Treffer: Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.
Title:
Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.
Authors:
Dong R; Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, Nanjing, China., Luo Z; Department of Infectious Disease Prevention and Control, Huadong Research Institute for Medicine and Biotechniques, Nanjing, China., Xue H; Department of Liver Disease, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China., Shao J; Nantong Institute of Liver Disease, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China., Chen L; Nantong Institute of Liver Disease, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China., Jin W; Department of Infectious Disease, The Affiliated Suzhou Ninth Hospital of Soochow University, Suzhou, China., Yang L; Nantong Institute of Liver Disease, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China., Shen C; Department of Immunization Program, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China., Xu M; Department of Infectious Disease Prevention and Control, Huadong Research Institute for Medicine and Biotechniques, Nanjing, China., Wu M; Department of Big Data Center, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, China., Wang J; Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, Nanjing, China.; Department of Nursing, Taizhou School of Clinical Medicine, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Nanjing Medical University, Taizhou, China.
Source:
Liver international : official journal of the International Association for the Study of the Liver [Liver Int] 2025 Jun; Vol. 45 (6), pp. e70129.
Publication Type:
Journal Article; Validation Study; Multicenter Study
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 101160857 Publication Model: Print Cited Medium: Internet ISSN: 1478-3231 (Electronic) Linking ISSN: 14783223 NLM ISO Abbreviation: Liver Int Subsets: MEDLINE
Imprint Name(s):
Publication: Malden, MA : Wiley-Blackwell
Original Publication: Oxford, UK : Blackwell Munksgaard, c2003-
Original Publication: Oxford, UK : Blackwell Munksgaard, c2003-
MeSH Terms:
Machine Learning* , Hepatitis E*/complications , Hepatitis E*/diagnosis , Liver Failure, Acute*/diagnosis , Liver Failure, Acute*/virology , Liver Failure, Acute*/etiology, Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Adult ; China/epidemiology ; Risk Assessment/methods ; Hepatitis E virus
References:
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European Association for the Study of the Liver and European Association for the Study of the Liver, “EASL Clinical Practice Guidelines on Hepatitis E Virus Infection,” Journal of Hepatology 68, no. 6 (2018): 1256–1271, https://doi.org/10.1016/j.jhep.2018.03.005.
D. Sedhom, M. D'Souza, E. John, and V. Rustgi, “Viral Hepatitis and Acute Liver Failure: Still a Problem,” Clinical Liver Disease 22, no. 2 (2018): 289–300, https://doi.org/10.1016/j.cld.2018.01.005.
Y. Wang, H. Liu, S. Liu, et al., “Incidence, Predictors and Prognosis of Genotype 4 Hepatitis E Related Liver Failure: A Tertiary Nested Case‐Control Study,” Liver International 39, no. 12 (2019): 2291–2300, https://doi.org/10.1111/liv.14221.
R. Dong, D. Chang, Z. Luo, et al., “The Burden of HEV‐Related Acute Liver Failure in Bangladesh, China and India: A Systematic Review and Meta‐Analysis,” BMC Public Health 23, no. 1 (2023): 2369, https://doi.org/10.1186/s12889‐023‐17302‐2.
W. Hui and L. Wei, “Treatment of Hepatitis E,” Advances in Experimental Medicine and Biology 1417 (2023): 215–226, https://doi.org/10.1007/978‐981‐99‐1304‐6_15.
X. Mu, J. Zou, J. Chen, et al., “Low Platelets: A New and Simple Prognostic Marker for Patients With Hepatitis E Virus‐Related Acute Liver Failure,” Hepatology International 16, no. 5 (2022): 1116–1126, https://doi.org/10.1007/s12072‐022‐10302‐1.
Z. Xiang, B. Jiang, W. Li, et al., “The Diagnostic and Prognostic Value of Serum Exosome‐Derived Carbamoyl Phosphate Synthase 1 in HEV‐Related Acute Liver Failure Patients,” Journal of Medical Virology 94, no. 10 (2022): 5015–5025, https://doi.org/10.1002/jmv.27961.
