ArticleInternational journal of general medicine2025
Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Risk estimation for stroke-associated pneumonia in acute ischemic stroke: a nomogram-based approach.Arquivos de neuro-psiquiatria · 2026Article
- Construction and validation of a tracheostomy prediction model in mechanically ventilated stroke patients and the impact of early versus late tracheostomy on clinical outcomes: an IPTW-based analysis.Frontiers in neurology · 2026Article
- Laboratory characteristics and immune-inflammatory profiles ofFrontiers in pediatrics · 2026Article
- Systemic Inflammation Mediates the Association Between Admission Hyperglycemia and Pulmonary Infection or Prognosis in Acute Ischemic Stroke.Mediators of inflammation · 2026Article
- Predicting stroke-associated infection in acute ischemic stroke patients treated by thrombolysis.Frontiers in cellular neuroscience · 2026Article
- Predicting Early Dysphagia in Acute Ischemic Stroke Using an Explainable Machine Learning Model.International journal of general medicine · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
Purpose: Stroke-associated pneumonia (SAP), a critical complication of ischemic stroke, significantly worsens outcomes. Our aim was to identify SAP risk factors and develop a machine learning (ML) model for early risk stratification. Methods: This retrospective study analyzed 574 ischemic stroke patients, divided into training (75%) and testing (25%) sets. Nine ML models were trained using 10-fold cross-validation, with performance evaluated by accuracy, AUC-ROC, and F1-score. Key predictors were interpreted via SHAP analysis. An interactive web tool was developed using the optimal model. Results: SAP incidence was 32.4%. LightGBM demonstrated superior predictive performance (ranking score=54) without overfitting, identifying 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) as the top predictors. Conclusion: Our findings demonstrate that machine learning models exhibit strong predictive performance for SAP, with the LightGBM algorithm outperforming other approaches. The web-based prediction tool developed from this model provides clinicians with actionable insights to support real-time clinical decision-making.
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Registered trials
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