Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context
Summary: Background: Although numerous prognostic scores have been developed for patients with cirrhosis after Transjugular intrahepatic portosystemic shunt (TIPS) placement over years, an accurate machine learning (ML)-based model remains unavailable. The aim of this study was to develop and valid...
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Elsevier
2025-01-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537024005807 |
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| author | Binlin Da Huan Chen Wei Wu Wuhua Guo Anru Zhou Qin Yin Jun Gao Junhui Chen Jiangqiang Xiao Lei Wang Ming Zhang Yuzheng Zhuge Feng Zhang |
| author_facet | Binlin Da Huan Chen Wei Wu Wuhua Guo Anru Zhou Qin Yin Jun Gao Junhui Chen Jiangqiang Xiao Lei Wang Ming Zhang Yuzheng Zhuge Feng Zhang |
| author_sort | Binlin Da |
| collection | DOAJ |
| description | Summary: Background: Although numerous prognostic scores have been developed for patients with cirrhosis after Transjugular intrahepatic portosystemic shunt (TIPS) placement over years, an accurate machine learning (ML)-based model remains unavailable. The aim of this study was to develop and validate a ML-based prognostic model to predict survival in patients with cirrhosis after TIPS placement. Methods: In this retrospective study in China, patients diagnosed with cirrhosis after TIPS placement from 2014 to 2020 in our cohort were included to develop a ML-based model. Patients from the other two tertiary hospitals between 2016 and 2022 were as external validation cohort. The random forest (RF) model was built using 7 selected features via the least absolute shrinkage and selection operator (LASSO) regression, and subsequent 10-fold cross-validation was performed. Findings: A total of 400 patients in our cohort were included (median age and interquartile range, 59 (50, 66); 240 men). Two hundred and eighty patients made up the training set and 120 were in the testing set, and 346 patients were included in the external validation cohort. Seven attributes were selected: Na, ammonia (Amm), total bilirubin (Tb), albumin (Alb), age, creatinine (Cr), and ascites. These parameters were included in a new score named the RF model. The accuracy, precision, recall, and F1 Score of the RF model were 0.84 (95% CI: 0.76, 0.91), 0.84 (95% CI: 0.77, 0.91), 0.99 (95% CI: 0.95, 1.00), 0.91 (95% CI: 0.81, 0.10) in the testing set, and 0.88 (95% CI: 0.84, 0.91), 0.89 (95% CI: 0.85, 0.92), 0.99 (95% CI: 0.97, 1.00), 0.93 (95% CI: 0.85, 0.97) in the validation cohort, respectively. The calibration curve showed a slope of 0.875 in the testing set and a slope of 0.778 in the external validation cohort, suggesting well calibration performance. The RF model outperformed other scoring systems, such as the (Child-Turcotte-Pugh score) CTP, (model for end-stage liver disease) MELD, (sodium MELD) MELD-Na, (Freiburg index of post-TIPS survival) FIPS and (Albumin-Bilirubin) ALBI, showing the highest (area under the curve) AUC of 0.82 (95% CI: 0.72, 0.91) and 0.7 (95% CI: 0.60, 0.79) in predicting 1-year survival across the testing set and external validation cohort. Interpretation: This study developed a RF model that better predicted 1-year survival for patients with cirrhosis after TIPS placement than the other scores. Funding: National Natural Science Foundation of China (grant numbers 81900552 and 82370628). |
| format | Article |
| id | doaj-art-4a34ba5e010f47f99d269b34f7784c50 |
| institution | Kabale University |
| issn | 2589-5370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj-art-4a34ba5e010f47f99d269b34f7784c502025-01-22T05:43:28ZengElsevierEClinicalMedicine2589-53702025-01-0179103001Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in contextBinlin Da0Huan Chen1Wei Wu2Wuhua Guo3Anru Zhou4Qin Yin5Jun Gao6Junhui Chen7Jiangqiang Xiao8Lei Wang9Ming Zhang10Yuzheng Zhuge11Feng Zhang12Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Research Institute of General Surgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, ChinaDepartment of Interventional Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, ChinaSchool of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Lishui District JingQiao Central Health Center, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Corresponding author. 