J. Luo, X. Liang, J. Xin, et al., “Predicting the Onset of Hepatitis B Virus‐Related Acute‐On‐Chronic Liver Failure,” Clinical Gastroenterology and Hepatology 21, no. 3 (2023): 681–693, https://doi.org/10.1016/j.cgh.2022.03.016.
J. Wu, Y. Xu, Y. Cui, et al., “Dynamic Changes of Serum Metabolites Associated With Infection and Severity of Patients With Acute Hepatitis E Infection,” Journal of Medical Virology 94, no. 6 (2022): 2714–2726, https://doi.org/10.1002/jmv.27669.
Z. Xiang, C. Jiang, J. Yang, et al., “Serum Extracellular Vesicle‐Derived ASS1 Is a Promising Predictor for the Occurrence of HEV‐ALF,” Journal of Medical Virology 95, no. 1 (2023): e28425, https://doi.org/10.1002/jmv.28425.
J. Wu, N. Guo, X. Zhang, et al., “HEV‐LFS: A Novel Scoring Model for Patients With Hepatitis E Virus‐Related Liver Failure,” Journal of Viral Hepatitis 26, no. 11 (2019): 1334–1343, https://doi.org/10.1111/jvh.13174.
J. Wu, C. Shi, X. Sheng, et al., “Prognostic Nomogram for Patients With Hepatitis E Virus‐Related Acute Liver Failure: A Multicenter Study in China,” Journal of Clinical and Translational Hepatology 9, no. 6 (2021): 828–837, https://doi.org/10.14218/JCTH.2020.00117.
Y. Shen, W. Xu, Y. Chen, et al., “Early Prediction of Acute‐On‐Chronic Liver Failure Development in Patients With Diverse Chronic Liver Diseases,” Scientific Reports 14, no. 1 (2024): 28245, https://doi.org/10.1038/s41598‐024‐79486‐w.
S. Yue, S. Li, X. Huang, et al., “Machine Learning for the Prediction of Acute Kidney Injury in Patients With Sepsis,” Journal of Translational Medicine 20, no. 1 (2022): 215, https://doi.org/10.1186/s12967‐022‐03364‐0.
G. Zhang, Z. Wang, Z. Tong, et al., “AI Hybrid Survival Assessment for Advanced Heart Failure Patients With Renal Dysfunction,” Nature Communications 15, no. 1 (2024): 6756, https://doi.org/10.1038/s41467‐024‐50415‐9.
J. Polson, W. M. Lee, and American Association for the Study of Liver Disease, “AASLD Position Paper: The Management of Acute Liver Failure,” Hepatology 41, no. 5 (2005): 1179–1197, https://doi.org/10.1002/hep.20703.
Joint Committee for Guideline Revision, “2018 Chinese Guidelines for Prevention and Treatment of Hypertension‐A Report of the Revision Committee of Chinese Guidelines for Prevention and Treatment of Hypertension,” Journal of Geriatric Cardiology 16, no. 3 (2019): 182–241, https://doi.org/10.11909/j.issn.1671‐5411.2019.03.014.
W. Jia, J. Weng, D. Zhu, et al., “Standards of Medical Care for Type 2 Diabetes in China 2019,” Diabetes/Metabolism Research and Reviews 35, no. 6 (2019): e3158, https://doi.org/10.1002/dmrr.3158.
Z. Liu, L. Liu, S. Weng, et al., “Machine Learning‐Based Integration Develops an Immune‐Derived lncRNA Signature for Improving Outcomes in Colorectal Cancer,” Nature Communications 13, no. 1 (2022): 816, https://doi.org/10.1038/s41467‐022‐28421‐6.
H. Liu, W. Zhang, Y. Zhang, et al., “Mime: A Flexible Machine‐Learning Framework to Construct and Visualize Models for Clinical Characteristics Prediction and Feature Selection,” Computational and Structural Biotechnology Journal 23 (2024): 2798–2810, https://doi.org/10.1016/j.csbj.2024.06.035.
