Luthra Megha, Ohri Puneet, Kashyap Priyanka V, Maheshwari Sonam
Department of Community Medicine, SGRRIM and HS, Dehradun, Uttarakhand, India.
Department of Medicine, Neurology Unit, SGRRIM and HS, Dehradun, Uttarakhand, India.
Indian J Community Med. 2021 Jan-Mar;46(1):107-111. doi: 10.4103/ijcm.IJCM_465_20. Epub 2021 Mar 1.
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.
(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.
Hospital-based cross-sectional analytic, retrospective study.
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.
Percentages, proportions, Chi-square trends, linear regression, independent -test, and analysis of variance (ANOVA).
Mean age of 102 patients was 62.1 (±12.8 years). Stroke subtype associated with socioeconomic status (χ = 6.38775, = 0.0115) and stroke severity (χ = 18.98, = 0) and stroke severity associated with stroke subtype (χ = 9.79366, = 0.0018). Stroke subtype could be predicted by stroke severity and stroke severity by subtype, sex, and dyslipidemia (regression models). Independent -test revealed excessive alcohol intake was a significant predictor and one-way ANOVA revealed education was a significant predictor of severe stroke.
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.
2013年，中风在全球导致670万人死亡。在印度，过去十年中风的累积发病率为每年105 - 152/10万人口。印度缺乏关于中风亚型和严重程度预测因素的数据，因此开展了本研究。
（1）根据亚型和严重程度对中风患者进行分类。（2）确定危险因素与上述因素的关联。（3）通过危险因素预测亚型和严重程度。
基于医院的横断面分析性回顾研究。
采用预先设计、预先测试的半结构化问卷及标准工具（国立卫生研究院卒中量表评分），经机构伦理和研究委员会事先批准后获得知情同意。
百分比、比例、卡方趋势、线性回归、独立样本t检验和方差分析（ANOVA）。
102例患者的平均年龄为62.1岁（±12.8岁）。中风亚型与社会经济地位相关（χ² = 6.38775，P = 0.0115），与中风严重程度相关（χ² = 18.98，P = 0），中风严重程度与中风亚型相关（χ² = 9.79366，P = 0.0018）。中风亚型可通过中风严重程度预测，中风严重程度可通过亚型、性别和血脂异常预测（回归模型）。独立样本t检验显示过量饮酒是一个显著的预测因素，单因素方差分析显示教育程度是严重中风的一个显著预测因素。
中风亚型与较高的社会经济地位和严重中风显著相关。中风严重程度与出血性中风显著相关。中风亚型、性别、血脂异常、饮酒和教育程度可能是中风严重程度的预测因素。
