/
predict_risk.R
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predict_risk.R
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#' CVD Risk Calculator
#'
#' @description This function implements
#'
#' - the Pooled Cohort Risk equations from Goff et al, 2013.
#'
#' - the updated Pooled Cohort Risk equations from Yadlowski et al, 2018
#'
#' - The PREVENT equations from Khan et al, 2023
#'
#' These equations predict 10-year risk of a first atherosclerotic
#' cardiovascular disease (ASCVD) event, such as a stroke or myocardial
#' infarction. The 2017 American College of Cardiology and American Heart
#' Association blood pressure guideline recommends using 10-year predicted
#' atherosclerotic cardiovascular disease risk to guide the decision to
#' initiate or intensify antihypertensive medication. The guideline recommends
#' using the Pooled Cohort risk prediction equations to predict 10-year
#' atherosclerotic cardiovascular disease risk in clinical practice.
#'
#' @param age_years numeric vector of age values, in years.
#'
#' @param race character vector of race values. Categories should include
#' only 'black' or 'white'. If the race variable has additional categories
#' other than 'black' or 'white', then group all non 'black' values into
#' the 'white' category. This variable is not required if
#' `equation_version = 'Khan_2023'`
#'
#' @param sex character vector of sex values. Categories should include
#' only 'male' or 'female'.
#'
#' @param smoke_current character vector of current smoking habits. Categories
#' should include only 'no' and 'yes'.
#'
#' @param chol_total_mgdl total cholesterol, in mg/dL.
#'
#' @param chol_hdl_mgdl HDL-cholesterol, in mg/dL.
#'
#' @param bp_sys_mmhg systolic blood pressure, in mm Hg.
#'
#' @param bp_meds character vector of blood pressure medication use habits.
#' Categories should include only 'no' and 'yes'. For example, if currently
#' using medication to lower blood pressure, the value should be 'yes'.
#'
#' @param statin_meds character vector of statin medication use habits.
#' Categories should include only 'no' and 'yes'. For example, if currently
#' using a statin, the value should be 'yes'. This variable is only
#' required if `equation_version = 'Khan_2023'`
#'
#' @param diabetes character vector of diabetes status. Categories
#' should include only 'no' and 'yes'. For example, if diabetes is present,
#' the value should be 'yes'.
#'
#' @param bmi numeric vector of bmi values. Only required if
#' `equation_version = "Khan_2023"`
#'
#' @param egfr_mlminm2 numeric vector of egfr_mlminm2 values. Only required
#' if `equation_version = "Khan_2023"`
#'
#' @param acr numeric vector of acr values. Only required if
#' `equation_version = "Khan_2023"` and `prevent_type` is `"acr"` or
#' `"full"`.
#'
#' @param hba1c numeric vector of hba1c values. Only required if
#' `equation_version = "Khan_2023"` and `prevent_type` is `"hba1c"` or
#' `"full"`.
#'
#' @param sdi numeric vector of sdi values. Only required if
#' `equation_version = "Khan_2023"` and `prevent_type` is `"sdi"` or
#' `"full"`.
#'
#' @param race_levels a list of length 2 with names 'black' and 'white'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `race` belong to the 'black' and 'white' categories. For example,
#' `race` may contain values of 'african_american', 'white', and
#' 'hispanic'. In this case, `race_levels` should be
#' `list(white = c('white', 'hispanic'), black = 'african_american')`.
#'
#' @param sex_levels a list of length 2 with names 'female' and 'male'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `sex` belong to the 'female' and 'male' categories (see examples).
#'
#' @param smoke_current_levels a list of length 2 with names 'no' and 'yes'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `smoke_current` belong to the 'no' and 'yes' categories (see examples).
#'
#' @param bp_meds_levels a list of length 2 with names 'no' and 'yes'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `bp_meds` belong to the 'no' and 'yes' categories (see examples).
#'
#' @param statin_meds_levels a list of length 2 with names 'no' and 'yes'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `statin_meds` belong to the 'no' and 'yes' categories (see examples).
#'
#' @param diabetes_levels a list of length 2 with names 'no' and 'yes'.
#' values in the list should be character vectors of any length, and
#' values in the character vectors should indicate what values in
#' `diabetes` belong to the 'no' and 'yes' categories (see examples).
#'
#' @param equation_version a character value of length 1. Valid options are
#'
#' - 'Goff_2013'
#'
#' - 'Yadlowsky_2018'
#'
#' - 'Khan_2023'
#'
#' If 'Goff_2013' (the default option) is selected, the original Pooled
#' Cohort risk equations are used (See Goff et al., 2013).
#'
#' If 'Yadlowsky_2018' is selected, the equations recommended by Yadlowsky
#' et al., 2018 are used.
#'
#' If 'Khan_2023' is selected, the equations recommended by Khan
#' et al., 2023 are used.
#'
#' @param prevent_type a character value of length 1. Only required if
#' `equation_version = "Khan_2023"`. Valid options are:
#'
#' - 'base': computes the base PREVENT equation (default).
#'
#' - 'acr': computes the PREVENT equation using albumin-to-creatinine ratio.