J. Hu, J. Xu, M. Li, et al., “Identification and Validation of an Explainable Prediction Model of Acute Kidney Injury With Prognostic Implications in Critically Ill Children: A Prospective Multicenter Cohort Study,” EClinicalMedicine 68 (2024): 102409, https://doi.org/10.1016/j.eclinm.2023.102409.
S. Dhanka and S. Maini, “A Hybridization of XGBoost Machine Learning Model by Optuna Hyperparameter Tuning Suite for Cardiovascular Disease Classification With Significant Effect of Outliers and Heterogeneous Training Datasets,” International Journal of Cardiology 420 (2025): 132757, https://doi.org/10.1016/j.ijcard.2024.132757.
G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. Moons, “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement,” BMJ 350 (2015): g7594, https://doi.org/10.1136/bmj.g7594.
A. C. Alba, T. Agoritsas, M. Walsh, et al., “Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature,” JAMA 318, no. 14 (2017): 1377–1384, https://doi.org/10.1001/jama.2017.12126.
J. Li, X. Liang, S. You, et al., “Development and Validation of a New Prognostic Score for Hepatitis B Virus‐Related Acute‐On‐Chronic Liver Failure,” Journal of Hepatology 75, no. 5 (2021): 1104–1115, https://doi.org/10.1016/j.jhep.2021.05.026.
G. H. Li, C. L. Cheung, K. C. Tan, et al., “Development and Validation of Sex‐Specific Hip Fracture Prediction Models Using Electronic Health Records: A Retrospective, Population‐Based Cohort Study,” EClinicalMedicine 58 (2023): 101876, https://doi.org/10.1016/j.eclinm.2023.101876.
H. Uno, L. Tian, T. Cai, I. S. Kohane, and L. J. Wei, “A Unified Inference Procedure for a Class of Measures to Assess Improvement in Risk Prediction Systems With Survival Data,” Statistics in Medicine 32, no. 14 (2013): 2430–2442, https://doi.org/10.1002/sim.5647.
O. V. Demler, N. P. Paynter, and N. R. Cook, “Tests of Calibration and Goodness‐Of‐Fit in the Survival Setting,” Statistics in Medicine 34, no. 10 (2015): 1659–1680, https://doi.org/10.1002/sim.6428.
B. Huang, J. Sollee, Y. H. Luo, et al., “Prediction of Lung Malignancy Progression and Survival With Machine Learning Based on Pre‐Treatment FDG‐PET/CT,” eBioMedicine 82 (2022): 104127, https://doi.org/10.1016/j.ebiom.2022.104127.
A. K. Clift, G. S. Collins, S. Lord, et al., “Predicting 10‐Year Breast Cancer Mortality Risk in the General Female Population in England: A Model Development and Validation Study,” Lancet Digit Health 5, no. 9 (2023): e571–e581, https://doi.org/10.1016/S2589‐7500(23)00113‐9.
Chinese Society of Hepatology and Chinese Medical Association, “Consensus on Prevention and Treatment of Hepatitis E,” Zhonghua Gan Zang Bing Za Zhi 30, no. 8 (2022): 820–831, https://doi.org/10.3760/cma.j.cn501113‐20220729‐00401.
F. Rui, Y. H. Yeo, L. Xu, et al., “Development of a Machine Learning‐Based Model to Predict Hepatic Inflammation in Chronic Hepatitis B Patients With Concurrent Hepatic Steatosis: A Cohort Study,” EClinicalMedicine 68 (2024): 102419, https://doi.org/10.1016/j.eclinm.2023.102419.
X. Yuan, Q. Xu, F. Du, et al., “Development and Validation of a Model to Predict Cognitive Impairment in Traumatic Brain Injury Patients: A Prospective Observational Study,” EClinicalMedicine 80 (2025): 103023, https://doi.org/10.1016/j.eclinm.2024.103023.
