A multivariate prediction model and its application in forecasting acute ischemic stroke: Protocol for a retrospective clinical study

Abstract

Background: Acute ischemic stroke (AIS) occurs due to brain ischemia as a result of thrombosis of a cerebral blood vessel. It is a common cerebral blood circulation disorder worldwide and an important cause of death and disability.Objective: This study aims to establish a prediction model of multiple single category indicators and a joint model, through which to plot multiple receiver operating characteristic curves and compare area under curve of the models so as to predict the occurrence of AIS, explore the pathogenesis of AIS, and provide reference for clinical diagnosis and treatment of AIS.Methods: A retrospective clinical study was conducted in a Level A tertiary hospital in Sichuan Province, China. The patients participated in this study were over 18 years of age and suffered from acute ischemic stroke. They were hospitalized in department of neurology from October 1, 2019 to September 30, 2022, and underwent coronary artery computed tomographic arteriography (CTA) and blood biomarker detection. We collected demographic information, CTA data and blood biomarker detection values of all these patients. Conclusion:Through analyzing the clinical data of high-risk groups, this study provides guidance for the prevention and treatment of AIS, and promote further research.