Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.

Abstract

Background: Stroke-associated pneumonia (SAP) is a major cause of mortality following ischemic stroke (IS). However, existing predictive models for SAP often lack transparency and interpretability, limiting their clinical utility. This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability.

Methods: This retrospective study included patients diagnosed with IS and admitted to the Second Affiliated Hospital of Nanchang University between January and December 2019. Clinical data collected within 48 h of admission and SAP occurrences within 7 days were analyzed. Dimensionality reduction was performed using Least Absolute Shrinkage and Selection Operator regression, while data imbalances were addressed using synthetic minority oversampling technique. A BN model was trained using a hill-climbing algorithm and compared to logistic regression, decision trees, deep neural networks, and existing risk-scoring systems. Decision curve analysis was used to assess clinical usefulness.

Results: Of the 1252 patients, 165 (13.18%) patients had SAP within 7 days of admission. The BN model identified age, risk of pressure injury (PI), National Institutes of Health Stroke Scale (NIHSS) score, and C-reactive protein (CRP) as significant prognostic factors. The BN model achieved an area under the curve of 0.85(95% CI: 0.78-0.92) on the test set, outperforming other models and demonstrating a greater net benefit in clinical decision-making.

Conclusions: Age, risk of PI, NIHSS score, and CRP are significant predictors of SAP in IS patients. The interpretable BN model demonstrates superior predictive performance and interpretability, suggesting its potential as an effective and interpretable tool for clinical decision support in SAP risk assessment.