TIMI UA/NSTEMI Risk Score function for data frame; TIMI = Thrombolysis In Myocardial Infarction

This function allows you to calculate the TIMI 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

TIMI_scores(
  data,
  Age = Age,
  hypertension = hypertension,
  hyperlipidaemia = hyperlipidaemia,
  family.history = family.history,
  diabetes = diabetes,
  smoker = smoker,
  previous.pci = previous.pci,
  previous.cabg = previous.cabg,
  aspirin = aspirin,
  number.of.episodes.24h = number.of.episodes.24h,
  ecg.st.depression = ecg.st.depression,
  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

Age

a numeric vector of age values, in years

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

diabetes

a binary numeric vector, 1 = yes and 0 = no

smoker

a binary numeric vector, 1 = yes and 0 = no

previous.pci

a binary numeric vector, 1 = yes and 0 = no

previous.cabg

a binary numeric vector, 1 = yes and 0 = no

aspirin

a binary numeric vector, 1 = yes and 0 = no

number.of.episodes.24h

a numeric vector of number of angina episodes in 24 hours

ecg.st.depression

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

set TRUE if wish to add a column with the scores' categories

Value

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),
  sweating = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  pain.radiation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  pleuritic = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  palpation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  ecg.twi = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  second_hstni = as.numeric(sample(1:200, num_rows, replace = TRUE)),
  killip.class = as.numeric(sample(1:4, num_rows, replace = TRUE)),
  systolic.bp = as.numeric(sample(0:300, num_rows, replace = TRUE)),
  heart.rate = as.numeric(sample(0:300, num_rows, replace = TRUE)),
  creat = as.numeric(sample(0:4, num_rows, replace = TRUE)),
  cardiac.arrest = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  previous.pci = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  previous.cabg = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  aspirin = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
  number.of.episodes.24h = as.numeric(sample(0:20, num_rows, replace = TRUE)),
  total.chol = as.numeric(sample(5:100, num_rows, replace = TRUE)),
  total.hdl = as.numeric(sample(2:5, num_rows, replace = TRUE)),
  Ethnicity = sample(c("white", "black", "asian", "other"), num_rows, replace = TRUE)
)


# Call the function with the cohort_xx
result <- TIMI_scores(data = cohort_xx, classify = TRUE)
# Print the results
summary(result$TIMI_score)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    2.00    4.00    4.00    4.38    5.00    7.00 
summary(result$TIMI_strat)
#> Very low risk      Low risk Moderate risk     High risk 
#>             0             2            53            45