Predictors of stroke subtype and severity in patients of a tertiary care hospital, Dehradun - Megha Luthra; Puneet Ohri; Priyanka V Kashyap; Sonam Maheshwari (Wolters Kluwer Medknow Publications, 2021)
Context: Stroke caused 6.7 million deaths worldwide in 2013. In India, the cumulated incidence of stroke was 105-152/100,000 persons per year in last decade. Dearth of data on predictors of stroke subtype and severity in India lead to this study. Aims: (1) To categorize presenting stroke patients by subtype and severity. (2) To establish association of risk factors with above. (3) To predict subtype and severity by risk factors. Settings and Design: Hospital-based cross-sectional analytic, retrospective study. Subjects and Methods: A predesigned, pretested, semi-structured questionnaire with standard tool (National Institute of Health Stroke Scale Score), informed consent after prior approval of institutional ethics and research committees. Statistical Analysis Used: Percentages, proportions, Chi-square trends, linear regression, independent t-test, and analysis of variance (ANOVA). Results: Mean age of 102 patients was 62.1 (±12.8 years). Stroke subtype associated with socioeconomic status (χ2 = 6.38775, P = 0.0115) and stroke severity (χ2 = 18.98, P = 0) and stroke severity associated with stroke subtype (χ2 = 9.79366, P = 0.0018). Stroke subtype could be predicted by stroke severity and stroke severity by subtype, sex, and dyslipidemia (regression models). Independent t-test revealed excessive alcohol intake was a significant predictor and one-way ANOVA revealed education was a significant predictor of severe stroke. Conclusions: Stroke subtype is significantly associated with higher socioeconomic status and severe stroke. Stroke severity is significantly associated with hemorrhagic stroke. Stroke subtype, sex, dyslipidemia, alcohol intake, and education may act as predictors of stroke severity.