- "unspecified" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of chest_pain_type chest_pain_type <- "Atypical" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_three if there is valid input of chest_pain_type chest_pain_type <- "angina" harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- NaN harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of chest_pain_type chest_pain_type <- "NIL" label_cpt_unknown <- c("NIL") harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ) # Gives error of invalid typical chest pain input with partial match chest_pain_type <- "Typical" label_cpt_unknown <- c(NA, NaN) try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA )) # Gives error of invalid typical chest pain input without partial match chest_pain_type <- "Something" label_cpt_unknown <- c(NA, NaN) try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA )) # Gives error of invalid missing input of chest_pain_type chest_pain_type <- NA label_cpt_unknown <- c("NIL") try(harmonise_three_labels( arg = chest_pain_type, label_one = label_cpt_nonanginal, label_two = label_cpt_atypical, label_three = label_cpt_typical, label_unknown = label_cpt_unknown, harmonise_label_one = "nonanginal", harmonise_label_two = "atypical", harmonise_label_three = "typical", harmonise_label_unknown = NA ))
Function to map an input from two different list into one of the two standardise labels
harmonise_two_labels( arg, label_one, label_two, label_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA, error_call = rlang::caller_env() )harmonise_two_labels( arg, label_one, label_two, label_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA, error_call = rlang::caller_env() )
arg |
Input argument, in characters to be harmonised |
label_one |
Input character vector representing the ways to identify |
label_two |
Input character vector representing the ways to identify |
label_unknown |
Input character vector representing the ways to identify |
harmonise_label_one |
Input character representing the harmonised label for |
harmonise_label_two |
Input character representing the harmonised label for |
harmonise_label_unknown |
Input character representing the harmonised label for |
error_call |
The execution environment of a currently
running function, e.g. |
Character representing one of the two standardise labels.
label_have_dyspnoea_no <- c("no", "No") label_have_dyspnoea_yes <- c("yes", "Yes") label_have_dyspnoea_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of have_dyspnoea have_dyspnoea <- "No" harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of have_dyspnoea have_dyspnoea <- "Yes" harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of have_dyspnoea have_dyspnoea <- NaN harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of have_dyspnoea have_dyspnoea <- "NIL" label_have_dyspnoea_unknown <- c("NIL") harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives error of invalid have_dyspnoea input with partial match have_dyspnoea <- "Not" label_have_dyspnoea_unknown <- c(NA, NaN) try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA )) # Gives error of invalid have_dyspnoea input without partial match have_dyspnoea <- "Something" label_have_dyspnoea_unknown <- c(NA, NaN) try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA )) # Gives error of invalid missing input of have_dyspnoea have_dyspnoea <- NA label_have_dyspnoea_unknown <- c("NIL") try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ))label_have_dyspnoea_no <- c("no", "No") label_have_dyspnoea_yes <- c("yes", "Yes") label_have_dyspnoea_unknown <- c(NA, NaN) # Gives harmonise_label_one if there is valid input of have_dyspnoea have_dyspnoea <- "No" harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_two if there is valid input of have_dyspnoea have_dyspnoea <- "Yes" harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of have_dyspnoea have_dyspnoea <- NaN harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives harmonise_label_unknown if there is valid missing input of have_dyspnoea have_dyspnoea <- "NIL" label_have_dyspnoea_unknown <- c("NIL") harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ) # Gives error of invalid have_dyspnoea input with partial match have_dyspnoea <- "Not" label_have_dyspnoea_unknown <- c(NA, NaN) try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA )) # Gives error of invalid have_dyspnoea input without partial match have_dyspnoea <- "Something" label_have_dyspnoea_unknown <- c(NA, NaN) try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA )) # Gives error of invalid missing input of have_dyspnoea have_dyspnoea <- NA label_have_dyspnoea_unknown <- c("NIL") try(harmonise_two_labels( arg = have_dyspnoea, label_one = label_have_dyspnoea_no, label_two = label_have_dyspnoea_yes, label_unknown = label_have_dyspnoea_unknown, harmonise_label_one = "no", harmonise_label_two = "yes", harmonise_label_unknown = NA ))
Function to check if the input value is an integer.
is_integer_value(input_value, allow_na = FALSE)is_integer_value(input_value, allow_na = FALSE)
input_value |
The input value |
allow_na |
If true, NA values are ignored and output is considered TRUE. Default: FALSE |
A boolean indicating TRUE when the input value is an integer and FALSE otherwise.
# An integer is_integer_value(1) # Not an integer is_integer_value(1.1) # Not numeric is_integer_value("1") # NA cases is_integer_value(NA, allow_na = FALSE) is_integer_value(NA, allow_na = TRUE)# An integer is_integer_value(1) # Not an integer is_integer_value(1.1) # Not numeric is_integer_value("1") # NA cases is_integer_value(NA, allow_na = FALSE) is_integer_value(NA, allow_na = TRUE)
A function that converts a character vector into a list phrase that uses the Oxford comma.
oxford_comma(chr, sep = ", ", final = "or")oxford_comma(chr, sep = ", ", final = "or")
chr |
A character vector to turn into a list phrase (e.g. c("a", "b", "c")). |
sep |
Separator symbols used to separate the elements in the character vector, Default: ', ' |
final |
String to use in place of the final separator when we have at least two elements in the character vector, Default: 'or'. |
A string in the form of a list that has a comma if there are at least three elements in the list (e.g. "a, b, or c")
oxford_comma(c("James", "John", "Jeremy")) oxford_comma(c("James", "John", "Jeremy"), final = "and") oxford_comma(c("James", "John")) oxford_comma(c("James"))oxford_comma(c("James", "John", "Jeremy")) oxford_comma(c("James", "John", "Jeremy"), final = "and") oxford_comma(c("James", "John")) oxford_comma(c("James"))
A function that does symmetric rounding to the nearest digits.
round_to_nearest_digit(number, digits = 0)round_to_nearest_digit(number, digits = 0)
number |
Input numeric value |
digits |
Input integer indicating the number of decimal places to be used. By default, it rounds off to the nearest integer. Default: 0 |
A numeric value rounded off to a number of decimal places specified in
the input digits.
round_to_nearest_digit(0.5) round_to_nearest_digit(1.5) round_to_nearest_digit(-0.5) round_to_nearest_digit(-1.5) round_to_nearest_digit(1021.125, digits = 2)round_to_nearest_digit(0.5) round_to_nearest_digit(1.5) round_to_nearest_digit(-0.5) round_to_nearest_digit(-1.5) round_to_nearest_digit(1021.125, digits = 2)