| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Development and validation of a machine learning model for hospital-acquired bacterial infection prediction in acute-on-chronic liver failure
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Jing-Yi Chen, Xin-Yi Chen, Yi-Wen Xie, Yu-Wei Chen, Xiao-Wei Shi, Ning Lin, Chen-Jie Huang, Rui Luo, Xiao-Qing Lu, Xiao-Xiao Chen, Jian Wu, Hai-Jun Huang, Qiang Zhu, Lan-Juan Li, Jiong Yu and Hong-Cui Cao |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Prevention and Control of Emerging and Major Infectious Diseases-National Science and Technology Major Project |
2025ZD01906501 |
| the Fundamental Research Funds for the Central Universities |
2025ZFJH03 |
| Central Guidance Fund for Local Science and Technology Development |
2024ZY01054 |
| High-level Personnel Cultivating Project of Zhejiang Province |
2023R5243 |
|
| Corresponding Author |
Hong-Cui Cao, State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou 310003, Zhejiang Province, China. hccao@zju.edu.cn |
| Key Words |
Acute-on-chronic liver failure; Hospital-acquired bacterial infection; Machine learning; Prediction model; Risk stratification |
| Core Tip |
This multicenter retrospective study developed and externally validated an interpretable admission-time machine learning model to predict hospital-acquired bacterial infections in patients with acute-on-chronic liver failure. A 12-variable Gradient Boosting Decision Tree model demonstrated robust discrimination in both derivation and external validation cohorts, outperformed conventional liver disease severity scores, and remained stable across calibration, decision-curve, time-dependent, cumulative incidence, and competing-risk analyses. By providing individualized risk estimates and SHapley Additive exPlanations-based interpretations through a web application, this model may support early risk stratification and targeted infection surveillance during hospitalization. |
| Citation |
Chen JY, Chen XY, Xie YW, Chen YW, Shi XW, Lin N, Huang CJ, Luo R, Lu XQ, Chen XX, Wu J, Huang HJ, Zhu Q, Li LJ, Yu J, Cao HC. Development and validation of a machine learning model for hospital-acquired bacterial infection prediction in acute-on-chronic liver failure. World J Gastroenterol 2026; In press
|
| PDF |
123339-in-press.pdf
|
