CT-derived skeletal muscle predicts lung transplant approval.

Authors

Alexa Lavergne, Brandi Bottiger, Mohamed Sobhi Jabal, Tommi Jarvinen, John M Reynolds, Mustafa R Bashir, Michael Rosenthal, Jacob Klapper, Kirti Magudia

Published in

JHLT open. Volume 14. Pages 100657. Epub Aug 10, 2026.

Abstract

Lung transplant candidate selection relies on assessment of physiologic reserve, with body mass index commonly used to guide eligibility despite its limitations in distinguishing muscle from adipose tissue. CT-derived body composition analysis enables precise quantification of skeletal muscle and fat compartments. This study evaluates the association between CT-derived body composition metrics and transplant committee decisions.
We performed a retrospective cohort study of 704 adult lung transplant candidates evaluated from 2019 to 2024 with available preoperative abdominal CT imaging at a single academic institution. CT scans were analyzed using an automated deep learning workflow to quantify skeletal muscle, visceral fat, and subcutaneous fat at the third lumbar vertebral level. Metrics were normalized by age, sex, and race reference values. Patients were categorized as approved or denied using transplant committee decisions. Multivariable logistic regression with likelihood ratio testing was used to identify independent predictors of approval.
Of 704 patients, 437 (62%) were approved and 267 (38%) were denied. Greater skeletal muscle area was independently associated with increased odds of transplant approval (per 50 cm²: OR 2.969, 95% CI 1.728-5.321, p<0.001). Visceral fat, subcutaneous fat, and body mass index category were not associated with approval. Skeletal muscle was lower in patients labeled as frail/deconditioned and positively associated with 6-minute walk distance (p<0.001).
CT-derived skeletal muscle is independently associated with lung transplant approval and may provide an objective marker of physiologic reserve that is not captured by body mass index, with potential to improve risk stratification in transplant candidate selection.

PMID:
42732190
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

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