Explainable machine learning for the assessment of donor grafts in liver transplantation
The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application. Data from 5242 donor-recipien...
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| Veröffentlicht in: | Hepatology research Jg. 55; H. 6; S. 908 - 921 |
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| Abstract | The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application.
Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions.
Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834.
The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions. |
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| AbstractList | The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application.BACKGROUND AND AIMThe shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application.Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions.METHODData from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions.Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834.RESULTSAmong 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834.The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.CONCLUSIONSThe developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions. The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application. Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions. Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834. The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions. Background and Aim The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision‐making process for clinical application. Method Data from 5242 donor–recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty‐three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1‐month and 3‐month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model’s decisions. Results Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ‐glutamyl transpeptidase, and blood glucose levels as top predictors of post‐LT DGF. The proposed model AUC’s 1‐month survival prediction was 0.841 and the 3‐month survival prediction was 0.834. Conclusions The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions. |
| Author | Siqi, Li Shuhong, Yi Linsen, Ye Mingshen, Zhang Siyi, Dong Kun, Li Haoyuan, Yu Wei, Liu Yang, Yang Guihua, Chen Zhixing, Liang Hua, Li Peng, Jiang Yongwei, Hu Xiao, Xu Shusen, Zheng |
| Author_xml | – sequence: 1 givenname: Liang surname: Zhixing fullname: Zhixing, Liang organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 2 givenname: Ye surname: Linsen fullname: Linsen, Ye organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 3 givenname: Jiang surname: Peng fullname: Peng, Jiang organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 4 givenname: Dong surname: Siyi fullname: Siyi, Dong organization: National Center for Healthcare Quality Management of Liver Transplant Hangzhou China – sequence: 5 givenname: Yu surname: Haoyuan fullname: Haoyuan, Yu organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 6 givenname: Li surname: Kun fullname: Kun, Li organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 7 givenname: Li surname: Siqi fullname: Siqi, Li organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 8 givenname: Hu surname: Yongwei fullname: Yongwei, Hu organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 9 givenname: Zhang surname: Mingshen fullname: Mingshen, Zhang organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 10 givenname: Liu surname: Wei fullname: Wei, Liu organization: Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 11 givenname: Li surname: Hua fullname: Hua, Li organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 12 givenname: Yi surname: Shuhong fullname: Shuhong, Yi organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 13 givenname: Chen surname: Guihua fullname: Guihua, Chen organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China, Guangdong Provincial Key Laboratory of Liver Disease Research The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China – sequence: 14 givenname: Xu orcidid: 0000-0002-2761-2811 surname: Xiao fullname: Xiao, Xu organization: National Center for Healthcare Quality Management of Liver Transplant Hangzhou China, Institute of Translational Medicine Zhejiang University Hangzhou China, School of Clinical Medicine Hangzhou Medical College Hangzhou China, NHC Key Laboratory of Combined Multi‐organ Transplantation Hangzhou China – sequence: 15 givenname: Zheng surname: Shusen fullname: Shusen, Zheng organization: National Center for Healthcare Quality Management of Liver Transplant Hangzhou China, Institute of Translational Medicine Zhejiang University Hangzhou China, Department of Hepatobiliary Surgery First Affiliated Hospital of Zhejiang University Hangzhou China, Department of Hepatobiliary and Pancreatic Surgery Shulan (Hangzhou) Hospital Hangzhou China – sequence: 16 givenname: Yang surname: Yang fullname: Yang, Yang organization: Department of Hepatic Surgery and Liver Transplantation Center The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China |
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| Keywords | eXtreme gradient boosting algorithms machine learning SHapley additive exPlaination delayed graft function liver transplantation |
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| Snippet | The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict... Background and Aim The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning... |
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| SubjectTerms | Accuracy Algorithms Blood levels Decision making Learning algorithms Liver transplantation Liver transplants Machine learning Survival γ-Glutamyltransferase |
| Title | Explainable machine learning for the assessment of donor grafts in liver transplantation |
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