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

Jianping Zhu1, Rui Zhao1, Zhenwei Yu1

  • 1Pharmacy Department, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310020, China.

BMC Medical Informatics and Decision Making
|October 4, 2024
PubMed
Summary

This study identifies risk factors for hypofibrinogenemia (HF) during tigecycline (TGC) treatment. Machine learning models predict HF risk and stratify patient survival, aiding clinical decision-making.

Keywords:
HypofibrinogenemiaInfluencing factorsMachine learningPrediction modelsSurvival modelTigecycline

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Area of Science:

  • Pharmacology and Clinical Medicine
  • Medical Informatics and Machine Learning

Background:

  • Hypofibrinogenemia (HF) is a significant adverse event associated with tigecycline (TGC) treatment.
  • The observed incidence of TGC-associated HF in clinical practice is higher than manufacturer-reported probabilities.

Purpose of the Study:

  • To identify key risk factors contributing to tigecycline-induced hypofibrinogenemia.
  • To develop predictive and survival models for TGC-associated HF and its temporal occurrence.

Main Methods:

  • A retrospective cohort study of 222 patients treated with TGC.
  • Binary logistic regression screened independent factors for TGC-associated HF.
  • Extreme Gradient Boosting (XGBoost) and Random Survival Forest (RSF) models were developed and validated.

Main Results:

  • Nine independent factors predicting TGC-associated HF were identified.
  • The XGBoost model demonstrated good predictive performance (AUC=0.792) and clinical utility.
  • The RSF model accurately stratified patient risk (C-index=0.746), showing significant survival differences between risk groups.

Conclusions:

  • The XGBoost model effectively predicts the risk of tigecycline-associated hypofibrinogenemia.
  • The RSF model provides valuable risk stratification for patients receiving TGC.
  • These models offer significant clinical value for mitigating TGC therapy risks.