Development and validation of a risk prediction model of prehospital delay in patients with acute ischemic stroke

Prehospital delay is the primary cause of low reperfusion treatment rates among patients with acute ischemic stroke (AIS). Healthcare providers lack tools to identify high-risk patients, and thus predictive models need to be developed to screen high-risk populations, aiming to reduce the incidence of delayed medical care among AIS patients.

This study was conducted to investigate the factors influencing delayed medical care among AIS patients and construct and validate a predictive model for the risk of prehospital delay.

By conducting convenience sampling, 348 AIS patients admitted to the neurology department of a tertiary hospital in China between September 2024 and June 2025 were enrolled. The patients were divided into a prehospital delay group and Non-prehospital delay group based on whether the time from the onset of symptoms to hospital admission exceeded 4.5 h (the critical time window for reperfusion therapy). Univariate and logistic regression analyses were performed to identify factors influencing delayed medical care. Using the R software, a risk prediction model incorporating a delay-to-treat line chart was constructed for AIS patients, followed by internal validation.

Among 348 AIS patients, 281 experienced prehospital delay, resulting in a delay rate of 80.8%. Logistic regression analysis identified education, History of cerebral infarction, place of onset, Subsequent measures, and National Institutes of Health Stroke Scale (NIHSS) score as independent risk factors. The Hosmer-Lemeshow test yielded a χ ² value of 9.84 (p = 0.277). The area under the ROC curve (AUC) of the model was 0.87, with an optimal cutoff value of 0.625, sensitivity of 0.79, and specificity of 0.84. The calibration curves demonstrated good agreement between the predicted and actual incidence rates, whereas the decision curves confirmed the clinical net benefit of the model.

The constructed risk prediction model shows strong predictive efficacy and can help clinical nurses rapidly identify high-risk individuals for prehospital delay in AIS patients and implement targeted interventions. This approach improves healthcare-seeking behavior among patients and decreases the risk of prehospital delay.