pretestcad (version 1.1.0)

calculate_esc_2019_ptp: Calculate ESC 2019 PTP for obstructive CAD

Description

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.

Usage

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)
)

Value

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.

Arguments

age

Input integer value to indicate the age of the patient in years.

sex

The value of variable in the parameters label_sex_male, label_sex_female and label_sex_unknown.

have_dyspnoea

The value of variable in the parameters label_have_dyspnoea_no, label_have_dyspnoea_yes and label_have_dyspnoea_unknown.

chest_pain_type

The value of variable in the parameters label_cpt_no_chest_pain, label_cpt_nonanginal, label_cpt_atypical, label_cpt_typical and label_cpt_unknown.

output

Input text to indicate the how pre-test probability results be expressed Default: c("grouping", "numeric", "percentage")

• grouping means the PTP will be expressed as Low, Intermediate and High.

  • low if PTP is less than 5%.

  • intermediate if PTP is in between 5% to 15%.

  • high if PTP is more than 15%.

• numeric means the PTP will be expressed as an integer probability (0-100).

• percentage means the PTP will be expressed as percentage text (0-100%).

label_sex_male

Label(s) for definition(s) of male sex. Default: c("male")

label_sex_female

Label(s) for definition(s) of female sex. Default: c("female")

label_sex_unknown

Label(s) for definition(s) of missing sex. Default: c(NA, NaN)

label_have_dyspnoea_no

Label(s) for patient having no dyspnoea symptoms. Default: c("no")

label_have_dyspnoea_yes

Label(s) for patient having dyspnoea symptoms. Default: c("yes")

label_have_dyspnoea_unknown

Label(s) for patient having unknown dyspnoea symptoms. Default: c(NA, NaN)

label_cpt_no_chest_pain

Label(s) for patient having no chest pain. Default: c("no chest pain")

label_cpt_nonanginal

Label(s) for patient having nonanginal or non-specific chest pain. Default: c("nonanginal")

label_cpt_atypical

Label(s) for patient having atypical chest pain. Default: c("atypical")

label_cpt_typical

Label(s) for patient having typical chest pain. Default: c("typical")

label_cpt_unknown

Label(s) for patient having unknown chest pain type symptoms. Default: c(NA, NaN)

Details

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.

Examples

# 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"
)