Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion

Author:

Faridi Kamil F.12ORCID,Ong Emily L.1ORCID,Zimmerman Sarah2ORCID,Varosy Paul D.3,Friedman Daniel J.4ORCID,Hsu Jonathan C.5ORCID,Kusumoto Fred6ORCID,Mortazavi Bobak J.7ORCID,Minges Karl E.2ORCID,Pereira Lucy2,Lakkireddy Dhanunjaya8ORCID,Koutras Christina9,Denton Beth9,Mobayed Julie9,Curtis Jeptha P.12ORCID,Freeman James V.12ORCID

Affiliation:

1. Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine (K.F.F., E.L.O., J.P.C., J.V.F.).

2. Center for Outcomes Research and Evaluation, Yale New Haven Health, CT (K.F.F., S.Z., K.E.M., L.P., J.P.C., J.V.F.).

3. Cardiology Section, VA Eastern Colorado Health Care System, Aurora, (P.D.V.).

4. Electrophysiology Section, Duke University School of Medicine, Durham, NC (D.J.F.).

5. Cardiac Electrophysiology Section, Division of Cardiology, University of California San Diego Health System, La Jolla (J.C.H.).

6. Department of Cardiovascular Disease, Mayo Clinic, Jacksonville, FL (F.K.).

7. Department of Computer Science and Engineering, Texas A&M University, College Station (B.J.M.).

8. Kansas City Heart Rhythm Institute, Overland Park (D.L.).

9. American College of Cardiology, Washington, DC (C.K., B.D., J.M.).

Abstract

BACKGROUND: The National Cardiovascular Data Registry Left Atrial Appendage Occlusion Registry (LAAO) includes the vast majority of transcatheter LAAO procedures performed in the United States. The objective of this study was to develop a model predicting adverse events among patients undergoing LAAO with Watchman FLX. METHODS: Data from 41 001 LAAO procedures with Watchman FLX from July 2020 to September 2021 were used to develop and validate a model predicting in-hospital major adverse events. Randomly selected development (70%, n=28 530) and validation (30%, n=12 471) cohorts were analyzed with 1000 bootstrapped samples, using forward stepwise logistic regression to create the final model. A simplified bedside risk score was also developed using this model. RESULTS: Increased age, female sex, low preprocedure hemoglobin, no prior attempt at atrial fibrillation termination, and increased fall risk most strongly predicted in-hospital major adverse events and were included in the final model along with other clinically relevant variables. The median in-hospital risk-standardized adverse event rate was 1.50% (range, 1.03%–2.84%; interquartile range, 1.42%–1.64%). The model demonstrated moderate discrimination (development C-index, 0.67 [95% CI, 0.65–0.70] and validation C-index, 0.66 [95% CI, 0.62–0.70]) with good calibration. The simplified risk score was well calibrated with risk of in-hospital major adverse events ranging from 0.26% to 3.90% for a score of 0 to 8, respectively. CONCLUSIONS: A transcatheter LAAO risk model using National Cardiovascular Data Registry and LAAO Registry data can predict in-hospital major adverse events, demonstrated consistency across hospitals and can be used for quality improvement efforts. A simple bedside risk score was similarly predictive and may inform shared decision-making.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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