Sarcopenia and Frailty in Patients Undergoing Transcatheter Aortic Valve Replacement.

Frailty aortic stenosis computed tomography low muscle mass skeletal muscle mass transcatheter aortic valve disease

Journal

American heart journal
ISSN: 1097-6744
Titre abrégé: Am Heart J
Pays: United States
ID NLM: 0370465

Informations de publication

Date de publication:
18 Jul 2024
Historique:
received: 14 06 2024
revised: 14 07 2024
accepted: 15 07 2024
medline: 21 7 2024
pubmed: 21 7 2024
entrez: 20 7 2024
Statut: aheadofprint

Résumé

Skeletal muscle mass (SMM) plays a crucial role in risk assessment in transcatheter aortic valve replacement (TAVR) candidates, yet it remains underutilized. Traditional methods focus on weakness or performance but omit SMM. This study compared traditional and novel markers of sarcopenia and frailty in terms of their ability to predict adverse outcomes post-TAVR. Three risk models were evaluated for the composite outcome of perioperative complications, 1-year rehospitalization, or 1-year mortality: (1) sarcopenia by combining low muscle mass (LMM) and weakness/performance assessed by hand grip strength or gait speed; (2) frailty by an Adapted Green score; and (3) frailty by the Green-SMI score incorporating LMM by multi-level opportunistic pre-TAVR thoracic CT segmentation. In this study we included 184 eligible patients from January to December of 2018, (96.7%) of which were balloon expandable valves. The three risk models identified 22.8% patients as sarcopenic, 63.6% as frail by the Adapted Green score, and 53.8% as frail by the Green-SMI score. There were higher rates of the composite outcome in patients with sarcopenia (54.8%) and frailty (41.9% with the Adapted Green and 50.5% with the Green-SMI score) compared to their non-sarcopenic (30.3%) and non-frail counterparts (25.4% with the Adapted Green and 18.8% with the Green-SMI score). Sarcopenia and frailty by Green-SMI, but not by the Adapted Green, were associated with higher risks of the composite outcome on multivariable adjustment (HR 2.2 [95% CI: 1.25-4.02], p=0.007 and HR 3.4 [95% CI: 1.75-6.65], p<0.001, respectively). The integration of pre-operative CT-based SMM to a frailty score significantly improves the prediction of adverse outcomes in patients undergoing TAVR.

Sections du résumé

BACKGROUND BACKGROUND
Skeletal muscle mass (SMM) plays a crucial role in risk assessment in transcatheter aortic valve replacement (TAVR) candidates, yet it remains underutilized. Traditional methods focus on weakness or performance but omit SMM. This study compared traditional and novel markers of sarcopenia and frailty in terms of their ability to predict adverse outcomes post-TAVR.
METHODS METHODS
Three risk models were evaluated for the composite outcome of perioperative complications, 1-year rehospitalization, or 1-year mortality: (1) sarcopenia by combining low muscle mass (LMM) and weakness/performance assessed by hand grip strength or gait speed; (2) frailty by an Adapted Green score; and (3) frailty by the Green-SMI score incorporating LMM by multi-level opportunistic pre-TAVR thoracic CT segmentation.
RESULTS RESULTS
In this study we included 184 eligible patients from January to December of 2018, (96.7%) of which were balloon expandable valves. The three risk models identified 22.8% patients as sarcopenic, 63.6% as frail by the Adapted Green score, and 53.8% as frail by the Green-SMI score. There were higher rates of the composite outcome in patients with sarcopenia (54.8%) and frailty (41.9% with the Adapted Green and 50.5% with the Green-SMI score) compared to their non-sarcopenic (30.3%) and non-frail counterparts (25.4% with the Adapted Green and 18.8% with the Green-SMI score). Sarcopenia and frailty by Green-SMI, but not by the Adapted Green, were associated with higher risks of the composite outcome on multivariable adjustment (HR 2.2 [95% CI: 1.25-4.02], p=0.007 and HR 3.4 [95% CI: 1.75-6.65], p<0.001, respectively).
CONCLUSIONS CONCLUSIONS
The integration of pre-operative CT-based SMM to a frailty score significantly improves the prediction of adverse outcomes in patients undergoing TAVR.

Identifiants

pubmed: 39032584
pii: S0002-8703(24)00173-X
doi: 10.1016/j.ahj.2024.07.007
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Ian Persits (I)

Department of Internal Medicine, Cleveland Clinic, Cleveland, OH.

Saeid Mirzai (S)

Department of Internal Medicine, Cleveland Clinic, Cleveland, OH; Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC.

Kunaal S Sarnaik (KS)

Case Western Reserve University School of Medicine, Cleveland, OH.

Maximilian C Volk (MC)

Department of Internal Medicine, Cleveland Clinic, Cleveland, OH.

James Yun (J)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

Serge Harb (S)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

Rishi Puri (R)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

Samir Kapadia (S)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

Amar Krishnaswamy (A)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

Po-Hao Chen (PH)

Section of Musculoskeletal Imaging, Diagnostics Institute, Cleveland Clinic, Cleveland, OH.

Grant Reed (G)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH.

W H Wilson Tang (WHW)

Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH. Electronic address: tangw@ccf.org.

Classifications MeSH