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

ABSTRACT:

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

Logisti

SUBMITTER: Wu S 

PROVIDER: S-EPMC12669143 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature