Predicting tigecycline-related adverse events in infected patients: a machine learning approach with clinical interpretability.

BACKGROUND

Tigecycline (TGC), while effective against multidrug-resistant infections, is limited by hepatotoxicity and coagulation disorders, yet lacks robust predictive tools.

METHODS

We developed an online dynamic nomogram to assess these adverse events using retrospective data from 2,553 TGC-treated patients (2020-2025). Seventy-seven clinical features were analyzed using Boruta and the Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection. Seven machine learning (ML) models were evaluated via ten-fold cross-validation, as well as Receiver Operating Characteristic (ROC) curve and calibration curves, with SHapley Additive exPlanations (SHAP) analysis for interpretability and an online dynamic nomogram for clinical translation.

RESULTS

Logistic regression (LR) outperformed other algorithms, achieving Area Under the ROC Curve (AUC) values of 0.800 (95% CI: 0.727-0.874) for hepatotoxicity and 0.755 (95% CI: 0.665-0.845) for coagulation dysfunction. Independent risk factors for liver injury included prolonged treatment duration, high dosage, ICU admission, hepatitis B virus (HBV) infection, and elevated baseline levels of lactate dehydrogenase (LDH) and gamma-glutamyl transferase (GGT). Risk factors for coagulation dysfunction included extended treatment duration, ICU admission, elevated baseline creatinine (Cr), sepsis, and septic shock. Notably, co-administration of meloxicillin and higher baseline red blood cell (RBC) levels appeared to be protective.

CONCLUSION

This study constructed an online dynamic nomogram with good discrimination and calibration, which can help to identify high-risk patients and assist clinicians in early risk stratification and individualized treatment planning.