Ischemic stroke is characterized by high incidence, high disability rate, and high recurrence rate. Stroke recurrence is a complex pathological phenomenon involving multiple factors. Logistic regression analysis is widely used in traditional medical statistical analysis. However, this method mainly investigates the net effect of single factors using large sample sizes. The fsQCA method is a more suitable means of analyzing the configuration effect of risk factors affecting cerebral infarction recurrence, because it is based on holism and considers interaction effects among the risk factors.

The purpose of this study was to explore the risk factors of cerebral infarction recurrence using fsQCA, to reveal the configurations that resulted in cerebral infarction recurrence, and to provide novel ideas for the prevention of recurrence.

A total of 155 cerebral infarction patients admitted to the Department of Neurology of a tertiary class hospital in Shandong province from June 2020 to June 2021 were selected to participate. The demographic characteristics and risk factors associated with recurrence of cerebral infarction were collected, and participants were followed for one year.

The comparative analysis was performed by fuzzy-set (fsQCA 3.0) software. We conducted a configuration analysis of the risk factors of cerebral infarction recurrence and generated eight different configurations by analyzing various combinations of eight risk factors.

This study presented results different from those reported in studies using linear regression models. It took into consideration the configurational effect of each conditional variable on cerebral infarction recurrence. To prevent such recurrences, medical workers should evaluate the combination of multiple influencing factors, instead of focusing on a single factor.