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

BACKGROUND

Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong anticoagulation, but optimal patient selection remains challenging.

OBJECTIVES

This study sought to apply a novel causal machine learning framework to identify patients who would benefit from LAAO vs a direct oral anticoagulant (DOAC).

METHODS

We identified 744,190 adult patients with atrial fibrillation treated with either LAAO or DOAC between March 13, 2015, and December 31, 2019, using data from OptumLabs Data Warehouse. One-to-one propensity score matching was used to create a cohort where patients were similar in 107 baseline characteristics. A causal forest model was used to estimate the heterogeneous treatment effect for a composite outcome of ischemic stroke, systemic embolism, major bleeding, and all-cause mortality.

RESULTS

In the matched cohort of 28,930 patients, the mean age was 76.8 ± 6.3 years; 5,818 patients (40%) were female, and the mean CHA2DS2-VASc score was 5.8. LAAO was associated with no difference with the primary composite outcome in comparison to NOAC early on (average treatment effect of -0.68% [-1.4%, 0.06%] at 1 year), but a lower risk at the end of 2 years (average treatment effect of -2.9% [-3.7%, -2.0%]). At the end of 2 years, 30.1% of the overall cohort were classified as potentially benefiting from LAAO, 69.7% were classified as neutral, and 1.4% were potentially harmed by LAAO.

CONCLUSIONS

Novel machine learning algorithms were developed to identify patients who are more likely to benefit from LAAO vs DOACs. This information can support clinical decision-making to determine which patients should be referred to subspecialists for further examination and discussion of LAAO.