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Related Experiment Video

Updated: Sep 19, 2025

Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model
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Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation

Mengqi Xie1, Zhiying Liu1, Fangfang Dai1

  • 1The Second Clinical Medical College of Xinjiang Medical University, Xinjiang Uygur Autonomous Region, People's Republic of China.

International Journal of General Medicine
|June 18, 2025
PubMed
Summary

Machine learning accurately predicts stroke-associated pneumonia (SAP) risk. Key factors include immune markers and patient demographics, enabling early clinical decision-making with a new prediction tool.

Keywords:
ischemic strokemachine learningstroke-associated pneumonia

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Area of Science:

  • Neurology
  • Infectious Diseases
  • Computational Biology

Background:

  • Stroke-associated pneumonia (SAP) is a severe complication following ischemic stroke.
  • SAP significantly increases patient morbidity and mortality.
  • Early identification of patients at high risk for SAP is crucial for improved outcomes.

Purpose of the Study:

  • To identify key risk factors for stroke-associated pneumonia (SAP).
  • To develop and validate a machine learning (ML) model for early SAP risk stratification.
  • To create a practical tool for clinical decision support.

Main Methods:

  • Retrospective analysis of 574 ischemic stroke patients.
  • Training and testing of nine machine learning models using 10-fold cross-validation.
  • Evaluation of model performance using accuracy, AUC-ROC, and F1-score; predictor importance assessed via SHAP analysis.

Main Results:

  • SAP incidence was 32.4% in the study cohort.
  • The LightGBM algorithm showed superior predictive performance without overfitting.
  • Top predictors identified included Monocyte-to-lymphocyte ratio (MLR), Systemic Immune-Inflammation Index (SII), NIHSS score, age, Aggregate Index of Systemic Inflammation (AISI), and Platelet-to-lymphocyte ratio (PLR).

Conclusions:

  • Machine learning models, particularly LightGBM, demonstrate high accuracy in predicting SAP risk.
  • The identified risk factors provide valuable insights into SAP pathogenesis.
  • An interactive web-based prediction tool offers clinicians actionable insights for real-time decision-making.