Predicting pneumonia algorithm in stroke patients
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…
How strong is the evidence?
Evidence Hub reads this as a case-control, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 812 participants or records.
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