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Systematic COronary Risk Evaluation (SCORE) model function

Source: R/11_SCORE2_func.R
SCORE2-slash-OP.Rd

This function implements the SCORE2 and SCORE2 older population (OP) score calculation as a vector

*Diabetes mellitus was included in the modelling since this was necessary for the recalibration approach, which relies data from the whole population, including those with diabetes. However, SCORE2 is not intended for use in individuals with diabetes and has not been validated in this population. For risk prediction in the target population of individuals without diabetes this risk factor will always be 0, meaning the coefficient can effectively be ignored.

formula in SCORE2 Updated Supplementary Material page 9. paper: "SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe"

Age 10-year risk of fatal and non-fatal cardiovascular disease

| Low risk | Moderate risk | High risk |
| :———— | ————- | ————— | ———-: |
| < 50 years | <2.5 | 50 - 69 years | <5 | => 70 years | <7.5

above classifications referred from https://www.inanutshell.ch/en/digital-doctors-bag/score2-and-score2-op/#:~:text=SCORE2

Usage

SCORE2(
  Risk.region,
  Age = Age,
  Gender = Gender,
  smoker = smoker,
  systolic.bp = systolic.bp,
  diabetes = diabetes,
  total.chol = total.chol,
  total.hdl = total.hdl,
  classify
)

Arguments

Risk.region

a character value to set the desired risk region calculations. Categories should include "Low", "Moderate", "High", or "Very high"

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

A vector with SCORE2/OP score calculations and/or a vector of their classifications if indicated

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

  results <- cohort_xx %>% rowwise() %>%
  mutate(SCORE2OP_score = SCORE2(Risk.region = "Low", Age, Gender, smoker,
  systolic.bp, diabetes, total.chol, total.hdl, classify = FALSE))