321#, Zhongshan Road, Nanjing, 210008, Jiangsu, China.Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Corresponding author. 321#, Zhongshan Road, Nanjing, 210008, Jiangsu, China.Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Corresponding author. 321#, Zhongshan Road, Nanjing, 210008, Jiangsu, China.Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China; Corresponding author. 321#, Zhongshan Road, Nanjing, 210008, Jiangsu, China.Summary: Background: Although numerous prognostic scores have been developed for patients with cirrhosis after Transjugular intrahepatic portosystemic shunt (TIPS) placement over years, an accurate machine learning (ML)-based model remains unavailable. The aim of this study was to develop and validate a ML-based prognostic model to predict survival in patients with cirrhosis after TIPS placement. Methods: In this retrospective study in China, patients diagnosed with cirrhosis after TIPS placement from 2014 to 2020 in our cohort were included to develop a ML-based model. Patients from the other two tertiary hospitals between 2016 and 2022 were as external validation cohort. The random forest (RF) model was built using 7 selected features via the least absolute shrinkage and selection operator (LASSO) regression, and subsequent 10-fold cross-validation was performed. Findings: A total of 400 patients in our cohort were included (median age and interquartile range, 59 (50, 66); 240 men). Two hundred and eighty patients made up the training set and 120 were in the testing set, and 346 patients were included in the external validation cohort. Seven attributes were selected: Na, ammonia (Amm), total bilirubin (Tb), albumin (Alb), age, creatinine (Cr), and ascites. These parameters were included in a new score named the RF model. The accuracy, precision, recall, and F1 Score of the RF model were 0.84 (95% CI: 0.76, 0.91), 0.84 (95% CI: 0.77, 0.91), 0.99 (95% CI: 0.95, 1.00), 0.91 (95% CI: 0.81, 0.10) in the testing set, and 0.88 (95% CI: 0.84, 0.91), 0.89 (95% CI: 0.85, 0.92), 0.99 (95% CI: 0.97, 1.00), 0.93 (95% CI: 0.85, 0.97) in the validation cohort, respectively. The calibration curve showed a slope of 0.875 in the testing set and a slope of 0.778 in the external validation cohort, suggesting well calibration performance. The RF model outperformed other scoring systems, such as the (Child-Turcotte-Pugh score) CTP, (model for end-stage liver disease) MELD, (sodium MELD) MELD-Na, (Freiburg index of post-TIPS survival) FIPS and (Albumin-Bilirubin) ALBI, showing the highest (area under the curve) AUC of 0.82 (95% CI: 0.72, 0.91) and 0.7 (95% CI: 0.60, 0.79) in predicting 1-year survival across the testing set and external validation cohort. Interpretation: This study developed a RF model that better predicted 1-year survival for patients with cirrhosis after TIPS placement than the other scores. Funding: National Natural Science Foundation of China (grant numbers 81900552 and 82370628).http://www.sciencedirect.com/science/article/pii/S2589537024005807Random forest modelPredictionSurvivalCirrhosisTransjugular intrahepatic portosystemic shunt |
| spellingShingle | Binlin Da Huan Chen Wei Wu Wuhua Guo Anru Zhou Qin Yin Jun Gao Junhui Chen Jiangqiang Xiao Lei Wang Ming Zhang Yuzheng Zhuge Feng Zhang Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context EClinicalMedicine Random forest model Prediction Survival Cirrhosis Transjugular intrahepatic portosystemic shunt |
| title | Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context |
| title_full | Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context |
| title_fullStr | Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context |
| title_full_unstemmed | Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context |
| title_short | Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntResearch in context |
| title_sort | development and validation of a machine learning based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shuntresearch in context |
| topic | Random forest model Prediction Survival Cirrhosis Transjugular intrahepatic portosystemic shunt |
| url | http://www.sciencedirect.com/science/article/pii/S2589537024005807 |
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