Effect of Carvedilol vs Metoprolol on Atrial and Ventricular Arrhythmias Among Implantable Cardioverter-Defibrillator Recipients.


Journal

JACC. Clinical electrophysiology
ISSN: 2405-5018
Titre abrégé: JACC Clin Electrophysiol
Pays: United States
ID NLM: 101656995

Informations de publication

Date de publication:
10 2023
Historique:
received: 04 11 2022
revised: 17 05 2023
accepted: 07 06 2023
medline: 27 10 2023
pubmed: 1 9 2023
entrez: 1 9 2023
Statut: ppublish

Résumé

Both selective and nonselective beta-blockers are used to treat patients with heart failure (HF). However, the data on the association of beta-blocker type with risk of atrial arrhythmia and ventricular arrhythmia (VA) in HF patients with a primary prevention implantable cardioverter-defibrillator (ICD) are limited. This study sought to evaluate the effect of metoprolol vs carvedilol on the risk of atrial tachyarrhythmia (ATA) and VA in HF patients with an ICD. This study pooled primary prevention ICD recipients from 5 landmark ICD trials (MADIT-II, MADIT-CRT, MADIT-RIT, MADIT-RISK, and RAID). Fine and Gray multivariate regression models, stratified by study, were used to evaluate the risk of ATA, inappropriate ICD shocks, and fast VA (defined as ventricular tachycardia ≥200 beats/min or ventricular fibrillation) by beta-blocker type. Among 4,194 patients, 2,920 (70%) were prescribed carvedilol and 1,274 (30%) metoprolol. The cumulative incidence of ATA at 3.5 years was 11% in patients treated with carvedilol vs 15% in patients taking metoprolol (P = 0.003). Multivariate analysis showed that carvedilol treatment was associated with a 35% reduction in the risk of ATA (HR: 0.65; 95% CI: 0.53-0.81; P < 0.001) when compared to metoprolol, and with a corresponding 35% reduction in the risk of inappropriate ICD shocks (HR: 0.65; 95% CI: 0.47-0.89; P = 0.008). Carvedilol vs metoprolol was also associated with a 16% reduction in the risk of fast VA. However, these findings did not reach statistical significance (HR: 0.84; 95% CI: 0.70-1.02; P = 0.085). These findings suggests that HF patients with ICDs on carvedilol treatment experience a significantly lower risk of ATA and inappropriate ICD shocks when compared to treatment with metoprolol.

Sections du résumé

BACKGROUND
Both selective and nonselective beta-blockers are used to treat patients with heart failure (HF). However, the data on the association of beta-blocker type with risk of atrial arrhythmia and ventricular arrhythmia (VA) in HF patients with a primary prevention implantable cardioverter-defibrillator (ICD) are limited.
OBJECTIVES
This study sought to evaluate the effect of metoprolol vs carvedilol on the risk of atrial tachyarrhythmia (ATA) and VA in HF patients with an ICD.
METHODS
This study pooled primary prevention ICD recipients from 5 landmark ICD trials (MADIT-II, MADIT-CRT, MADIT-RIT, MADIT-RISK, and RAID). Fine and Gray multivariate regression models, stratified by study, were used to evaluate the risk of ATA, inappropriate ICD shocks, and fast VA (defined as ventricular tachycardia ≥200 beats/min or ventricular fibrillation) by beta-blocker type.
RESULTS
Among 4,194 patients, 2,920 (70%) were prescribed carvedilol and 1,274 (30%) metoprolol. The cumulative incidence of ATA at 3.5 years was 11% in patients treated with carvedilol vs 15% in patients taking metoprolol (P = 0.003). Multivariate analysis showed that carvedilol treatment was associated with a 35% reduction in the risk of ATA (HR: 0.65; 95% CI: 0.53-0.81; P < 0.001) when compared to metoprolol, and with a corresponding 35% reduction in the risk of inappropriate ICD shocks (HR: 0.65; 95% CI: 0.47-0.89; P = 0.008). Carvedilol vs metoprolol was also associated with a 16% reduction in the risk of fast VA. However, these findings did not reach statistical significance (HR: 0.84; 95% CI: 0.70-1.02; P = 0.085).
CONCLUSIONS
These findings suggests that HF patients with ICDs on carvedilol treatment experience a significantly lower risk of ATA and inappropriate ICD shocks when compared to treatment with metoprolol.

Identifiants

pubmed: 37656097
pii: S2405-500X(23)00444-9
doi: 10.1016/j.jacep.2023.06.009
pii:
doi:

Substances chimiques

Metoprolol GEB06NHM23
Carvedilol 0K47UL67F2
Adrenergic beta-Antagonists 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2122-2131

Subventions

Organisme : NHLBI NIH HHS
ID : U01 HL096607
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL096610
Pays : United States

Informations de copyright

Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Funding Support and Author Disclosures Each of the MADIT trials were funded by an unrestricted research grant from Boston Scientific to the University of Rochester Medical Center. The MADIT-RISK study was supported by the National Heart, Lung, and Blood Institute grant HL077478. The RAID trial was supported by National Heart, Lung, and Blood Institute grants UO1 HL096607 and UO1 HL096610 and by Gilead Sciences Inc grant IN-US-259-0125. Dr Goldenberg has received research grants from Boston Scientific, ZOLL Medical Corporation, Medtronic, Biosense-Webster, and Biotronik. Dr Kutyifa has received research grants from Boston Scientific, Biotronik, and ZOLL Medical Corporation, and Spire Inc; and has consulting agreements with ZOLL Medical Corporation and Biotronik. Dr Steinberg has received research grants from the National Institutes of Health, Medtronic, AliveCor, and Atricure. Dr Zareba has received research grants from Boston Scientific. Dr Aktas has received research grants from Boston Scientific, Abbott, and Medtronic. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Auteurs

Alexander Diamond (A)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Ilan Goldenberg (I)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Arwa Younis (A)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Ido Goldenberg (I)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Ramya Sampath (R)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Valentina Kutyifa (V)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Anita Y Chen (AY)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Scott McNitt (S)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Bronislava Polonsky (B)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Jonathan S Steinberg (JS)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Wojciech Zareba (W)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.

Mehmet K Aktaş (MK)

University of Rochester Medical Center, Clinical Cardiovascular Research Center, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA. Electronic address: Mehmet_Aktas@urmc.rochester.edu.

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Classifications MeSH