J. W. Liou, H. Mani, and J. H. Yen, “Viral Hepatitis, Cholesterol Metabolism, and Cholesterol‐Lowering Natural Compounds,” International Journal of Molecular Sciences 23, no. 7 (2022): 3897, https://doi.org/10.3390/ijms23073897.
R. Dong, H. Xue, L. Chen, et al., “Associations of Lipid Profiles With the Onset of HEV‐Related Acute Liver Failure: A Multicenter Cohort Study,” Journal of Medical Virology 96, no. 11 (2024): e70033, https://doi.org/10.1002/jmv.70033.
M. Trieb, F. Rainer, V. Stadlbauer, et al., “HDL‐Related Biomarkers Are Robust Predictors of Survival in Patients With Chronic Liver Failure,” Journal of Hepatology 73, no. 1 (2020): 113–120, https://doi.org/10.1016/j.jhep.2020.01.026.
C. Chen, A. Zhu, S. Ye, et al., “A New Dyslipidemia‐Based Scoring Model to Predict Transplant‐Free Survival in Patients With Hepatitis E‐Triggered Acute‐On‐Chronic Liver Failure,” Lipids in Health and Disease 22, no. 1 (2023): 80, https://doi.org/10.1186/s12944‐023‐01826‐y.
C. Chen, S. Y. Zhang, and L. Chen, “Review of Clinical Characteristics, Immune Responses and Regulatory Mechanisms of Hepatitis E‐Associated Liver Failure,” World Journal of Clinical Cases 10, no. 19 (2022): 6341–6348, https://doi.org/10.12998/wjcc.v10.i19.6341.
J. Wu, B. Ling, N. Guo, G. Zhai, M. Li, and Y. Guo, “Immunological Manifestations of Hepatitis E‐Associated Acute and Chronic Liver Failure and Its Regulatory Mechanisms,” Frontiers in Medicine 8 (2021): 725993, https://doi.org/10.3389/fmed.2021.725993.
J. Wu, X. Zhang, H. Liu, N. Guo, Q. Pan, and Y. Wang, “RDW, NLR and RLR in Predicting Liver Failure and Prognosis in Patients With Hepatitis E Virus Infection,” Clinical Biochemistry 63 (2019): 24–31, https://doi.org/10.1016/j.clinbiochem.2018.11.012.
N. X. Hoan, H. V. Tong, N. Hecht, et al., “Hepatitis E Virus Superinfection and Clinical Progression in Hepatitis B Patients,” eBioMedicine 2, no. 12 (2015): 2080–2086, https://doi.org/10.1016/j.ebiom.2015.11.020.
R. Dong, L. Huang, L. Chen, et al., “The Predictive Value of Fibrosis Profiles for Hepatitis E Virus‐Related Liver Failure Among Hospitalized Patients With Acute Hepatitis E: A Retrospective Cohort Study,” BMC Infectious Diseases 25, no. 1 (2025): 255, https://doi.org/10.1186/s12879‐025‐10632‐z.
G. W. Ji, C. Y. Jiao, Z. G. Xu, X. C. Li, K. Wang, and X. H. Wang, “Development and Validation of a Gradient Boosting Machine to Predict Prognosis After Liver Resection for Intrahepatic Cholangiocarcinoma,” BMC Cancer 22, no. 1 (2022): 258, https://doi.org/10.1186/s12885‐022‐09352‐3.
J. E. Bibault, D. T. Chang, and L. Xing, “Development and Validation of a Model to Predict Survival in Colorectal Cancer Using a Gradient‐Boosted Machine,” Gut 70, no. 5 (2021): 884–889, https://doi.org/10.1136/gutjnl‐2020‐321799.
H. Y. Kim, P. Lampertico, J. Y. Nam, et al., “An Artificial Intelligence Model to Predict Hepatocellular Carcinoma Risk in Korean and Caucasian Patients With Chronic Hepatitis B,” Journal of Hepatology 76, no. 2 (2022): 311–318, https://doi.org/10.1016/j.jhep.2021.09.025.
