A nomogram for predicting the risk of tigecycline-associated drug-induced liver injury in a Chinese population.

The aim of this study was to identify risk factors more comprehensively and develop the first nomogram for predicting tigecycline-associated drug induced liver injury (DILI) in a Chinese population. Patients who underwent tigecycline treatment from January 1, 2021, to July 31, 2024, at the Affiliated Hospital of Jining Medical University were included in this retrospective study. Candidate variables were selected using least absolute shrinkage and selection operator (Lasso) regression and support vector machine recursive feature elimination (SVM-RFE), followed by univariate and multivariate logistic regression to identify independent risk factors, which were visualized in a nomogram. Nomogram model performance was evaluated via the area under the receiver operating characteristic curve (AUC). A total of 357 patients were enrolled, including 73 patients (20.4%) diagnosed with DILI, and 284 patients (79.6%) without. Fourteen intersected variables were screened through Lasso regression and SVM-RFE method. Seven variables were identified as independent risk factors and were used to construct prediction nomogram model. The AUC values of 0.82 (95% CI: 0.76-0.88) in the training cohort and 0.80 (95% CI: 0.70-0.89) indicated the nomogram model had satisfactory prediction ability. Furthermore, decision curve analysis (DCA) revealed that the nomogram provided a significant net benefit in the identifying patients at high risk of tigecycline-associated DILI. This study was the first to identify patients treated with voriconazole, with a history of malignant tumors, in a state of septic shock, and with intra-abdominal infections were at a significantly elevated risk of developing tigecycline-associated DILI. The constructed nomogram demonstrated a high level of accuracy, showcasing substantial potential to aid clinicians in pinpointing risk factors and implementing preventive measures.