RiskScoresCalc: Six commonly used cardiovascular risk scores for the...

In RiskScorescvd: Cardiovascular Risk Scores Calculator

Six commonly used cardiovascular risk scores for the prediction of major cardiac events (MACE)

Description

This function implements seven cardiovascular risk scores row wise in a data frame with the required variables. It would then retrieve a data frame with two extra columns for each risk score including their calculations and classifications

Usage

calc_scores(
  data,
  typical_symptoms.num = typical_symptoms.num,
  ecg.normal = ecg.normal,
  abn.repolarisation = abn.repolarisation,
  ecg.st.depression = ecg.st.depression,
  Age = Age,
  diabetes = diabetes,
  smoker = smoker,
  hypertension = hypertension,
  hyperlipidaemia = hyperlipidaemia,
  family.history = family.history,
  atherosclerotic.disease = atherosclerotic.disease,
  presentation_hstni = presentation_hstni,
  Gender = Gender,
  sweating = sweating,
  pain.radiation = pain.radiation,
  pleuritic = pleuritic,
  palpation = palpation,
  ecg.twi = ecg.twi,
  second_hstni = second_hstni,
  killip.class = killip.class,
  heart.rate = heart.rate,
  systolic.bp = systolic.bp,
  aspirin = aspirin,
  number.of.episodes.24h = number.of.episodes.24h,
  previous.pci = previous.pci,
  creat = creat,
  previous.cabg = previous.cabg,
  total.chol = total.chol,
  total.hdl = total.hdl,
  Ethnicity = Ethnicity,
  eGFR = eGFR,
  ACR = NA,
  trace = NA
)

Arguments

data	A data frame with all the variables needed for calculation:
typical_symptoms.num	a numeric vector of the number of typical symptoms; renames alternative column name
ecg.normal	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
abn.repolarisation	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
ecg.st.depression	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
Age	a numeric vector of age values, in years; renames alternative column name
diabetes	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
smoker	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
hypertension	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
hyperlipidaemia	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
family.history	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
atherosclerotic.disease	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
presentation_hstni	a continuous numeric vector of the troponin levels; renames alternative column name
Gender	a binary character vector of sex values. Categories should include only 'male' or 'female'; renames alternative column name
sweating	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
pain.radiation	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
pleuritic	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
palpation	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
ecg.twi	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
second_hstni	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
killip.class	a numeric vector of killip class values, 1 to 4; renames alternative column name
heart.rate	a numeric vector of heart rate continuous values; renames alternative column name
systolic.bp	a numeric vector of systolic blood pressure continuous values; renames alternative column name
aspirin	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
number.of.episodes.24h	a numeric vector of number of angina episodes in 24 hours; renames alternative column name
previous.pci	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
creat	a continuous numeric vector of the creatine levels
previous.cabg	a binary numeric vector, 1 = yes and 0 = no; renames alternative column name
total.chol	a numeric vector of total cholesterol values, in mmol/L; renames alternative column name
total.hdl	a numeric vector of total high density lipoprotein HDL values, in mmol/L; renames alternative column name
Ethnicity	a character vector, 'white', 'black', 'asian', or other
eGFR	a numeric vector of total estimated glomerular rate (eGFR) values, in mL/min/1.73m2
ACR	a numeric vector of total urine albumin to creatine ratio (ACR) values, in mg/g. Default set to NA
trace	a character vector of urine protein dipstick categories. Categories should include 'negative', 'trace', '1+', '2+', '3+', '4+. Default set to NA

Value

a data frame with two extra columns including all the cardiovascular risk 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),
  eGFR = as.numeric(sample(15:120, num_rows, replace = TRUE)),
  ACR = as.numeric(sample(5:1500, num_rows, replace = TRUE)),
  trace = sample(c("trace", "1+", "2+", "3+", "4+"), num_rows, replace = TRUE)
)



# Call the function with the cohort_xx

new_data_frame <- calc_scores(data = cohort_xx)

RiskScorescvd documentation built on June 27, 2024, 5:13 p.m.