European Association for the Study of the Liver and European Association for the Study of the Liver, “EASL Clinical Practice Guidelines on Hepatitis E Virus Infection,” Journal of Hepatology 68, no. 6 (2018): 1256–1271, https://doi.org/10.1016/j.jhep.2018.03.005.
D. Sedhom, M. D'Souza, E. John, and V. Rustgi, “Viral Hepatitis and Acute Liver Failure: Still a Problem,” Clinical Liver Disease 22, no. 2 (2018): 289–300, https://doi.org/10.1016/j.cld.2018.01.005.
Y. Wang, H. Liu, S. Liu, et al., “Incidence, Predictors and Prognosis of Genotype 4 Hepatitis E Related Liver Failure: A Tertiary Nested Case‐Control Study,” Liver International 39, no. 12 (2019): 2291–2300, https://doi.org/10.1111/liv.14221.
R. Dong, D. Chang, Z. Luo, et al., “The Burden of HEV‐Related Acute Liver Failure in Bangladesh, China and India: A Systematic Review and Meta‐Analysis,” BMC Public Health 23, no. 1 (2023): 2369, https://doi.org/10.1186/s12889‐023‐17302‐2.
W. Hui and L. Wei, “Treatment of Hepatitis E,” Advances in Experimental Medicine and Biology 1417 (2023): 215–226, https://doi.org/10.1007/978‐981‐99‐1304‐6_15.
X. Mu, J. Zou, J. Chen, et al., “Low Platelets: A New and Simple Prognostic Marker for Patients With Hepatitis E Virus‐Related Acute Liver Failure,” Hepatology International 16, no. 5 (2022): 1116–1126, https://doi.org/10.1007/s12072‐022‐10302‐1.
Z. Xiang, B. Jiang, W. Li, et al., “The Diagnostic and Prognostic Value of Serum Exosome‐Derived Carbamoyl Phosphate Synthase 1 in HEV‐Related Acute Liver Failure Patients,” Journal of Medical Virology 94, no. 10 (2022): 5015–5025, https://doi.org/10.1002/jmv.27961.
J. Luo, X. Liang, J. Xin, et al., “Predicting the Onset of Hepatitis B Virus‐Related Acute‐On‐Chronic Liver Failure,” Clinical Gastroenterology and Hepatology 21, no. 3 (2023): 681–693, https://doi.org/10.1016/j.cgh.2022.03.016.
J. Wu, Y. Xu, Y. Cui, et al., “Dynamic Changes of Serum Metabolites Associated With Infection and Severity of Patients With Acute Hepatitis E Infection,” Journal of Medical Virology 94, no. 6 (2022): 2714–2726, https://doi.org/10.1002/jmv.27669.
Z. Xiang, C. Jiang, J. Yang, et al., “Serum Extracellular Vesicle‐Derived ASS1 Is a Promising Predictor for the Occurrence of HEV‐ALF,” Journal of Medical Virology 95, no. 1 (2023): e28425, https://doi.org/10.1002/jmv.28425.
J. Wu, N. Guo, X. Zhang, et al., “HEV‐LFS: A Novel Scoring Model for Patients With Hepatitis E Virus‐Related Liver Failure,” Journal of Viral Hepatitis 26, no. 11 (2019): 1334–1343, https://doi.org/10.1111/jvh.13174.
J. Wu, C. Shi, X. Sheng, et al., “Prognostic Nomogram for Patients With Hepatitis E Virus‐Related Acute Liver Failure: A Multicenter Study in China,” Journal of Clinical and Translational Hepatology 9, no. 6 (2021): 828–837, https://doi.org/10.14218/JCTH.2020.00117.
Y. Shen, W. Xu, Y. Chen, et al., “Early Prediction of Acute‐On‐Chronic Liver Failure Development in Patients With Diverse Chronic Liver Diseases,” Scientific Reports 14, no. 1 (2024): 28245, https://doi.org/10.1038/s41598‐024‐79486‐w.
