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
BackgroundPatients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis. ObjectiveThis study used machine learning (ML) algorithms with an aim to extract critical p...
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JMIR Publications
2025-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e55046 |
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| author | Zhendong Ding Linan Zhang Yihan Zhang Jing Yang Yuheng Luo Mian Ge Weifeng Yao Ziqing Hei Chaojin Chen |
| author_facet | Zhendong Ding Linan Zhang Yihan Zhang Jing Yang Yuheng Luo Mian Ge Weifeng Yao Ziqing Hei Chaojin Chen |
| author_sort | Zhendong Ding |
| collection | DOAJ |
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BackgroundPatients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis.
ObjectiveThis study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.
MethodsIn 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.
ResultsIn 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.
ConclusionsA 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. |
| format | Article |
| id | doaj-art-1f08cf600a474042a9ee17f3ff241a34 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-1f08cf600a474042a9ee17f3ff241a342025-01-15T15:45:32ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e5504610.2196/55046A 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 StudyZhendong Dinghttps://orcid.org/0000-0001-5178-8983Linan Zhanghttps://orcid.org/0000-0002-6125-9871Yihan Zhanghttps://orcid.org/0000-0002-3546-9752Jing Yanghttps://orcid.org/0000-0002-0108-677XYuheng Luohttps://orcid.org/0000-0001-8920-8390Mian Gehttps://orcid.org/0000-0002-5197-3940Weifeng Yaohttps://orcid.org/0000-0002-5284-9159Ziqing Heihttps://orcid.org/0000-0001-7466-2769Chaojin Chenhttps://orcid.org/0000-0001-5101-4101 BackgroundPatients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis. ObjectiveThis study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. MethodsIn 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. ResultsIn 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. ConclusionsA 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.https://www.jmir.org/2025/1/e55046 |
| spellingShingle | Zhendong Ding Linan Zhang Yihan Zhang Jing Yang Yuheng Luo Mian Ge Weifeng Yao Ziqing Hei Chaojin Chen 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 Journal of Medical Internet Research |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://www.jmir.org/2025/1/e55046 |
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