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A Practical Nomogram for Predicting the Bleeding Risk in Patients with a History of Myocardial Infarction Treating with Aspirin.

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Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning.

Tenggao Chen1, Wanlin Lei2, Maofeng Wang2

  • 1Department of Colorectal Surgery, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People's Republic of China.

Risk Management and Healthcare Policy
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PubMed
Summary

This study developed a machine learning model to predict internal bleeding risk in elderly aspirin users. The model, using six clinical variables, aids clinicians in managing patient treatment and care.

Keywords:
aspirinbleedingextreme gradient boostinghaemorrhagenomogrampredictive model

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Area of Science:

  • Medical Informatics
  • Gerontology
  • Cardiovascular Medicine

Background:

  • Aspirin is widely prescribed for elderly individuals, increasing their risk of gastrointestinal bleeding.
  • Predictive models are needed to identify high-risk patients for proactive management.

Purpose of the Study:

  • To develop and validate a machine learning-based predictive model for internal bleeding risk in elderly aspirin users.
  • To identify key clinical factors associated with bleeding events in this population.

Main Methods:

  • Retrospective analysis of 26,030 elderly aspirin users (aged >65).
  • Development of prediction models using Least Absolute Shrinkage and Selection Operator (LASSO) regression, Extreme Gradient Boosting (XGBoost), and multivariate logistic regression.
  • Model performance evaluated using Area Under the Curve (AUC), calibration curves, and decision curve analysis (DCA).

Main Results:

  • The XGBoost model demonstrated superior performance with an AUC of 0.842 (training) and 0.820 (testing).
  • Key predictors identified include Hemoglobin (HGB), Platelet count (PLT), previous bleeding history, gastric ulcer, cerebral infarction, and tumor.
  • A nomogram was developed based on these six variables, showing good discriminatory and calibration performance.

Conclusions:

  • A robust predictive model for bleeding risk in elderly aspirin users was successfully developed.
  • The model and associated nomogram can assist clinicians in risk stratification and personalized treatment decisions.