Machine learning-based prediction model for hypofibrinogenemia after tigecycline therapy.

ABSTRACT:

Background

In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.

Objective

We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.

Methods

This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CI

SUBMITTER: Zhu J 

PROVIDER: S-EPMC11451173 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature