Related Experiment Video
Updated: Jul 31, 2026
The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation
Published on: February 28, 2012
Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation
Che Ngufor1, Nan Zhang2, Holly K Van Houten2
1Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
Insights
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.
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.
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.
More Related Videos
28:13Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
Published on: February 26, 2013
08:10Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
