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Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.

Eun-Tae Jeon1, Seung Jin Jung2, Tae Young Yeo1

  • 1Department of Neurology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea.

Frontiers in Neurology
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict outcomes for atrial fibrillation (AF) stroke patients. These models identify high-risk individuals, improving prognostic prediction for better stroke management.

Keywords:
atrial fibrilationischemic strokemachine learningoutcomeprediction model

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Area of Science:

  • Computational neurology
  • Cardiovascular research
  • Medical informatics

Background:

  • Atrial fibrillation (AF) is linked to adverse outcomes in stroke patients.
  • Accurate prognostic prediction is crucial for early management of AF-related strokes.

Purpose of the Study:

  • To develop and validate machine learning models for predicting short-term outcomes in AF-related stroke.
  • To identify key prognostic factors contributing to stroke outcomes in AF patients.

Main Methods:

  • Utilized two independent datasets (K-ATTENTION and KUSR) for internal and external validation.
  • Developed and compared a logistic regression model with tree-based and multi-layer perceptron (MLP) machine learning models.
  • Employed the Area Under the Receiver Operating Characteristic Curve (AUROC) for performance evaluation and Shapley Additive Explanation (SHAP) for variable importance.

Main Results:

  • Machine learning models, particularly MLP, significantly outperformed logistic regression in predicting 3-month unfavorable functional status (AUROC 0.890 internal, 0.859 external).
  • Both ML models showed superior prediction for 3-month mortality in internal validation compared to logistic regression.
  • The initial National Institute of Health and Stroke Scale score was the most significant predictor for both unfavorable outcomes and mortality.

Conclusions:

  • Explainable machine learning models offer reliable prediction of short-term outcomes for AF-related stroke.
  • These models effectively identify high-risk patients, aiding in targeted interventions and improved stroke care.