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Analytic morphomics identifies predictors of new‐onset diabetes after liver transplantation
Author(s) -
Vaughn Valerie M.,
Cron David C.,
Terjimanian Michael N.,
Gala Zachary S.,
Wang Stewart C.,
Su Grace L.,
Volk Michael L.
Publication year - 2015
Publication title -
clinical transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 76
eISSN - 1399-0012
pISSN - 0902-0063
DOI - 10.1111/ctr.12537
Subject(s) - medicine , immunosuppression , liver transplantation , transplantation , diabetes mellitus , incidence (geometry) , type 2 diabetes , gastroenterology , surgery , endocrinology , physics , optics
Abstract Among liver transplant recipients, development of post‐transplant complications such as new‐onset diabetes after transplantation ( NODAT ) is common and highly morbid. Current methods of predicting patient risk are inaccurate in the pre‐transplant period, making implementation of targeted therapies difficult. We sought to determine whether analytic morphomics (using computed tomography scans) could be used to predict the incidence of NODAT . We analyzed peri‐transplant scans from 216 patients with varying indications for liver transplantation, among whom 61 (28%) developed NODAT . Combinations of visceral fat, subcutaneous fat, and psoas area were considered in addition to traditional risk factors. On multivariate analysis adjusting for usual risk factors such as type of immunosuppression, subcutaneous fat thickness remained significantly associated with NODAT ( OR = 1.43, 95% CI 1.00–1.88, p = 0.047). Subgroup analysis showed that patients with later‐onset of NODAT had higher visceral fat, whereas subcutaneous fat thickness was more correlated with earlier‐onset of NODAT (using 10 months post‐transplant as the cut‐off). Conclusion Analytic morphomics may be used to help assess NODAT risk in patients undergoing liver transplantation.