SCORE2/OP risk score function for data frame; SCORE2/OP = Systematic COronary Risk Evaluation /and Older Population

This function allows you to calculate the SCORE2 and OP 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

SCORE2_scores(
  data,
  Risk.region,
  Age = Age,
  Gender = Gender,
  smoker = smoker,
  systolic.bp = systolic.bp,
  diabetes = diabetes,
  total.chol = total.chol,
  total.hdl = total.hdl,
  classify
)

Arguments

data

A data frame with all the variables needed for calculation: Age, Gender, smoker, systolic.bp, diabetes, total.chol, total.hdl

Risk.region

a character value to set the desired risk region calculations. Categories should include

Age

a numeric vector of age values, in years

Gender

a binary character vector of Gender values. Categories should include only 'male' or 'female'.

smoker

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

systolic.bp

a numeric vector of systolic blood pressure continuous values

diabetes

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

total.chol

a numeric vector of total cholesterol values, in mmol/L

total.hdl

a numeric vector of total high density lipoprotein total.hdl values, in mmol/L

classify

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

Value

data frame with two extra columns including the SCORE2/OP 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(40:85, 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(90:180, 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(round(runif(num_rows, 3.9, 7.2), 1)),
  total.hdl = as.numeric(round(runif(num_rows, .8, 2.1), 1)),
  Ethnicity = sample(c("white", "black", "asian", "other"), num_rows, replace = TRUE)
)

# Call the function with the cohort_xx
result <- SCORE2_scores(data = cohort_xx, Risk.region = "Low", classify = TRUE)

# Print the results
summary(result$SCORE2_score)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   4.475   7.750  10.421  14.200  35.900 
summary(result$SCORE2_strat)
#> Very low risk      Low risk Moderate risk     High risk 
#>             0            23            39            38