Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study
BackgroundMethods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple c...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1510674/full |
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| author | Zihan Li Zihan Li Yibo Zhang Zixiang Chen Jiangming Chen Hui Hou Cheng Wang Zheng Lu Xiaoming Wang Xiaoping Geng Fubao Liu |
| author_facet | Zihan Li Zihan Li Yibo Zhang Zixiang Chen Jiangming Chen Hui Hou Cheng Wang Zheng Lu Xiaoming Wang Xiaoping Geng Fubao Liu |
| author_sort | Zihan Li |
| collection | DOAJ |
| description | BackgroundMethods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. Nine predictive models, which we named the Correlation Analysis and Recurrence Evaluation System (CARES), were developed and compared using machine learning (ML) methods to predict the patients’ dynamic recurrence risk within 5 post-operative years. We adopted a k-fold cross validation with k = 10 and tested model performance on a separate testing set. The area under the receiver operating characteristic curve was used to evaluate the performance of the models, and the significance and direction of each predictive variable were interpreted and justified based on Shapley Additive Explanations.ResultsModels based on ML methods outperformed those based on traditional regression analysis in predicting the recurrent risk of patients with RH, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) showing the best performance, both yielding an AUC (Area Under the receiver operating characteristic Curve) of∼0.9 or higher at predictions. These models were proved to have even better performance on testing sets than in a 10-fold cross validation, indicating that the model was not overfitted. The SHAP method revealed that immediate stone clearance, final stone clearance, number of previous surgeries, and preoperative CA19-9 index were the most important predictors of recurrence after reoperation in RH patients. An online version of the CARES model was implemented.ConclusionThe CARES model was firstly developed based on ML methods and further encapsulated into an online version for predicting the recurrence of patients with RH after hepatectomy, which can guide clinical decision-making and personalised postoperative surveillance. |
| format | Article |
| id | doaj-art-36a949bef1374b9ab97985deba2fe3a4 |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-36a949bef1374b9ab97985deba2fe3a42024-11-27T06:33:01ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2024-11-01610.3389/fdgth.2024.15106741510674Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective studyZihan Li0Zihan Li1Yibo Zhang2Zixiang Chen3Jiangming Chen4Hui Hou5Cheng Wang6Zheng Lu7Xiaoming Wang8Xiaoping Geng9Fubao Liu10Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaCardiology Division, Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Analytics, Marketing and Operations, Imperial College London, London, United KingdomDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of General Surgery, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, ChinaDepartment of General Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, ChinaDepartment of General Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, ChinaDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaBackgroundMethods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. Nine predictive models, which we named the Correlation Analysis and Recurrence Evaluation System (CARES), were developed and compared using machine learning (ML) methods to predict the patients’ dynamic recurrence risk within 5 post-operative years. We adopted a k-fold cross validation with k = 10 and tested model performance on a separate testing set. The area under the receiver operating characteristic curve was used to evaluate the performance of the models, and the significance and direction of each predictive variable were interpreted and justified based on Shapley Additive Explanations.ResultsModels based on ML methods outperformed those based on traditional regression analysis in predicting the recurrent risk of patients with RH, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) showing the best performance, both yielding an AUC (Area Under the receiver operating characteristic Curve) of∼0.9 or higher at predictions. These models were proved to have even better performance on testing sets than in a 10-fold cross validation, indicating that the model was not overfitted. The SHAP method revealed that immediate stone clearance, final stone clearance, number of previous surgeries, and preoperative CA19-9 index were the most important predictors of recurrence after reoperation in RH patients. An online version of the CARES model was implemented.ConclusionThe CARES model was firstly developed based on ML methods and further encapsulated into an online version for predicting the recurrence of patients with RH after hepatectomy, which can guide clinical decision-making and personalised postoperative surveillance.https://www.frontiersin.org/articles/10.3389/fdgth.2024.1510674/fullrecurrent hepatolithiasismachine learningprediction modelhigh-order correlation datamachine learning operations |
| spellingShingle | Zihan Li Zihan Li Yibo Zhang Zixiang Chen Jiangming Chen Hui Hou Cheng Wang Zheng Lu Xiaoming Wang Xiaoping Geng Fubao Liu Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study Frontiers in Digital Health recurrent hepatolithiasis machine learning prediction model high-order correlation data machine learning operations |
| title | Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study |
| title_full | Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study |
| title_fullStr | Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study |
| title_full_unstemmed | Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study |
| title_short | Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study |
| title_sort | correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis a multicentre retrospective study |
| topic | recurrent hepatolithiasis machine learning prediction model high-order correlation data machine learning operations |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1510674/full |
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