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Bayesian Machine Learning Model Guiding Iterative, Personalized Anticoagulant Dosing Decision-Making: ENGAGE AF-TIMI

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  • 1Baim Institute for Clinical Research, Harvard Medical School, Boston, Massachusetts, USA.

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|February 26, 2026
PubMed
Summary
This summary is machine-generated.

A new Bayesian machine learning model, Adele, offers personalized anticoagulation for atrial fibrillation patients by predicting risks and optimizing edoxaban dosing based on individual preferences for stroke, bleeding, and death. This approach improves risk prediction accuracy compared to standard methods.

Keywords:
artificial intelligenceatrial fibrillationdirect oral anticoagulantedoxabanpatient centricitypharmacokinetics

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Area of Science:

  • Cardiovascular Medicine
  • Machine Learning in Healthcare
  • Pharmacometrics

Background:

  • Current anticoagulation for atrial fibrillation uses fixed dosing, neglecting patient-specific preferences for risks like stroke, bleeding, and death.
  • Individualized treatment strategies are needed to optimize anticoagulation therapy in atrial fibrillation.

Purpose of the Study:

  • To develop and evaluate Adele, a Bayesian machine learning model for individualized, long-term anticoagulation dosing in atrial fibrillation patients.
  • To incorporate patient preferences for risks of stroke, bleeding, and death into dosing decisions.

Main Methods:

  • Developed Adele, a Bayesian competing-risk, multistate hazard model, trained on 5,380 edoxaban-treated atrial fibrillation patients from the ENGAGE AF-TIMI 48 trial.
  • Utilized patient pharmacokinetic (PK) and baseline data for personalized, continuous risk prediction.
  • Compared Adele's predictive accuracy to standard Kaplan-Meier estimators using concordance index.

Main Results:

  • Adele demonstrated superior predictive accuracy over Kaplan-Meier estimators, improving 3-year predictions for cardiovascular death (+12.1%), disability (+11.8%), major gastrointestinal bleeding (+13.7%), and ischemic stroke (+3.3%).
  • The model showed dynamic risk prediction, adjusting event probabilities after events like intracranial hemorrhage or ischemic stroke.
  • Patient examples illustrated Adele's ability to identify optimal edoxaban doses (15mg, 30mg, 60mg) based on varied hypothetical outcome preferences.

Conclusions:

  • Adele is an advanced Bayesian framework integrating clinical factors and PK data for adaptive, patient-centered, preference-weighted predictions.
  • This model enables personalized anticoagulation management by dynamically adjusting predictions and guiding optimal dosing.