The aim of the study was to investigate the main factors for prehospital delay in patients experiencing 1st-ever acute ischemic stroke (AIS), and to develop a predictive model based on these factors to inform effective interventions. A multicenter retrospective observational cohort study enrolled 699 patients with 1st-ever AIS admitted to 2 tertiary hospitals and 1 secondary hospital in Chongqing between August 2023 and April 2024. Independent predictors of prehospital delay were identified by univariable and multivariable logistic regression analyses, and a predictive nomogram was constructed based on the results of the analyses. To verify the model’s validity and assess its accuracy, we plotted the receiver operating characteristic curves and calculated the area under the curve. A total of 699 patients with 1st-ever AIS experienced prehospital delay in this study, among whom 490 cases were recorded, with an incidence rate of 70.10%. Through univariate and multivariate logistic regression analyses, a total of 10 variables were identified as independent risk factors for prehospital delay in 1st-ever AIS patients. The 5 most influential risk factors for prehospital delay, in descending order of contribution, were: transportation delay due to traffic congestion (odds ratio OR = 25.652, 95% CI: 6.957–94.591), resting as the initial response to symptoms (OR = 9.390, 95% CI: 3.920–22.498), presence of unconsciousness or fainting (OR = 4.502, 95% CI: 1.281–15.842), an onset-to-hospital distance of 6 to 10 km (OR = 2.462, 95% CI: 1.022–5.927), and symptoms 1st noticed by someone else (OR = 2.445, 95% CI: 1.169–5.115). The nomogram demonstrated good discrimination and calibration for predicting prehospital delay in 1st-ever AIS, suggesting its potential as a practical tool for identifying high-risk individuals. This tool could facilitate the implementation of targeted interventions aimed at shortening prehospital time and improving clinical outcomes.
Chen et al. (Fri,) studied this question.