Abstract
Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.
Highlights
Despite the availability of direct oral anticoagulants (DOACs), warfarin remains a commonly prescribed drug in the United States and Latin America
While some previous literature suggests nonlinear models may outperform multiple linear regression methods when used to predict warfarin dose, our observations suggest that linear models perform to nonlinear models in diverse populations including a high number of US Latinos and Latin Americans (ULLA) participants
HS, HP, JG, and JK wrote the manuscript; HS, JF, XS and JK designed the research; HS performed the research; HS analyzed the data; NE, KC, LT, MB, LM, JG, CC, MH, SS, LC, DF, JD and PS revised the manuscript and contributed data. In this systematic comparison of nine models, classic linear regression models remained advantageous compared to nonlinear models with respect to prediction accuracy of therapeutic warfarin dose in a large diverse cohort as well as a Hispanic/Latino cohort alone
Summary
Despite the availability of direct oral anticoagulants (DOACs), warfarin remains a commonly prescribed drug in the United States and Latin America. Clinical concerns with DOACs continue to limit their use, including fewer indications than warfarin, concerns about bleeding risk and renal function, availability and cost of reversal agents, and contraindication in valvular heart disease (Nielsen et al, 2015; Verdecchia et al, 2016; Mendoza-Sanchez et al, 2018; Vinogradova et al, 2018; Zhu et al, 2018) This is especially true for medically underserved patients, including US Latino and Latin American (ULLA) patients, who may have access barriers to newer agents because of high costs and copays(Kirley et al, 2012; Shahin and Giacomini, 2020). Given the long track record of warfarin use in clinical practice, its affordable cost, and limited clinical utility of DOACs in special populations, warfarin is likely to continue to be preferentially used over DOACs in a substantial proportion of the population(Shahin et al, 2011; Barnes et al, 2015; Arwood et al, 2017)
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