Why {preventr}?
The goal of {preventr} is to implement the American Heart Association (AHA) Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations. The PREVENT equations are a large collection of predictive/prognostic models for predicting the risk of cardiovascular disease events over the next 10 and 30 years. Specifically, estimation includes both 10- and 30-year risk of 5 events:
- Total cardiovascular disease (CVD)
- This outcome includes atherosclerotic CVD (ASCVD) and heart failure as defined below
- ASCVD
- This outcome includes coronary heart disease (CHD) and stroke as defined below
- Heart failure (often abbreviated HF, but not herein)
- CHD
- This outcome includes nonfatal myocardial infarction (MI) and fatal CHD
- Stroke
The predicted risk of each of these 5 outcomes is defined by separate beta coefficients and intercepts. Additionally, the beta coefficients and intercepts are sex-specific, yielding 10 sets of beta coefficients and intercepts for the 5 outcomes predicted by the PREVENT equations.
But wait, there’s more. In addition, there are 5 model “types”:
- the base model
- the base model adding HbA1c
- the base model adding urine albumin-to-creatinine ratio (UACR)
- the base model adding social deprivation index (SDI)
- the base model adding HbA1c, UACR, and SDI (also called the “full model”)
We are now up to 50 sets of beta coefficients and intercepts.
Finally, there are separate models for 10- and 30-year risks, bringing the grand total to 100 sets of beta coefficients and intercepts for the entirety of the PREVENT equations.
{preventr} takes care of all of that for you. This includes selecting among the 5 model “types”, but there is an option to specify this yourself if you want. The example below gives a quick overview, but the function documentation has much more detail, including many more examples.
The original article describing the derivation and validation of the PREVENT equations is available here.
Notably, the PREVENT equations have better calibration than the Pooled Cohort Equations, which were originally released in 2013 as part of the American College of Cardiology/American Heart Association (ACC/AHA) Guideline on the Assessment of Cardiovascular Risk.
Installation
Install the released version of {preventr} from CRAN with:
install.packages("preventr")You can install the development version of {preventr} from GitHub with:
# install.packages("devtools")
devtools::install_github("martingmayer/preventr")Using {preventr}
Despite the work it does, I designed {preventr} with the goal of having a simple, intuitive API. The “workhorses” are “behind the scenes”, with just one function (and synonym) being exposed to the user:
# Very basic example; see function documentation for many more examples
preventr::estimate_risk(
age = 50,
sex = "female",
sbp = 160,
bp_tx = TRUE,
total_c = 200,
hdl_c = 45,
statin = FALSE,
dm = TRUE,
smoking = FALSE,
egfr = 90,
bmi = 35
)
#> Estimates are from: Base model.
#> $risk_est_10yr
#> # A tibble: 1 × 8
#> total_cvd ascvd heart_failure chd stroke model over_years input_problems
#> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <int> <chr>
#> 1 0.147 0.092 0.081 0.044 0.054 base 10 <NA>
#>
#> $risk_est_30yr
#> # A tibble: 1 × 8
#> total_cvd ascvd heart_failure chd stroke model over_years input_problems
#> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <int> <chr>
#> 1 0.53 0.354 0.39 0.198 0.221 base 30 <NA>Why not 1.0.0?
First and most importantly: I have tested the package’s functionality extensively. That is not the reason for the < 1.0.0 release.
Rather, I developed the API with an eye toward maximizing simplicity and intuitiveness. Thus, while I do not anticipate any changes to the API, I remain open to the idea there may be improvements to the API that may surface after more people use this package. This is the only reason I did not release as 1.0.0. Nevertheless, users should rest assured if any changes come to the API, I will avoid breaking changes unless they are absolutely necessary or there is an extremely compelling argument for making such changes; likewise, any such changes will be communicated clearly and in advance. Again, I consider all of this very unlikely, but in the off chance something like that does arise, I also want others to rest assured I will make every effort to ensure there are no surprises, essentially following the best practices outlined at the excellent reference R Packages.
How to cite
If you use {preventr} in your work, please cite the package as follows:
citation("preventr")Acknowledgments
This package would, of course, not be possible without the efforts from the authors of the PREVENT equations. Additionally, the Social Deprivation Index (SDI) is a key element informing the PREVENT equations. Citations for both appear below, as does acknowledgment of the other packages and software I used in creating {preventr}.
The PREVENT equations
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. Epub 2023 Nov 10. PMID: 37947085.
Social Deprivation Index (SDI)
Social deprivation index (SDI). Robert Graham Center - Policy Studies in Family Medicine & Primary Care. (2018, November 5). Retrieved December 13, 2023, from https://www.graham-center.org/maps-data-tools/social-deprivation-index.html.
Other packages and software
{preventr} also makes use of the {dplyr} package. It also uses {zipcodeR} to help validate zip code entry, though this is entirely “behind the scenes” as a data set, and thus {zipcodeR} is not imported as part of using {preventr}. Thank you to the authors and maintainers of these packages.
The authors and maintainers of packages dedicated to developing packages also deserve recognition. These include but are not necessarily limited to: {devtools}, {roxygen2}, {usethis}, {testthat}, and {pkgdown}. The {rmarkdown} package, though not dedicated to package development, also deserves explicit recognition, as it directly and significantly contributes to the feasibility and functionality of packages like {pkgdown} and {roxygen2}. Thank you to the authors and maintainers of these packages.
A huge thank you is also in order for the R Core Team and others who have contributed to maintaining R, as well as the CRAN Team. Your tireless work is, of course, foundational to all the above packages.
Lastly, I also used DALL·E 3 to help generate the logo. Thank you to the authors and maintainers of DALL·E 3.