A Narrative Review: Predicting Liver Transplant Graft Survival Using Artificial Intelligence Modeling

Overview

Journal Front Transplant

Specialty General Surgery

Date 2024 Jul 12

PMID 38993758

Authors

Aiste Gulla

Ieva Jakiunaite

Ivona Juchneviciute

Gintautas Dzemyda

Affiliations

Soon will be listed here.

Abstract

Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.

References

Su F, Yu L, Berry K, Liou I, Landis C, Rayhill S . Aging of Liver Transplant Registrants and Recipients: Trends and Impact on Waitlist Outcomes, Post-Transplantation Outcomes, and Transplant-Related Survival Benefit. Gastroenterology. 2015; 150(2):441-53.e6. DOI: 10.1053/j.gastro.2015.10.043. View

Nacif L, Pinheiro R, de Arruda Pecora R, Tanigawa R, Rocha-Santos V, Andraus W . Re-Transplantation, Higher Creatinine Levels in Hepatitis C Virus Patients, and Donor Age Are Predictors of Mortality in Long-Term Analysis of Late Acute Rejection in Liver Transplantation. Ann Transplant. 2017; 22:9-16. DOI: 10.12659/aot.901010. View

Croome K, Marotta P, Wall W, Dale C, Levstik M, Chandok N . Should a lower quality organ go to the least sick patient? Model for end-stage liver disease score and donor risk index as predictors of early allograft dysfunction. Transplant Proc. 2012; 44(5):1303-6. DOI: 10.1016/j.transproceed.2012.01.115. View

Kim J, Akalin E, Dikman S, Gagliardi R, Schiano T, Bromberg J . The variable pathology of kidney disease after liver transplantation. Transplantation. 2010; 89(2):215-21. DOI: 10.1097/TP.0b013e3181c353e5. View

Gitto S, De Maria N, Marzi L, Magistri P, Falcini M, Vitale G . Pre-transplant diabetes predicts atherosclerotic vascular events and cardiovascular mortality in liver transplant recipients: a long-term follow-up study. Eur J Intern Med. 2020; 79:70-75. DOI: 10.1016/j.ejim.2020.05.041. View

Agatonovic-Kustrin S, Beresford R . Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000; 22(5):717-27. DOI: 10.1016/s0731-7085(99)00272-1. View

Ershoff B, Lee C, Wray C, Agopian V, Urban G, Baldi P . Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. Transplant Proc. 2020; 52(1):246-258. PMC: 7523496. DOI: 10.1016/j.transproceed.2019.10.019. View

Garcia-Compean D, Jaquez-Quintana J, Gonzalez-Gonzalez J, Maldonado-Garza H . Liver cirrhosis and diabetes: risk factors, pathophysiology, clinical implications and management. World J Gastroenterol. 2009; 15(3):280-8. PMC: 2653324. DOI: 10.3748/wjg.15.280. View

Grancini V, Resi V, Palmieri E, Pugliese G, Orsi E . Management of diabetes mellitus in patients undergoing liver transplantation. Pharmacol Res. 2019; 141:556-573. DOI: 10.1016/j.phrs.2019.01.042. View

10.

Borner N, Schoenberg M, Poschke P, Heiliger C, Jacob S, Koch D . A Novel Deep Learning Model as a Donor-Recipient Matching Tool to Predict Survival after Liver Transplantation. J Clin Med. 2022; 11(21). PMC: 9655123. DOI: 10.3390/jcm11216422. View

11.

Bhat V, Tazari M, Watt K, Bhat M . New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach. Mayo Clin Proc. 2018; 93(12):1794-1802. DOI: 10.1016/j.mayocp.2018.06.020. View

12.

Lau L, Kankanige Y, Rubinstein B, Jones R, Christophi C, Muralidharan V . Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation. Transplantation. 2016; 101(4):e125-e132. PMC: 7228574. DOI: 10.1097/TP.0000000000001600. View

13.

Asrani S, Saracino G, OLeary J, Gonzalez S, Kim P, McKenna G . Recipient characteristics and morbidity and mortality after liver transplantation. J Hepatol. 2018; 69(1):43-50. DOI: 10.1016/j.jhep.2018.02.004. View

14.

Allen A, Kim W, Therneau T, Larson J, Heimbach J, Rule A . Chronic kidney disease and associated mortality after liver transplantation--a time-dependent analysis using measured glomerular filtration rate. J Hepatol. 2014; 61(2):286-92. PMC: 4160310. DOI: 10.1016/j.jhep.2014.03.034. View

15.

Rigatti S . Random Forest. J Insur Med. 2017; 47(1):31-39. DOI: 10.17849/insm-47-01-31-39.1. View

16.

Bhat M, Rabindranath M, Chara B, Simonetto D . Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023; 78(6):1216-1233. DOI: 10.1016/j.jhep.2023.01.006. View

17.

Kantidakis G, Putter H, Lancia C, de Boer J, Braat A, Fiocco M . Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques. BMC Med Res Methodol. 2020; 20(1):277. PMC: 7667810. DOI: 10.1186/s12874-020-01153-1. View

18.

Fujiwara N, Nakagawa H, Kudo Y, Tateishi R, Taguri M, Watadani T . Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma. J Hepatol. 2015; 63(1):131-40. DOI: 10.1016/j.jhep.2015.02.031. View

19.

Klein K, Stafinski T, Menon D . Predicting survival after liver transplantation based on pre-transplant MELD score: a systematic review of the literature. PLoS One. 2013; 8(12):e80661. PMC: 3861188. DOI: 10.1371/journal.pone.0080661. View

20.

Elkrief L, Rautou P, Sarin S, Valla D, Paradis V, Moreau R . Diabetes mellitus in patients with cirrhosis: clinical implications and management. Liver Int. 2016; 36(7):936-48. DOI: 10.1111/liv.13115. View