S. Yue, S. Li, X. Huang, et al., “Machine Learning for the Prediction of Acute Kidney Injury in Patients With Sepsis,” Journal of Translational Medicine 20, no. 1 (2022): 215, https://doi.org/10.1186/s12967‐022‐03364‐0.
G. Zhang, Z. Wang, Z. Tong, et al., “AI Hybrid Survival Assessment for Advanced Heart Failure Patients With Renal Dysfunction,” Nature Communications 15, no. 1 (2024): 6756, https://doi.org/10.1038/s41467‐024‐50415‐9.
J. Polson, W. M. Lee, and American Association for the Study of Liver Disease, “AASLD Position Paper: The Management of Acute Liver Failure,” Hepatology 41, no. 5 (2005): 1179–1197, https://doi.org/10.1002/hep.20703.
Joint Committee for Guideline Revision, “2018 Chinese Guidelines for Prevention and Treatment of Hypertension‐A Report of the Revision Committee of Chinese Guidelines for Prevention and Treatment of Hypertension,” Journal of Geriatric Cardiology 16, no. 3 (2019): 182–241, https://doi.org/10.11909/j.issn.1671‐5411.2019.03.014.
W. Jia, J. Weng, D. Zhu, et al., “Standards of Medical Care for Type 2 Diabetes in China 2019,” Diabetes/Metabolism Research and Reviews 35, no. 6 (2019): e3158, https://doi.org/10.1002/dmrr.3158.
Z. Liu, L. Liu, S. Weng, et al., “Machine Learning‐Based Integration Develops an Immune‐Derived lncRNA Signature for Improving Outcomes in Colorectal Cancer,” Nature Communications 13, no. 1 (2022): 816, https://doi.org/10.1038/s41467‐022‐28421‐6.
H. Liu, W. Zhang, Y. Zhang, et al., “Mime: A Flexible Machine‐Learning Framework to Construct and Visualize Models for Clinical Characteristics Prediction and Feature Selection,” Computational and Structural Biotechnology Journal 23 (2024): 2798–2810, https://doi.org/10.1016/j.csbj.2024.06.035.
J. Hu, J. Xu, M. Li, et al., “Identification and Validation of an Explainable Prediction Model of Acute Kidney Injury With Prognostic Implications in Critically Ill Children: A Prospective Multicenter Cohort Study,” EClinicalMedicine 68 (2024): 102409, https://doi.org/10.1016/j.eclinm.2023.102409.
S. Dhanka and S. Maini, “A Hybridization of XGBoost Machine Learning Model by Optuna Hyperparameter Tuning Suite for Cardiovascular Disease Classification With Significant Effect of Outliers and Heterogeneous Training Datasets,” International Journal of Cardiology 420 (2025): 132757, https://doi.org/10.1016/j.ijcard.2024.132757.
G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. Moons, “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement,” BMJ 350 (2015): g7594, https://doi.org/10.1136/bmj.g7594.
A. C. Alba, T. Agoritsas, M. Walsh, et al., “Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature,” JAMA 318, no. 14 (2017): 1377–1384, https://doi.org/10.1001/jama.2017.12126.
J. Li, X. Liang, S. You, et al., “Development and Validation of a New Prognostic Score for Hepatitis B Virus‐Related Acute‐On‐Chronic Liver Failure,” Journal of Hepatology 75, no. 5 (2021): 1104–1115, https://doi.org/10.1016/j.jhep.2021.05.026.
G. H. Li, C. L. Cheung, K. C. Tan, et al., “Development and Validation of Sex‐Specific Hip Fracture Prediction Models Using Electronic Health Records: A Retrospective, Population‐Based Cohort Study,” EClinicalMedicine 58 (2023): 101876, https://doi.org/10.1016/j.eclinm.2023.101876.
