Development and external validation of a machine learning model for predicting tigecycline-associated drug-induced liver injury.

Authors

Yujing Zhang, Pei Ji, Danni Wang, Wen Li, Shumei Miao, Chunlai Feng, Tingting Sun, Yilei Zheng, Xiaolan Zhang, Yaxin Deng, Qian Qian, Wenkui Sun

Published in

iScience. Volume 29. Issue 8. Pages 116876. Aug 21, 2026. Epub Jul 27, 2026.

Abstract

Tigecycline (TGC) is widely used to treat severe multidrug-resistant infections but can cause drug-induced liver injury. Existing prediction studies frequently lack external validation and offer limited clinical interpretability. We performed a multicenter retrospective cohort study of 1,946 adult inpatients who received TGC at three tertiary hospitals in Jiangsu, China. Liver injury was defined by biochemical criteria, and causality was assessed with a structured clinical method. After variable selection and model comparison, seven routinely available predictors were retained: TGC duration, concomitant hepatotoxic medications, neurological disease as the principal admission diagnosis, trauma as the principal admission diagnosis, baseline hepatic impairment, alcohol drinking history, and hemodialysis. eXtreme Gradient Boosting (XGBoost) showed the best overall performance and maintained high sensitivity in external validation. We implemented the final explainable model as a web-based calculator to support early screening, monitoring prioritization, and clinical reassessment.

PMID:
42548806
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

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