CVrisk (version 1.2.1)

ascvd_30y_prevent: PREVENT 30-year ASCVD risk score

Description

Computes 30-year risk for ASCVD (atherosclerotic cardiovascular disease) using the American Heart Association PREVENT equations (2023).

Usage

ascvd_30y_prevent(
  gender = c("male", "female"),
  age,
  sbp,
  bp_med,
  totchol,
  hdl,
  statin,
  diabetes,
  smoker,
  egfr,
  bmi,
  hba1c = NULL,
  uacr = NULL,
  zip = NULL,
  model = "auto",
  ...
)

Value

30-year ASCVD risk estimate (percent)

Arguments

gender

patient gender (male, female)

age

patient age (years), between 30 and 79

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

statin

Patient is on a statin (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

egfr

Estimated glomerular filtration rate (mL/min/1.73m2)

bmi

Body mass index (kg/m2)

hba1c

Glycated hemoglobin (HbA1c) in percent (optional)

uacr

Urine albumin-to-creatinine ratio in mg/g (optional)

zip

ZIP code for Social Deprivation Index (optional)

model

PREVENT model variant to use: "auto" (default, selects based on available data), "base", "hba1c", "uacr", "sdi", or "full"

...

Additional predictors can be passed and will be ignored

References

Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, Go AS, Gutierrez OM, Hwang SJ, Jassal SK, Kovesdy CP, Lloyd-Jones DM, Shlipak MG, Palaniappan LP, Sperling L, Virani SS, Tuttle K, Neeland IJ, Chow SL, Rangaswami J, Pencina MJ, Ndumele CE, Coresh J; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024 Feb 6;149(6):430-449.

Examples

library(CVrisk)
# Base model (default when model = "auto" and no optional predictors provided)
ascvd_30y_prevent(
  gender = "female", age = 50,
  sbp = 160, bp_med = 1,
  totchol = 200, hdl = 45,
  statin = 0, diabetes = 1, smoker = 0,
  egfr = 90, bmi = 35
)

# Explicitly specify base model
ascvd_30y_prevent(
  gender = "male", age = 45,
  sbp = 130, bp_med = 0,
  totchol = 200, hdl = 50,
  statin = 0, diabetes = 0, smoker = 1,
  egfr = 95, bmi = 28,
  model = "base"
)

# Auto model with UACR (will use uacr model variant)
ascvd_30y_prevent(
  gender = "male", age = 55,
  sbp = 140, bp_med = 0,
  totchol = 213, hdl = 50,
  statin = 0, diabetes = 0, smoker = 0,
  egfr = 90, bmi = 30,
  uacr = 25
)