Abstract
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters. Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy. Compared to the IWPC-derived MLR algorithm, Stack 1 and 2 based on stacked generalization frameworks performed significantly better overall. Subgroup analysis revealed that the mean of the percentage of patients whose predicted dose of warfarin within 20% of the actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08% to 25.05%) in the low-dose group compared to that for MLR, respectively. These data suggest that our algorithms would especially benefit patients requiring low warfarin maintenance dose, as subtle changes in warfarin dose could lead to adverse clinical events (thrombosis or bleeding) in patients with low dose. Our study offers novel pharmacogenetic algorithms for clinical trials and practice.
Highlights
Warfarin is the most widely used oral anticoagulant worldwide
In Asians, Stack 1 and Stack 2 performed significantly better than multivariate linear regression (MLR) for both mean absolute error (MAE) and mean percentage within 20% (Table 4)
In the intermediate-dose group, Stack 1 and Stack 2 performed significantly better than MLR for both MAE and mean percentage within 20% (P < 0.001)
Summary
Warfarin is the most widely used oral anticoagulant worldwide. Due to its narrow therapeutic window and large interpatient variability, warfarin dosing remains challenging [1]. Consequence of incorrect warfarin dosing can be devastating, predisposing the patient to thrombosis in the case of under-dosing or bleeding in the case of overdosing Because of these challenges associated with warfarin use, it is one of the leading causes in emergency department visits and the most often cited cause of drug-related mortality [2]. Other advanced machine learning approaches such as deep learning (neural networks), tree-based algorithms and support vector machines have been used to predict warfarin dose [7, 10, 11], but those studies use a single machine learning algorithm to maximize the accuracy of predicting warfarin dose.
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