#'
#' - 'hba1c': computes the PREVENT equation using hemoglobin A1c.
#'
#' - 'sdi': computes the PREVENT equation using social deprivation index.
#'
#' - 'full': computes the PREVENT equation using all novel predictors.
#'
#' @param override_boundary_errors a logical vector of length 1. If `FALSE`
#' (the default), then `predict_10yr_ascvd_risk()` will throw hard errors
#' if you give it continuous input values that are outside the bounaries
#' of what the Pooled Cohort risk calculator recommends. If `TRUE`, errors
#' will not be thrown. Please use with caution.
#'
#'
#' @return a numeric vector with 10-year predicted risk values for ASCVD events.
#'
#' @export
#'
#' @details The 2017 American College of Cardiology (ACC) / American Heart
#' Association (AHA) blood pressure (BP) guideline recommends using 10-year
#' predicted atherosclerotic cardiovascular disease (ASCVD) risk to guide
#' the decision to initiate antihypertensive medication. The guideline
#' recommends using the Pooled Cohort risk prediction equations (Goff et al, 2013)
#' to predict 10-year ASCVD risk. The Pooled Cohort risk prediction equations
#' have been externally validated in several studies and, in some populations,
#' are known to overestimate 10-year ASCVD risk. In 2018, an updated set of
#' equations were developed by Yadlowsky et al. using more contemporary data
#' and statistical methods.
#'
#' @references Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB,
#' Gibbons R, Greenland P, Lackland DT, Levy D, O’donnell CJ, Robinson JG.
#' 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report
#' of the American College of Cardiology/American Heart Association Task
#' Force on Practice Guidelines. *Journal of the American College of Cardiology*.
#' 2014 Jul 1;63(25 Part B):2935-59. DOI: 10.1016/j.jacc.2014.03.006
#'
#' Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min YI, Basu S.
#' Clinical implications of revised pooled cohort equations for estimating
#' atherosclerotic cardiovascular disease risk. *Annals of internal medicine*.
#' 2018 Jul 3;169(1):20-9. DOI: 10.7326/M17-3011
#'
#' Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL,
#' Palaniappan LP, Sperling LS, Virani SS, Ho JE, Neeland IJ, Tuttle KR,
#' Rajgopal Singh R, Elkind MSV, Lloyd-Jones DM; American Heart Association.
#' Novel Prediction Equations for Absolute Risk Assessment of Total
#' Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic
#' Health: A Scientific Statement From the American Heart Association.
#' *Circulation*. 2023 Dec 12;148(24):1982-2004. PMID: 37947094.
#'
#' @examples
#'
#' # example taken from Goff et al, 2013
#'
#' sex = c('female', 'female', 'male', 'male')
#' race = c('black', 'white', 'black', 'white')
#' # 55 years of age
#' age_years = rep(55, times = 4)
#' # total cholesterol 213 mg/dL
#' chol_total_mgdl = rep(213, times = 4)
#' # HDL cholesterol 50 mg/dL
#' chol_hdl_mgdl = rep(50, times = 4)
#' # untreated systolic BP 120 mm Hg
#' bp_sys_mmhg = rep(120, times = 4)
#' bp_meds = rep('no', times = 4)
#' # nonsmoker
#' smoke_current = rep('no', times = 4)
#' # without diabetes
#' diabetes = rep('no', times = 4)
#'
#' pcr_probs <- predict_10yr_ascvd_risk(
#' sex = sex,
#' race = race,
#' age_years = age_years,
#' chol_total_mgdl = chol_total_mgdl,
#' chol_hdl_mgdl = chol_hdl_mgdl,
#' bp_sys_mmhg = bp_sys_mmhg,
#' bp_meds = bp_meds,
#' smoke_current = smoke_current,
#' diabetes = diabetes
#' )
#'
#' # note that this isn't an exact match of Table 4 in
#' # Goff et al supplement - this is because the table's
#' # coefficients are rounded to a lower decimal count than
#' # the coefficients used in predict_10yr_ascvd_risk()
#' round(100 * pcr_probs, 1)
#'
#' # using a data frame with more granular categories and names
#'
#' some_data <- data.frame(
#' gender = c('woman', 'woman', 'man', 'male'),
#' race_3cats = c('AA', 'white', 'AA', 'other'),
#' # 55 years of age
#' age_years = rep(55, times = 4),
#' # total cholesterol 213 mg/dL
#' chol_total_mgdl = rep(213, times = 4),
#' # HDL cholesterol 50 mg/dL
#' chol_hdl_mgdl = rep(50, times = 4),
#' # untreated systolic BP 120 mm Hg
#' bp_sys_mmhg = rep(120, times = 4),
#' bp_meds = rep('No', times = 4),
#' # nonsmoker
#' smoke_current = c("no", "former", "no", "never"),
#' # without diabetes
#' diabetes = rep('No', times = 4),
#' stringsAsFactors = FALSE
#' )
#'
#' pcr_probs <- with(
#' some_data,
#' predict_10yr_ascvd_risk(
#' sex = gender,
#' sex_levels = list(female = 'woman', male = c('man', 'male')),
#' race = race_3cats,
#' age_years = age_years,
#' chol_total_mgdl = chol_total_mgdl,
#' chol_hdl_mgdl = chol_hdl_mgdl,
#' bp_sys_mmhg = bp_sys_mmhg,
#' bp_meds = bp_meds,
#' smoke_current = smoke_current,
#' diabetes = diabetes,
#' race_levels = list(black = 'AA', white = c('white', 'other')),
#' smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
#' bp_meds_levels = list(no = 'No', yes = 'Yes'),
#' diabetes_levels = list(no = 'No', yes = 'Yes')
#' )
#' )
#'
#'
