%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e55046
%T A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study
%A Ding,Zhendong
%A Zhang,Linan
%A Zhang,Yihan
%A Yang,Jing
%A Luo,Yuheng
%A Ge,Mian
%A Yao,Weifeng
%A Hei,Ziqing
%A Chen,Chaojin
%+ Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, No. 600 Tianhe Road, Guangzhou, 510630, China, 86 13430322182, chenchj28@mail.sysu.edu.cn
%K machine learning
%K risk factors
%K liver transplantation
%K perioperative neurocognitive disorders
%K MIMIC-Ⅳ database
%K external validation
%D 2025
%7 15.1.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis. Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients. Results: In the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method. Conclusions: A real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients. 
%M 39813086
%R 10.2196/55046
%U https://www.jmir.org/2025/1/e55046
%U https://doi.org/10.2196/55046
%U http://www.ncbi.nlm.nih.gov/pubmed/39813086