Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding
Abstract Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (M...
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01883-w |
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| author | Zhaohui Bai Su Lin Mingyu Sun Shanshan Yuan Mariana Barros Marcondes Dapeng Ma Qiang Zhu Yiling Li Yingli He Cyriac Abby Philips Xiaofeng Liu Kanokwan Pinyopornpanish Lichun Shao Nahum Méndez-Sánchez Metin Basaranoglu Yunhai Wu Yu Chen Ling Yang Andrea Mancuso Frank Tacke Bimin Li Lei Liu Fanpu Ji Xingshun Qi |
| author_facet | Zhaohui Bai Su Lin Mingyu Sun Shanshan Yuan Mariana Barros Marcondes Dapeng Ma Qiang Zhu Yiling Li Yingli He Cyriac Abby Philips Xiaofeng Liu Kanokwan Pinyopornpanish Lichun Shao Nahum Méndez-Sánchez Metin Basaranoglu Yunhai Wu Yu Chen Ling Yang Andrea Mancuso Frank Tacke Bimin Li Lei Liu Fanpu Ji Xingshun Qi |
| author_sort | Zhaohui Bai |
| collection | DOAJ |
| description | Abstract Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (ML) models were established based on CAGIB components, and their area under curves (AUCs) were calculated and compared. Gray zone approach was employed to further stratify the risk of death. In training cohort, the AUC of CAGIB score was 0.789. Among the ML models, the least square support vector machine regression (LS-SVMR) model had the best predictive performance (AUC = 0.986). Patients were further divided into low- (LS-SVMR score <0.084), moderate- (LS-SVMR score 0.084–0.160), and high-risk (LS-SVMR score >0.160) groups with in-hospital mortality of 0.38%, 2.22%, and 64.37%, respectively. Statistical results were retained in validation cohort. LS-SVMR model has an excellent predictive performance for in-hospital death in cirrhotic patients with AGIB (ClinicalTrials.gov; NCT04662918). |
| format | Article |
| id | doaj-art-edad1489273f4a4e9c6f2da52297c2c8 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-edad1489273f4a4e9c6f2da52297c2c82025-08-20T03:43:36ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111210.1038/s41746-025-01883-wMachine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleedingZhaohui Bai0Su Lin1Mingyu Sun2Shanshan Yuan3Mariana Barros Marcondes4Dapeng Ma5Qiang Zhu6Yiling Li7Yingli He8Cyriac Abby Philips9Xiaofeng Liu10Kanokwan Pinyopornpanish11Lichun Shao12Nahum Méndez-Sánchez13Metin Basaranoglu14Yunhai Wu15Yu Chen16Ling Yang17Andrea Mancuso18Frank Tacke19Bimin Li20Lei Liu21Fanpu Ji22Xingshun Qi23Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater CommandLiver Research Center, The First Affiliated Hospital of Fujian Medical UniversityInstitute of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Gastroenterology, Xi’an Central HospitalSão Paulo State University (UNESP), Botucatu Medical SchoolDepartment of Critical Care Medicine, The Sixth People’s Hospital of DalianDepartment of Infectious Disease, Shandong Provincial Hospital affiliated to Shandong First Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of China Medical UniversityDepartment of Infectious Diseases, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Clinical and Translational Hepatology, The Liver Institute, Center of Excellence in GI Sciences, Rajagiri HospitalDepartment of Gastroenterology, The 960th Hospital of Chinese PLADepartment of Internal Medicine, Faculty of Medicine, Chiang Mai UniversityDepartment of Gastroenterology, Air Force Hospital of Northern Theater CommandMedica Sur Clinic & Foundation, National Autonomous University of MexicoGastroenterology and Hepatology, Bezmialem Vakif UniversityDepartment of Critical Care Medicine, The Sixth People’s Hospital of ShenyangDifficult and Complicated Liver Diseases and Artificial Liver Center, Beijing Youan Hospital, Capital Medical UniversityDivision of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyMedicina Interna 1, Azienda di Rilievo Nazionale ad Alta Specializzazione Civico-Di Cristina-BenfratelliDepartment of Hepatology & Gastroenterology, Charité - Universitätsmedizin Berlin, Campus Virchow-Klinikum (CVK) and Campus Charité Mitte (CCM)Department of Gastroenterology, The First Affiliated Hospital of Nanchang UniversityDepartment of Infectious Diseases, Tangdu Hospital, Fourth Military Medical UniversityDepartment of Hepatology, The Second Affiliated Hospital of Xi’an Jiaotong UniversityLiver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater CommandAbstract Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (ML) models were established based on CAGIB components, and their area under curves (AUCs) were calculated and compared. Gray zone approach was employed to further stratify the risk of death. In training cohort, the AUC of CAGIB score was 0.789. Among the ML models, the least square support vector machine regression (LS-SVMR) model had the best predictive performance (AUC = 0.986). Patients were further divided into low- (LS-SVMR score <0.084), moderate- (LS-SVMR score 0.084–0.160), and high-risk (LS-SVMR score >0.160) groups with in-hospital mortality of 0.38%, 2.22%, and 64.37%, respectively. Statistical results were retained in validation cohort. LS-SVMR model has an excellent predictive performance for in-hospital death in cirrhotic patients with AGIB (ClinicalTrials.gov; NCT04662918).https://doi.org/10.1038/s41746-025-01883-w |
| spellingShingle | Zhaohui Bai Su Lin Mingyu Sun Shanshan Yuan Mariana Barros Marcondes Dapeng Ma Qiang Zhu Yiling Li Yingli He Cyriac Abby Philips Xiaofeng Liu Kanokwan Pinyopornpanish Lichun Shao Nahum Méndez-Sánchez Metin Basaranoglu Yunhai Wu Yu Chen Ling Yang Andrea Mancuso Frank Tacke Bimin Li Lei Liu Fanpu Ji Xingshun Qi Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding npj Digital Medicine |
| title | Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| title_full | Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| title_fullStr | Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| title_full_unstemmed | Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| title_short | Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| title_sort | machine learning based cagib score predicts in hospital mortality of cirrhotic patients with acute gastrointestinal bleeding |
| url | https://doi.org/10.1038/s41746-025-01883-w |
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