predict_10yr_ascvd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Goff_2013',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'ascvd',
year = 10,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_10yr_cvd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'cvd',
year = 10,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_10yr_hf_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'hf',
year = 10,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_10yr_chd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'chd',
year = 10,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_10yr_stroke_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'stroke',
year = 10,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_30yr_ascvd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'ascvd',
year = 30,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_30yr_cvd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'cvd',
year = 30,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_30yr_hf_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'hf',
year = 30,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_30yr_chd_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'chd',
year = 30,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_30yr_stroke_risk <- function(
age_years,
race = NULL,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds = NULL,
diabetes,
bmi = NULL,
egfr_mlminm2 = NULL,
acr = NULL,
hba1c = NULL,
sdi = NULL,
equation_version = 'Khan_2023',
prevent_type = 'base',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
statin_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
._predict_risk(pred_type = 'stroke',
year = 30,
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
equation_version = equation_version,
prevent_type = prevent_type,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels)
}
._predict_risk <- function(pred_type,
year,
age_years,
race,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
statin_meds,
diabetes,
bmi,
egfr_mlminm2,
acr,
hba1c,
sdi,
equation_version,
prevent_type,
override_boundary_errors,
race_levels,
sex_levels,
smoke_current_levels,
bp_meds_levels,
statin_meds_levels,
diabetes_levels){
check_input(
arg_name = 'equation_version',
arg_value = equation_version,
expected = list(
type = 'character',
length = 1,
options = c("Goff_2013", "Yadlowsky_2018", "Khan_2023")
)
)
check_input(
arg_name = 'override_boundary_errors',
arg_value = override_boundary_errors,
expected = list(
type = 'logical',
length = 1
)
)
if(equation_version %in% c("Goff_2013", "Yadlowsky_2018")){
if(is.null(race)){
stop("missing required variable for Pooled Cohort equations: race")
}
result <- ._pcr(
age_years = age_years,
race = race,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
diabetes = diabetes,
equation_version = equation_version,
override_boundary_errors = override_boundary_errors,
race_levels = race_levels,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
diabetes_levels = diabetes_levels,
year = year
)
} else if (equation_version == "Khan_2023"){
missing <- c(is.null(statin_meds), is.null(egfr_mlminm2), is.null(bmi))
missing_vars <- c("statin_meds", "egfr_mlminm2", "bmi")[missing]
if(length(missing_vars) > 0){
plural <- length(missing_vars > 1)
if(plural){
missing_vars <- paste(missing_vars, collapse = ', ')
stop("missing required variables for PREVENT equations: ",
missing_vars)
} else {
stop("missing required variable for PREVENT equations: ",
missing_vars)
}
}
result <- ._prevent(age_years = age_years,
sex = sex,
smoke_current = smoke_current,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
statin_meds = statin_meds,
diabetes = diabetes,
bmi = bmi,
egfr_mlminm2 = egfr_mlminm2,
acr = acr,
hba1c = hba1c,
sdi = sdi,
override_boundary_errors = override_boundary_errors,
sex_levels = sex_levels,
smoke_current_levels = smoke_current_levels,
bp_meds_levels = bp_meds_levels,
statin_meds_levels = statin_meds_levels,
diabetes_levels = diabetes_levels,
prevent_type = prevent_type,
pred_type = pred_type,
year = year)
}
result
}
#' @rdname predict_10yr_ascvd_risk
#' @export
predict_5yr_ascvd_risk <- function(
age_years,
race,
sex,
smoke_current,
chol_total_mgdl,
chol_hdl_mgdl,
bp_sys_mmhg,
bp_meds,
diabetes,
equation_version = 'Goff_2013',
override_boundary_errors = FALSE,
race_levels = list(black = 'black', white = 'white'),
sex_levels = list(female = 'female', male = 'male'),
smoke_current_levels = list(no = 'no', yes = 'yes'),
bp_meds_levels = list(no = 'no', yes = 'yes'),
diabetes_levels = list(no = 'no', yes = 'yes')
) {
check_input(
arg_name = 'equation_version',
arg_value = equation_version,
expected = list(
type = 'character',
length = 1,
options = c("Goff_2013", "Yadlowsky_2018"))
)
if(equation_version == 'Yadlowsky_2018'){
stop("only 10-year ASCVD risk is available for Yadlowsky 2018",
call. = FALSE)
}
check_input(
arg_name = 'override_boundary_errors',
arg_value = override_boundary_errors,
expected = list(
type = 'logical',
length = 1