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Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning

Diabetes mellitus (DM) significantly impacts long‐term survival after liver transplantation (LT). We identified survival factors for LT recipients who had DM to inform preventive care using machine‐learning analysis. We analyzed risk factors for mortality in patients from across the United States us...

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Autores principales: Yasodhara, Angeline, Dong, Victor, Azhie, Amirhossein, Goldenberg, Anna, Bhat, Mamatha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248095/
https://www.ncbi.nlm.nih.gov/pubmed/33113221
http://dx.doi.org/10.1002/lt.25930
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author Yasodhara, Angeline
Dong, Victor
Azhie, Amirhossein
Goldenberg, Anna
Bhat, Mamatha
author_facet Yasodhara, Angeline
Dong, Victor
Azhie, Amirhossein
Goldenberg, Anna
Bhat, Mamatha
author_sort Yasodhara, Angeline
collection PubMed
description Diabetes mellitus (DM) significantly impacts long‐term survival after liver transplantation (LT). We identified survival factors for LT recipients who had DM to inform preventive care using machine‐learning analysis. We analyzed risk factors for mortality in patients from across the United States using the Scientific Registry of Transplant Recipients (SRTR). Patients had undergone LT from 1987 to 2019, with a follow‐up of 6.47 years (standard deviation [SD] 5.95). Findings were validated on a cohort from the University Health Network (UHN) from 1989 to 2014 (follow‐up 8.15 years [SD 5.67]). Analysis was conducted with Cox proportional hazards and gradient boosting survival. The training set included 84.67% SRTR data (n = 15,289 patients), and the test set included 15.33% SRTR patients (n = 2769) and data from UHN patients (n = 1290). We included 18,058 adults (12,108 [67.05%] men, average age 54.21 years [SD 9.98]) from the SRTR who had undergone LT and had complete data for investigated features. A total of 4634 patients had preexisting DM, and 3158 had post‐LT DM. The UHN data consisted of 1290 LT recipients (910 [70.5%] men, average age 54.0 years [SD 10.4]). Increased serum creatinine and hypertension significantly impacted mortality with preexisting DM 1.36 (95% confidence interval [CI], 1.21‐1.54) and 1.20 (95% CI, 1.06‐1.35) times, respectively. Sirolimus use increased mortality 1.36 times (95% CI, 1.18‐1.58) in nondiabetics and 1.33 times (95% CI, 1.09‐1.63) in patients with preexisting DM. A similar effect was found in post‐LT DM, although it was not statistically significant (1.38 times; 95% CI, 1.07‐1.77; P = 0.07). Survival predictors generally achieved a 0.60 to 0.70 area under the receiver operating characteristic for 5‐year mortality. LT recipients who have DM have a higher mortality risk than those without DM. Hypertension, decreased renal function, and sirolimus for maintenance immunosuppression compound this mortality risk. These predisposing factors must be intensively treated and modified to optimize long‐term survival after transplant.
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spelling pubmed-82480952021-07-02 Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning Yasodhara, Angeline Dong, Victor Azhie, Amirhossein Goldenberg, Anna Bhat, Mamatha Liver Transpl Original Articles Diabetes mellitus (DM) significantly impacts long‐term survival after liver transplantation (LT). We identified survival factors for LT recipients who had DM to inform preventive care using machine‐learning analysis. We analyzed risk factors for mortality in patients from across the United States using the Scientific Registry of Transplant Recipients (SRTR). Patients had undergone LT from 1987 to 2019, with a follow‐up of 6.47 years (standard deviation [SD] 5.95). Findings were validated on a cohort from the University Health Network (UHN) from 1989 to 2014 (follow‐up 8.15 years [SD 5.67]). Analysis was conducted with Cox proportional hazards and gradient boosting survival. The training set included 84.67% SRTR data (n = 15,289 patients), and the test set included 15.33% SRTR patients (n = 2769) and data from UHN patients (n = 1290). We included 18,058 adults (12,108 [67.05%] men, average age 54.21 years [SD 9.98]) from the SRTR who had undergone LT and had complete data for investigated features. A total of 4634 patients had preexisting DM, and 3158 had post‐LT DM. The UHN data consisted of 1290 LT recipients (910 [70.5%] men, average age 54.0 years [SD 10.4]). Increased serum creatinine and hypertension significantly impacted mortality with preexisting DM 1.36 (95% confidence interval [CI], 1.21‐1.54) and 1.20 (95% CI, 1.06‐1.35) times, respectively. Sirolimus use increased mortality 1.36 times (95% CI, 1.18‐1.58) in nondiabetics and 1.33 times (95% CI, 1.09‐1.63) in patients with preexisting DM. A similar effect was found in post‐LT DM, although it was not statistically significant (1.38 times; 95% CI, 1.07‐1.77; P = 0.07). Survival predictors generally achieved a 0.60 to 0.70 area under the receiver operating characteristic for 5‐year mortality. LT recipients who have DM have a higher mortality risk than those without DM. Hypertension, decreased renal function, and sirolimus for maintenance immunosuppression compound this mortality risk. These predisposing factors must be intensively treated and modified to optimize long‐term survival after transplant. John Wiley and Sons Inc. 2021-02-02 2021-04 /pmc/articles/PMC8248095/ /pubmed/33113221 http://dx.doi.org/10.1002/lt.25930 Text en Copyright © 2020 The Authors. Liver Transplantation published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Yasodhara, Angeline
Dong, Victor
Azhie, Amirhossein
Goldenberg, Anna
Bhat, Mamatha
Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title_full Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title_fullStr Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title_full_unstemmed Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title_short Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning
title_sort identifying modifiable predictors of long‐term survival in liver transplant recipients with diabetes mellitus using machine learning
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248095/
https://www.ncbi.nlm.nih.gov/pubmed/33113221
http://dx.doi.org/10.1002/lt.25930
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