ArticleFrontiers in neurology2025
Predicting pneumonia algorithm in stroke patients.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Aspirated thrombus weight and volume are not associated with improvements in hemodynamics after large-bore mechanical thrombectomy in pulmonary embolism.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Limitations of Retrospective Machine Learning Models for Predicting Tracheostomy After Cardiac Surgery.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pneumonia is a serious complication of stroke, particularly in patients with dysphagia during inpatient rehabilitation, as it significantly increases morbidity, prolongs hospital stays, and impairs functional recovery. Early identification of patients at risk for pneumonia is crucial for improving outcomes and reducing post-stroke complications. This study aimed to develop a comprehensive algorithm for predicting post-stroke pneumonia risk by integrating clinical assessments of defense mechanisms against pneumonia. Methods: This case-control study enrolled stroke patients at a single tertiary hospital and followed them for 4 weeks to assess pneumonia incidence. A total of 812 patients aged 20 years or older with ischemic or hemorrhagic stroke and signs of dysphagia were screened. Of these, 484 were excluded based on the following criteria: inability to maintain a sitting posture with back support, dyspnea requiring oxygen supplementation, concurrent aspiration pneumonia before enrollment, infectious diseases requiring isolation, and refusal to participate. Final cohort of 328 patients was enrolled. All participants underwent evaluations, including a videofluoroscopic swallowing study (VFSS), a modified cough reflex test (mCRT), and assessments of nutritional status (serum albumin) and cognitive function [Mini-Mental State Examination (MMSE)]. Pneumonia was diagnosed using the Mann criteria, and predictive factors were analyzed using univariate logistic regression and classification and regression tree (CART) analysis. Results: Among 328 participants, 28 (8.5%) developed pneumonia. Significant predictors included tracheostomy status (OR 9.34), VFSS-confirmed aspiration (OR 8.21) and bilateral stroke lesions (OR 5.91). CART analysis revealed tracheostomy, VFSS-confirmed aspiration, cough frequency, albumin levels, and MMSE scores as key predictors. The algorithm demonstrated a predictive accuracy of 92.7% with an AUC of 0.89 (95% CI: 0.82-0.95). Conclusion: This study developed a highly accurate predictive algorithm for post-stroke pneumonia, emphasizing the role of defense mechanisms against pneumonia. Implementing this algorithm in clinical practice could enable early preventive measures, reduce pneumonia incidence, and improve patient outcomes.
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