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Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

Che Ngufor1, Nan Zhang2, Holly K Van Houten2

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Summary

Machine learning identifies patients benefiting from left atrial appendage occlusion (LAAO) over direct oral anticoagulants (DOACs). This aids clinical decisions for optimal stroke prevention in atrial fibrillation patients.

Keywords:
atrial fibrillationleft atrial appendage closuremachine learningoral anticoagulant

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Transcatheter left atrial appendage occlusion (LAAO) offers an alternative to long-term anticoagulation for atrial fibrillation.
  • Selecting appropriate patients for LAAO versus direct oral anticoagulants (DOACs) remains a clinical challenge.

Purpose of the Study:

  • To develop and apply a novel causal machine learning framework.
  • To identify patients who would specifically benefit from LAAO compared to DOAC therapy.

Main Methods:

  • Utilized a large dataset (744,190 patients) from the OptumLabs Data Warehouse (March 2015-December 2019).
  • Employed one-to-one propensity score matching on 107 baseline characteristics to create a balanced cohort.
  • Applied a causal forest model to estimate heterogeneous treatment effects for a composite outcome (stroke, embolism, bleeding, mortality).

Main Results:

  • In the matched cohort (28,930 patients), LAAO showed no early difference but a lower risk of the composite outcome at 2 years (ATE -2.9%).
  • At 2 years, 30.1% of patients were predicted to benefit from LAAO, 69.7% were neutral, and 1.4% potentially harmed.
  • The average CHA₂DS₂-VASc score was 5.8, with a mean age of 76.8 years.

Conclusions:

  • Developed novel machine learning algorithms to predict LAAO benefit over DOACs.
  • This predictive capability can enhance clinical decision-making for patient referral and treatment selection.
  • Supports personalized medicine approaches in atrial fibrillation management.