Compute multiple CV risk scores
Usage
compute_CVrisk(
df,
scores = c("ascvd_10y_accaha", "ascvd_10y_frs", "ascvd_10y_frs_simple", "chd_10y_mesa",
"chd_10y_mesa_cac"),
age,
gender,
race = NULL,
sbp = NULL,
bmi = NULL,
hdl = NULL,
totchol = NULL,
bp_med = NULL,
smoker = NULL,
diabetes = NULL,
lipid_med = NULL,
statin = NULL,
egfr = NULL,
fh_heartattack = NULL,
cac = NULL,
...
)Arguments
- df
input dataframe
- scores
scores to compute, default is all scores
- age
patient age in years (required for all scores)
- gender
patient gender (male or female)
- race
character string for patient race (white, aa, other) column
- sbp
character string of systolic blood pressure (in mm Hg) column
- bmi
character string of Body mass index (kg/m2) column
- hdl
character string of HDL column
- totchol
character string of total cholesterol column
- bp_med
character string of blood pressure medication column
- smoker
character string of smoking status column
- diabetes
character string of diabetes status column
- lipid_med
character string of lipid medication column (used as statin if statin not provided)
- statin
character string of statin medication column (takes precedence over lipid_med)
- egfr
character string of estimated glomerular filtration rate column
- fh_heartattack
character string of fh of heart attack status column
- cac
character string of cac column
- ...
Additional arguments to pass to score functions (e.g., model parameter for PREVENT scores)
Examples
library(CVrisk)
# Compute traditional risk scores
compute_CVrisk(sample_data,
scores = c("ascvd_10y_accaha", "ascvd_10y_frs", "ascvd_10y_frs_simple",
"chd_10y_mesa", "chd_10y_mesa_cac", "ascvd_10y_prevent"),
age = "age", race = "race", gender = "gender", bmi = "BMI", sbp = "sbp",
hdl = "hdl", totchol = "totchol", bp_med = "bp_med", smoker = "smoker",
diabetes = "diabetes", lipid_med = "lipid_med", egfr = "egfr",
fh_heartattack = "fh_heartattack", cac = "cac"
)
#> age gender race BMI sbp hdl totchol bp_med smoker diabetes lipid_med
#> 1 55 male white 30 140 50 NA 0 0 0 0
#> 2 45 female white 27 125 50 200 1 0 0 0
#> 3 45 female white 27 125 50 200 NA 0 0 0
#> 4 70 male hispanic NA 140 50 190 1 0 0 0
#> 5 70 male hispanic NA 140 50 190 1 0 0 0
#> 6 80 female chinese NA 140 50 190 1 0 0 0
#> 7 60 male aa 29 140 50 190 1 0 0 0
#> fh_heartattack cac egfr ascvd_10y_accaha ascvd_10y_frs ascvd_10y_frs_simple
#> 1 0 NA 90 NA NA 16.75
#> 2 0 0 90 1.22 4.68 4.91
#> 3 0 NA 90 NA NA NA
#> 4 0 NA 75 23.47 30.95 NA
#> 5 0 0 75 23.47 30.95 NA
#> 6 0 0 65 NA NA NA
#> 7 0 50 85 15.49 20.63 28.35
#> chd_10y_mesa chd_10y_mesa_cac ascvd_10y_prevent
#> 1 NA NA NA
#> 2 1.65 1.38 1.3
#> 3 NA NA NA
#> 4 9.34 NA NA
#> 5 9.34 3.26 NA
#> 6 5.19 1.88 NA
#> 7 5.91 8.33 5.8