Evidence map›Paper›PMID 41123698›Full record

ArticleLangenbeck's archives of surgery2025

AI-based body composition score predicts survival after liver transplantation.

Eugen Malamutmann, Friederike Roehrborn, Ksenia Vershinina, Sven Koitka, Derar Jaradat, Sophia M Schmitz, Johannes Haubold, Ulf P Neumann, Felix Nensa, Arzu Oezcelik

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Article in Langenbeck's archives of surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Eugen MalamutmannDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Friederike RoehrbornDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Ksenia VershininaDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Sven KoitkaInstitute of Artificial Intelligence, University Medicine Essen, Essen, Germany.
Derar JaradatDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Sophia M SchmitzDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Johannes HauboldInstitute of Artificial Intelligence, University Medicine Essen, Essen, Germany.
Ulf P NeumannDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
Felix NensaInstitute of Artificial Intelligence, University Medicine Essen, Essen, Germany.
Arzu OezcelikDepartment of General, Visceral and Transplantation Surgery, University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany. arzu.oezcelik@uk-essen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBody composition has a significant role to predict survival in patients with malignant disease. This study evaluates the importance of body composition for predicting short- and long-term survival after liver transplantation. Additionally, the sex specific differences will be evaluated.

methodsBody composition, of all patients who underwent liver transplantation between January 2011 and December 2023 with computed tomography prior liver transplantation, was assessed fully automated with AI based technique. Pre-, intra- and postoperative data were retrospectively reported. Uni- and multivariate regression analyses was performed to identify independent prognostic factors for survival. The statistical analyses was performed separately for male and female with comparison of the both groups.

resultsThere were 346 patients (60.1%male, 39.9%female) with median age of 52.2 years (SD 10.8) included to the study. The univariate and multivariate cox regression analyses have identified the ratio of the subcutaneous fat volume to muscle volume as well as the ratio of the visceral fat volume to muscle volume as significant prognostic parameter for the overall survival. The separate analyses of the two groups show that these factors predict survival in male and female. However, visceral fat and also the ratio of FVM is significantly higher in male.

conclusionBased on the results of our study we can conclude that the ratio of visceral fat volume to muscle volume (FVM-ratio) has an essential impact on overall survival after liver transplantation in male and female patients. The fully automated AI based assessment is fast, accurate and investigator independent.

Indexed as

Body CompositionLiver TransplantationAdultAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesSex FactorsSurvival RateTomography, X-Ray ComputedBody compositionFrailtyLiver transplantationSarcopenia

Identifiers

PMID41123698
PMCPMC12546405

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.