# https://github.com/r-lib/pkgdown/issues/2704

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ASCVD risk score function for data frame; ASCVD = Atherosclerotic Cardiovascular Disease

Source: R/10_ASCVD_df.R
ASCVD_scores.Rd

This function allows you to calculate the ASCVD 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

ASCVD_scores(
  data,
  Gender = Gender,
  Ethnicity = Ethnicity,
  Age = Age,
  total.chol = total.chol,
  total.hdl = total.hdl,
  systolic.bp = systolic.bp,
  hypertension = hypertension,
  smoker = smoker,
  diabetes = diabetes,
  classify
)

Arguments

data

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

Gender

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

Ethnicity

a character vector, 'white', 'black', 'asian', or other

Age

a numeric vector of age values, in years

total.chol

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

total.hdl

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

systolic.bp

a numeric vector of systolic blood pressure continuous values

hypertension

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

smoker

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

diabetes

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

classify

a logical parameter to indicate classification of Scores "TRUE" or none "FALSE"

Value

data frame with two extra columns including the ASCVD 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(2:6, 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 <- ASCVD_scores(data = cohort_xx, classify = TRUE)
# Print the results
summary(result$ASCVD_score)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.0000  0.0000  0.0200  0.1002  0.0925  0.9500 
summary(result$ASCVD_strat)
#> Very low risk      Low risk Moderate risk     High risk 
#>            59             8            21            12