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Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

Author

Listed:
  • Andreas D. Meid

    (Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany)

  • Lucas Wirbka

    (Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany)

Abstract

Background: Decision making for the “best†treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC). Methods: In German claims data for the calendar years 2014–2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation. Results: A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: −0.78 % [−1.40; −0.03]). Conclusions: If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients’ complexity deviates from “typical†study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making. Highlights It was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly. ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding. When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option. Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

Suggested Citation

  • Andreas D. Meid & Lucas Wirbka, 2022. "Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants," Medical Decision Making, , vol. 42(5), pages 587-598, July.
  • Handle: RePEc:sae:medema:v:42:y:2022:i:5:p:587-598
    DOI: 10.1177/0272989X211064604
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    References listed on IDEAS

    as
    1. Lucas Wirbka & Walter E Haefeli & Andreas D Meid, 2020. "A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-12, May.
    2. Paul K. J. Han & Tania D. Strout & Caitlin Gutheil & Carl Germann & Brian King & Eirik Ofstad & PÃ¥l Gulbrandsen & Robert Trowbridge, 2021. "How Physicians Manage Medical Uncertainty: A Qualitative Study and Conceptual Taxonomy," Medical Decision Making, , vol. 41(3), pages 275-291, April.
    3. Erika A. Waters & Julia Maki & Ying Liu & Nicole Ackermann & Chelsey R. Carter & Hank Dart & Deborah J. Bowen & Linda D. Cameron & Graham A. Colditz, 2021. "Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases," Medical Decision Making, , vol. 41(1), pages 74-88, January.
    4. Gal Dinstag & David Amar & Erik Ingelsson & Euan Ashley & Ron Shamir, 2019. "Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-12, August.
    5. Carissa Bonner & Lyndal J. Trevena & Wolfgang Gaissmaier & Paul K. J. Han & Yasmina Okan & Elissa Ozanne & Ellen Peters & Daniëlle Timmermans & Brian J. Zikmund-Fisher, 2021. "Current Best Practice for Presenting Probabilities in Patient Decision Aids: Fundamental Principles," Medical Decision Making, , vol. 41(7), pages 821-833, October.
    6. Jakub Z Qazi & Mireille E Schnitzer & Robert Côté & Marie-Josée Martel & Marc Dorais & Sylvie Perreault, 2021. "Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
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