H. Uno, L. Tian, T. Cai, I. S. Kohane, and L. J. Wei, “A Unified Inference Procedure for a Class of Measures to Assess Improvement in Risk Prediction Systems With Survival Data,” Statistics in Medicine 32, no. 14 (2013): 2430–2442, https://doi.org/10.1002/sim.5647.
O. V. Demler, N. P. Paynter, and N. R. Cook, “Tests of Calibration and Goodness‐Of‐Fit in the Survival Setting,” Statistics in Medicine 34, no. 10 (2015): 1659–1680, https://doi.org/10.1002/sim.6428.
B. Huang, J. Sollee, Y. H. Luo, et al., “Prediction of Lung Malignancy Progression and Survival With Machine Learning Based on Pre‐Treatment FDG‐PET/CT,” eBioMedicine 82 (2022): 104127, https://doi.org/10.1016/j.ebiom.2022.104127.
A. K. Clift, G. S. Collins, S. Lord, et al., “Predicting 10‐Year Breast Cancer Mortality Risk in the General Female Population in England: A Model Development and Validation Study,” Lancet Digit Health 5, no. 9 (2023): e571–e581, https://doi.org/10.1016/S2589‐7500(23)00113‐9.
Chinese Society of Hepatology and Chinese Medical Association, “Consensus on Prevention and Treatment of Hepatitis E,” Zhonghua Gan Zang Bing Za Zhi 30, no. 8 (2022): 820–831, https://doi.org/10.3760/cma.j.cn501113‐20220729‐00401.
F. Rui, Y. H. Yeo, L. Xu, et al., “Development of a Machine Learning‐Based Model to Predict Hepatic Inflammation in Chronic Hepatitis B Patients With Concurrent Hepatic Steatosis: A Cohort Study,” EClinicalMedicine 68 (2024): 102419, https://doi.org/10.1016/j.eclinm.2023.102419.
X. Yuan, Q. Xu, F. Du, et al., “Development and Validation of a Model to Predict Cognitive Impairment in Traumatic Brain Injury Patients: A Prospective Observational Study,” EClinicalMedicine 80 (2025): 103023, https://doi.org/10.1016/j.eclinm.2024.103023.
J. W. Liou, H. Mani, and J. H. Yen, “Viral Hepatitis, Cholesterol Metabolism, and Cholesterol‐Lowering Natural Compounds,” International Journal of Molecular Sciences 23, no. 7 (2022): 3897, https://doi.org/10.3390/ijms23073897.
R. Dong, H. Xue, L. Chen, et al., “Associations of Lipid Profiles With the Onset of HEV‐Related Acute Liver Failure: A Multicenter Cohort Study,” Journal of Medical Virology 96, no. 11 (2024): e70033, https://doi.org/10.1002/jmv.70033.
M. Trieb, F. Rainer, V. Stadlbauer, et al., “HDL‐Related Biomarkers Are Robust Predictors of Survival in Patients With Chronic Liver Failure,” Journal of Hepatology 73, no. 1 (2020): 113–120, https://doi.org/10.1016/j.jhep.2020.01.026.
C. Chen, A. Zhu, S. Ye, et al., “A New Dyslipidemia‐Based Scoring Model to Predict Transplant‐Free Survival in Patients With Hepatitis E‐Triggered Acute‐On‐Chronic Liver Failure,” Lipids in Health and Disease 22, no. 1 (2023): 80, https://doi.org/10.1186/s12944‐023‐01826‐y.
C. Chen, S. Y. Zhang, and L. Chen, “Review of Clinical Characteristics, Immune Responses and Regulatory Mechanisms of Hepatitis E‐Associated Liver Failure,” World Journal of Clinical Cases 10, no. 19 (2022): 6341–6348, https://doi.org/10.12998/wjcc.v10.i19.6341.
J. Wu, B. Ling, N. Guo, G. Zhai, M. Li, and Y. Guo, “Immunological Manifestations of Hepatitis E‐Associated Acute and Chronic Liver Failure and Its Regulatory Mechanisms,” Frontiers in Medicine 8 (2021): 725993, https://doi.org/10.3389/fmed.2021.725993.
