Applying machine learning tools to personalize dabigatran treatment decisions

Applying machine learning tools to personalize dabigatran treatment decisions

NIH

Atrial fibrillation—a condition in which the top of the heart beats irregularly—affects 35.5 million people worldwide. People with atrial fibrillation experience a heightened risk of stroke and are therefore often prescribed “blood thinners”, or anti-clot medications. Unfortunately, the most commonly-used drug called warfarin presents risks and burdens to patients, including the need for laboratory monitoring, and interactions with many other medications. The newer medication dabigatran does not pose these limitations. In the RELY trial, dabigatran 150 mg twice daily was superior to warfarin in preventing a stroke or major clot, while dabigatran 110 mg twice daily was similar to warfarin for these outcomes. Both doses greatly reduced strokes caused by bleeding in the brain, and dabigatran 110 mg twice daily significantly reduced major bleeding anywhere in the body in comparison with warfarin. The investigators of RELY commented that because the risk of clot was lower with the 150-mg dose, but the risk of major bleeding was lower with the 100mg dose, it may be possible to tailor the choice of dosage to an individual patient, based on their unique risk of clotting or bleeding. There were variations in the absolute risk reduction in clotting and absolute risk increase in bleeding within each group of participants on dabigatran in RELY and the subsequent RELY-ABLE trial. Traditional statistical analyses (called univariate subgroup analyses) have low power for detecting what features contribute to greater benefit or greater harm from a medication, but newer “machine learning” methods have greater power for accomplishing this aim. Our objective is to create a personalized dabigatran treatment decision tool, which is a calculator that estimates the personalized estimated reduction in risk in stroke or major clot, and increase in risk of major bleeding, from either dose of dabigatran. The tool would be developed using a novel machine learning method called gradient forest analysis, which analyzes participant data from trials to assess how multiple combinations of patient features predict benefit or harm from a drug. Our research will help improve patient care by assisting clinicians to determine the potential risks and benefits of dabigatran, and optimal dose of dabigatran, before prescribing it. We have chosen the gradient forest method because it has been shown to have benefits over previous methods, particularly reducing the rate of wrong conclusions (“false positive” findings), accounting for complex interactions between multiple factors (e.g., a patient who is both lower weight and of South Asian ethnicity may have more benefit than a patient with just one or the other feature), and producing correct (“unbiased”) estimates of the change in the probability of an event on the drug. The findings will be interpreted using a pre-specified protocol for assessing significance of the results, and communicated through a peer-reviewed journal.