HEART risk score function for data frame; HEART = History, ECG, Age, Risk factors, Troponin
Source:R/02_HEART_df.R
HEART_scores.Rd
This function allows you to calculate the HEART score row wise in a data frame with the required variables. It would then retrieve a data frame with two extra columns including the calculations and their classifications
Usage
HEART_scores(
data,
typical_symptoms.num = typical_symptoms.num,
ecg.normal = ecg.normal,
abn.repolarisation = abn.repolarisation,
ecg.st.depression = ecg.st.depression,
Age = Age,
diabetes = diabetes,
smoker = smoker,
hypertension = hypertension,
hyperlipidaemia = hyperlipidaemia,
family.history = family.history,
atherosclerotic.disease = atherosclerotic.disease,
presentation_hstni = presentation_hstni,
Gender = Gender,
classify
)
Arguments
- data
A data frame with all the variables needed for calculation: typical_symptoms.num, ecg.normal, abn.repolarisation, ecg.st.depression,Age, diabetes, smoker, hypertension, hyperlipidaemia, family.history, atherosclerotic.disease, presentation_hstni, Gender
- typical_symptoms.num
a numeric vector of the number of typical symptoms
- ecg.normal
a binary numeric vector, 1 = yes and 0 = no
- abn.repolarisation
a binary numeric vector, 1 = yes and 0 = no
- ecg.st.depression
a binary numeric vector, 1 = yes and 0 = no
- Age
a numeric vector of age values, in years
- diabetes
a binary numeric vector, 1 = yes and 0 = no
- smoker
a binary numeric vector, 1 = yes and 0 = no
- hypertension
a binary numeric vector, 1 = yes and 0 = no
- hyperlipidaemia
a binary numeric vector, 1 = yes and 0 = no
- family.history
a binary numeric vector, 1 = yes and 0 = no
- atherosclerotic.disease
a binary numeric vector, 1 = yes and 0 = no
- presentation_hstni
a continuous numeric vector of the troponin levels
- Gender
a binary character vector of sex values. Categories should include only 'male' or 'female'
- classify
a logical parameter to indicate classification of Scores "TRUE" or none "FALSE"
Value
a data frame with two extra columns including the HEART score calculations and their classifications
Examples
# Create a data frame or list with the necessary variables
# Set the number of rows
num_rows <- 100
# Create a larger dataset with 100 rows
cohort_xx <- data.frame(
typical_symptoms.num = as.numeric(sample(0:6, num_rows, replace = TRUE)),
ecg.normal = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
abn.repolarisation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
ecg.st.depression = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
Age = as.numeric(sample(30:80, num_rows, replace = TRUE)),
diabetes = sample(c(1, 0), num_rows, replace = TRUE),
smoker = sample(c(1, 0), num_rows, replace = TRUE),
hypertension = sample(c(1, 0), num_rows, replace = TRUE),
hyperlipidaemia = sample(c(1, 0), num_rows, replace = TRUE),
family.history = sample(c(1, 0), num_rows, replace = TRUE),
atherosclerotic.disease = sample(c(1, 0), num_rows, replace = TRUE),
presentation_hstni = as.numeric(sample(10:100, num_rows, replace = TRUE)),
Gender = sample(c("male", "female"), num_rows, replace = TRUE)
)
# Call the function with the cohort_xx
result <- HEART_scores(data = cohort_xx, classify = TRUE)
# Print the results
summary(result$HEART_score)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 2.00 5.00 6.00 6.14 7.00 10.00
summary(result$HEART_strat)
#> Low risk Moderate risk High risk
#> 4 51 45