Development of stroke predictive model in community-dwelling population: A longitudinal cohort study in Southeast China

Qi Wang; Lulu Zhang; Yidan Li; Xiang Tang; Ye Yao; Qi Fang

doi:10.3389/fnagi.2022.1036215

Development of stroke predictive model in community-dwelling population: A longitudinal cohort study in Southeast China

Front Aging Neurosci. 2022 Dec 22:14:1036215. doi: 10.3389/fnagi.2022.1036215. eCollection 2022.

Authors

Qi Wang¹, Lulu Zhang², Yidan Li², Xiang Tang², Ye Yao^{1

3

4}, Qi Fang²

Affiliations

¹ Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
² Department of Neurology, First Affiliated Hospital of Soochow University, Suzhou, China.
³ National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
⁴ Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.

Abstract

Background: Stroke has been the leading cause of death and disability in the world. Early recognition and treatment of stroke could effectively limit brain damage and vastly improve outcomes. This study aims to develop a highly accurate prediction model of stroke with a list of lifestyle behaviors and clinical characteristics to distinguish high-risk groups in the community-dwelling population.

Methods: Participants in this longitudinal cohort study came from the community-dwelling population in Suzhou between November 2018 and June 2019. A total of 4,503 residents participated in the study, while stroke happened to 22 participants in the 2-year follow-up period. Baseline information of each participant was acquired and enrolled in this study. T-test, Chi-square test, and Fisher's exact test were used to examine the relationship of these indexes with stroke, and a prediction scale was constructed by multivariate logistic regression afterward. Receiver operating characteristic analysis was applied to testify to the prediction accuracy.

Results: A highly accurate prediction model of stroke was constructed by age, gender, exercise, meat and vegetarian diet, BMI, waist circumference, systolic blood pressure, Chinese visceral adiposity index, and waist-height ratio. Two additional prediction models for overweight and non-overweight individuals were formulated based on crucial risk factors, respectively. The stroke risk prediction models for community-dwelling and overweight populations had accuracies of 0.79 and 0.82, severally. Gender and exercise were significant predictors (χ² > 4.57, p < 0.05) in the community-dwelling population model, while homocysteine (χ² = 4.95, p < 0.05) was significant in the overweight population model.

Conclusion: The predictive models could predict 2-year stroke with high accuracy. The models provided an effective tool for identifying high-risk groups and supplied guidance for improving prevention and treatment strategies in community-dwelling population.

Keywords: ROC analysis; factors; predictive model; risk; stroke.