ASCVD {RiskScorescvd}

ASCVD (Atherosclerotic Cardiovascular Disease) Risk Algorithm including Known ASCVD from AHA/ACC

Description

This function implements the ASCVD score calculation as a vector

Scored using 3 steps

Step 1: High-Risk Criteria: History of ASCVD
History of ASCVD - History of acute coronary syndrome (ACS), myocardial infarction (MI), stable angina, coronary/other arterial revascularization, stroke, transient ischemic attack, or Peripheral Arterial Disease (PAD) from atherosclerosis

Step 2: High-Risk Criteria:
Extreme LDL
LDL Cholesterol >= 190mg/dL (4.92 mmol/L)

Step 3: ASCVD Risk Criteria:
Only Apply When LDL 70-189mg/dL (1.81-4.90 mmol/L)

Age
Diabetes
Total cholesterol
HDL cholesterol
Systolic BP
Treatment for Hypertension
Smoker
Race
Black British as Black; everything else as White

Three possible outcome

High risk - Intensity Statin Therapy
Moderate risk- Intensity Statin Therapy
Low risk - Intensity Statin Therapy

Usage

ASCVD(
  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 = FALSE
)

Arguments

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

A vector with ASCVD 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(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

results <- cohort_xx %>% rowwise() %>%
 mutate(ASCVD_score = ASCVD(Gender, Ethnicity, Age, total.chol, total.hdl,
 systolic.bp,hypertension, smoker, diabetes, classify = FALSE))