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

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.