J. Wu, X. Zhang, H. Liu, N. Guo, Q. Pan, and Y. Wang, “RDW, NLR and RLR in Predicting Liver Failure and Prognosis in Patients With Hepatitis E Virus Infection,” Clinical Biochemistry 63 (2019): 24–31, https://doi.org/10.1016/j.clinbiochem.2018.11.012.
N. X. Hoan, H. V. Tong, N. Hecht, et al., “Hepatitis E Virus Superinfection and Clinical Progression in Hepatitis B Patients,” eBioMedicine 2, no. 12 (2015): 2080–2086, https://doi.org/10.1016/j.ebiom.2015.11.020.
R. Dong, L. Huang, L. Chen, et al., “The Predictive Value of Fibrosis Profiles for Hepatitis E Virus‐Related Liver Failure Among Hospitalized Patients With Acute Hepatitis E: A Retrospective Cohort Study,” BMC Infectious Diseases 25, no. 1 (2025): 255, https://doi.org/10.1186/s12879‐025‐10632‐z.
G. W. Ji, C. Y. Jiao, Z. G. Xu, X. C. Li, K. Wang, and X. H. Wang, “Development and Validation of a Gradient Boosting Machine to Predict Prognosis After Liver Resection for Intrahepatic Cholangiocarcinoma,” BMC Cancer 22, no. 1 (2022): 258, https://doi.org/10.1186/s12885‐022‐09352‐3.
J. E. Bibault, D. T. Chang, and L. Xing, “Development and Validation of a Model to Predict Survival in Colorectal Cancer Using a Gradient‐Boosted Machine,” Gut 70, no. 5 (2021): 884–889, https://doi.org/10.1136/gutjnl‐2020‐321799.
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Grant Information:
2023YQZL02 Center for Disease Control and Prevention 'Yiqi' Independent Innovation Incubation Fund; MS2023080 Nantong Social Livelihood Science and Technology Project; JiangsuEducationDepartment[2023]No.11 Project of 'Nursing Science' Funded by the 4th Priority Discipline Development Program of Jiangsu Higher Education Institutions; JSHD2022046 Open project of Jiangsu Health Development Research Center
Contributed Indexing:
Keywords: acute liver failure; hepatitis E virus; machine learning; risk prediction
Entry Date(s):
Date Created: 20250509 Date Completed: 20250510 Latest Revision: 20250522
Update Code:
20250523
DOI:
10.1111/liv.70129
PMID:
40344287
Database:
MEDLINE
Weitere Informationen
Background and Aims: Early identification of patients with acute hepatitis E (AHE) who are at high risk of progressing to hepatitis E virus-related acute liver failure (HEV-ALF) is crucial for enabling timely monitoring and intervention. This multicentre retrospective cohort study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of HEV-ALF in hospitalised patients with AHE in tertiary care settings.
Methods: The study cohort included patients admitted to seven tertiary medical centers in Jiangsu, China, between 01 January 2018 and 31 December 2024. Multiple ML algorithms were applied for feature selection and model training. The predictive performance of the models was evaluated in terms of discrimination, calibration and clinical net benefit. The interpretability of the final model was enhanced using the SHapley Additive exPlanations.
Results: A total of 1912 participants were included in the study. Ten ML models were developed based on seven consensus-selected baseline features, with the survival gradient boosting machine (GBM) demonstrating superior performance compared to the traditional Cox proportional hazards regression model and other relevant models or scores. The GBM model achieved a Harrell's concordance index of 0.853 (95% CI: 0.791-0.914) in the external validation set. To facilitate clinical application, the GBM model was interpreted globally and locally and deployed as a web-based tool using the Streamlit-Python framework.
Conclusions: The GBM model demonstrated excellent performance in predicting HEV-ALF risk in hospitalised patients with AHE, offering a promising tool for clinical decision-making.
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