Evaluating the Effect of Longitudinal Dose and INR Data on Maintenance Warfarin Dose Predictions

Warfarin, a commonly prescribed drug to prevent blood clots, has a highly variable individual response. Determining a maintenance warfarin dose that achieves a therapeutic blood clotting time, as measured by the international normalized ratio (INR), is crucial in preventing complications. Machine learning algorithms are increasingly being used for warfarin dosing; usually, an initial dose is predicted with clinical and genotype factors, and revised after a few days based on previous doses and current INR. Since sequential dose and INR better capture individual differences in warfarin response, we hypothesized that longitudinal dose response data will improve maintenance dose predictions. To test this hypothesis, we analyzed a dataset from the COAG warfarin dosing clinical trial, which includes clinical data, warfarin dosing history and INR measurements over the study period, and maintenance dose when therapeutic INR was achieved. Various regression models were trained to predict the maintenance warfarin dose using clinical factors, warfarin dosing history and INR data. Dose revision algorithms that used a single dose and INR measurement achieved comparable performance as the baseline revision algorithm. In contrast, dose revision algorithms that used longitudinal dose and INR data predicted doses that were statistically significantly much closer to the true maintenance dose. With the best performing model, gradient boosting (GB), the proportion of ideal estimated dose, i.e., within ±20% of the true dose, increased from baseline (54.92%) to that with GB with single (63.11%) and longitudinal (75.41%) INR. More accurate maintenance dose predictions with longitudinal dose response data can potentially achieve therapeutic INR faster, reduce drug-related complications and improve patient outcomes with warfarin therapy.