Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data

Abstract

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.

Development of a system to support warfarin dose decisions using deep neural networks

“…There are various warfarin dose suggestion models that use artificial intelligence (AI) 17 , 20 , 21 However, these models are all limited in that they were constructed to predict optimum initial starting or maintenance warfarin doses using only cross-sectional data. However, this approach does not reflect the actual human decision-making process.…”

“…The algorithm developed in this study was trained to adapt to individual pharmacokinetic and pharmacodynamic characteristics by examining how fluctuations in a person’s PT INR correlated with their warfarin doses over the 4 days. Several other studies divided patients according to dose ranges and developed a separate algorithm for each and so could not cover a wide dose range 11 , 17 , 21 . However, the algorithm developed in this study does not require patients to be classified by dose because it was trained on the whole warfarin dose range and PT INR responses.…”

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters

“…We selected the percentage within 20% because this range has been widely accepted and applied to assess models for predicting the dosages of such drugs as warfarin and tacrolimus (see Table 1) [19][20][21][22] . Moreover, ignoring problems of adherence and pharmacokinetic alteration, the intra-individual variation in the C/D ratio is generally considered to not be above 20% 7 .…”

“…The application of ML to clinical drug therapies has garnered considerable research interest in recent years, and is playing an increasingly important role in the development of personalized dosing, especially in drug dose selection 17 . A few studies have been published on the application of ML to predict either drug doses or blood concentrations [18][19][20][21][22][23] . Jovanović et al 18 explored the application of ML as an alternative to pharmacokinetics analysis.…”

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

“…Recommended warfarin stable dose algorithms are based on multiple linear regression models (Johnson et al, 2017). Given that the relationship between warfarin dose and predictor variables is complex, nonlinear modeling strategies have been tested in warfarin dose prediction (Grossi et al, 2014;Liu et al, 2015;Roche-Lima et al, 2020). Non-parametric machine learning models are potentially powerful alternatives to linear parametric models in that they lack many of the assumptions of linear regression and they are flexible enough to fit virtually any curve in the data.…”