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

atrial appendage atrial fibrillation ischemic stroke quality improvement risk factors

Journal

Circulation. Arrhythmia and electrophysiology
ISSN: 1941-3084
Titre abrégé: Circ Arrhythm Electrophysiol
Pays: United States
ID NLM: 101474365

Informations de publication

Date de publication:
23 Feb 2024
Historique:
medline: 23 2 2024
pubmed: 23 2 2024
entrez: 23 2 2024
Statut: aheadofprint

Résumé

The National Cardiovascular Data Registry and Left Atrial Appendage Occlusion (LAAO) registry 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. 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. 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. 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.

Sections du résumé

BACKGROUND UNASSIGNED
The National Cardiovascular Data Registry and Left Atrial Appendage Occlusion (LAAO) registry 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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 38390713
doi: 10.1161/CIRCEP.123.012424
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e012424

Auteurs

Kamil F Faridi (KF)

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

Emily L Ong (EL)

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

Sarah Zimmerman (S)

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.).

Paul D Varosy (PD)

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

Daniel J Friedman (DJ)

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

Jonathan C Hsu (JC)

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

Fred Kusumoto (F)

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

Bobak J Mortazavi (BJ)

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

Karl E Minges (KE)

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.).

Lucy Pereira (L)

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.).

Dhanunjaya Lakkireddy (D)

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

Christina Koutras (C)

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

Beth Denton (B)

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

Julie Mobayed (J)

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

Jeptha P Curtis (JP)

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

James V Freeman (JV)

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

Classifications MeSH