The Association between Pre-operative Pectoralis Muscle Quantity and Outcomes after Cardiac Transplantation.

Sarcopenia cardiac transplantation outcomes

Journal

Journal of cardiac failure
ISSN: 1532-8414
Titre abrégé: J Card Fail
Pays: United States
ID NLM: 9442138

Informations de publication

Date de publication:
12 Apr 2024
Historique:
received: 05 12 2023
revised: 01 03 2024
accepted: 04 03 2024
medline: 15 4 2024
pubmed: 15 4 2024
entrez: 14 4 2024
Statut: aheadofprint

Résumé

Sarcopenia is underappreciated in advanced heart failure and is not routinely assessed. In patients receiving a left ventricular assist device (LVAD), preoperative sarcopenia, defined using CT-derived pectoralis muscle area index (muscle area indexed to body surface area), is an independent predictor of post-operative mortality. The association between preoperative sarcopenia and outcomes after heart transplant (HT) is unknown. The primary aim was to determine if preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of days alive and out of the hospital (DAOH) post-transplant. Patients who underwent HT from January 2018 to June 2022 with available preoperative chest CT scans were included. Sarcopenia was diagnosed as pectoralis muscle area index in the lowest sex-specific tertile. The primary endpoint was DAOH at 1-year post-transplant. 169 patients were included. Patients with sarcopenia (n=55) had fewer DAOH compared to those without, with a median difference of 17 days (320 vs. 337 days, p=0.004). Patients with sarcopenia had a longer index hospitalization and were also more likely to be discharged to a facility other than home. In a Poisson regression model, sarcopenia was a significant univariable and the strongest multivariable predictor of DAOH at 1-year (Parameter estimate = -0.17, 95% CI -0.19 to -14, p = <0.0001). Preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of poor outcomes after HT. This parameter is easily measurable from commonly obtained preoperative CT scans and may be considered in the transplant evaluation.

Sections du résumé

BACKGROUND BACKGROUND
Sarcopenia is underappreciated in advanced heart failure and is not routinely assessed. In patients receiving a left ventricular assist device (LVAD), preoperative sarcopenia, defined using CT-derived pectoralis muscle area index (muscle area indexed to body surface area), is an independent predictor of post-operative mortality. The association between preoperative sarcopenia and outcomes after heart transplant (HT) is unknown.
OBJECTIVES OBJECTIVE
The primary aim was to determine if preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of days alive and out of the hospital (DAOH) post-transplant.
METHODS METHODS
Patients who underwent HT from January 2018 to June 2022 with available preoperative chest CT scans were included. Sarcopenia was diagnosed as pectoralis muscle area index in the lowest sex-specific tertile. The primary endpoint was DAOH at 1-year post-transplant.
RESULTS RESULTS
169 patients were included. Patients with sarcopenia (n=55) had fewer DAOH compared to those without, with a median difference of 17 days (320 vs. 337 days, p=0.004). Patients with sarcopenia had a longer index hospitalization and were also more likely to be discharged to a facility other than home. In a Poisson regression model, sarcopenia was a significant univariable and the strongest multivariable predictor of DAOH at 1-year (Parameter estimate = -0.17, 95% CI -0.19 to -14, p = <0.0001).
CONCLUSIONS CONCLUSIONS
Preoperative sarcopenia, diagnosed using pectoralis muscle area index, is an independent predictor of poor outcomes after HT. This parameter is easily measurable from commonly obtained preoperative CT scans and may be considered in the transplant evaluation.

Identifiants

pubmed: 38616005
pii: S1071-9164(24)00118-0
doi: 10.1016/j.cardfail.2024.03.012
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of competing interest There were no outside sources of funding for this study.

Auteurs

Elissa Driggin (E)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY.

Alice Chung (A)

Department of Medicine, Columbia University Irving Medical Center- NewYork-Presbyterian Hospital, New York, NY.

Erin Harris (E)

Department of Medicine, Columbia University Irving Medical Center- NewYork-Presbyterian Hospital, New York, NY.

Abraham Bordon (A)

Department of Radiology, Columbia University Irving Medical Center- NewYork-Presbyterian Hospital, New York, NY.

Salwa Rahman (S)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY.

Gabriel Sayer (G)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY.

Koji Takeda (K)

Division of Cardiothoracic and Vascular Surgery, Department of Surgery, Columbia University Medical Center- NewYork-Presbyterian Hospital, New York, NY.

Nir Uriel (N)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY.

Mathew S Maurer (MS)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY.

Jay Leb (J)

Department of Radiology, Columbia University Irving Medical Center- NewYork-Presbyterian Hospital, New York, NY.

Kevin Clerkin (K)

Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center-NewYork-Presbyterian Hospital, New York, NY. Electronic address: kjc2142@cumc.columbia.edu.

Classifications MeSH