Can We Predict Poor Outcome in Stroke Patients Without Imaging Data? A Decision Tree Analysis of Stroke Patients

Abstract

Objective: The aging world population and increased cardiovascular risk factors contribute to stroke and stroke related morbidity. In this study, we aimed to analyze predictors of increased morbidity of ischemic stroke patients in a single stroke unit. Materials and Methods: Stroke patients recorded in the Istanbul University Stroke Registry between 2014 and 2020 were included and decision tree analyses [chi-squared automatic interaction detection (CHAID) method] were conducted. Gender, diabetes, hypertension, previous stroke, ischemic heart disease, hyperlipidemia, diagnosis of pneumonia during hospitalization in the stroke unit, and atrial fibrillation were determined as possible indicators for poor clinical outcomes. Results: We included 881 patients with ischemic stroke in the study according to the inclusion and exclusion criteria. The mean age of patients was 66.5+-14.4 years and 59% of the patients were male. CHAID analysis revealed that the most important factor for predicting modified Rankin Scale (mRS) score >3 is pneumonia. In patients with mRS score >3 and without pneumonia; hypertension and hyperlipidemia were found to be risk factors for poor functional outcome. Conclusion: Preventative measures in stroke patients should not be limited to secondary prophylaxis of stroke. Avoiding infections in the acute phase plays an essential role in achieving favorable clinical outcomes.

Keywords

• ischemic stroke
• decision tree
• chaid
• prognosis
• stroke registry