Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning

Abstract

Objective: This study investigates prehospital delays in recurrent Acute Ischemic Stroke (AIS) patients, aiming to identify key factors contributing to these delays to inform effective interventions.

Methods: A retrospective cohort analysis of 1419 AIS patients in Shenzhen from December 2021 to August 2023 was performed. The study applied the Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP) for identifying determinants of delay.

Results: Living with others and lack of stroke knowledge emerged as significant risk factors for delayed hospital presentation in recurrent AIS patients. Key features impacting delay times included residential status, awareness of stroke symptoms, presence of conscious disturbance, diabetes mellitus awareness, physical weakness, mode of hospital presentation, type of stroke, and presence of coronary artery disease.

Conclusion: Prehospital delays are similarly prevalent among both recurrent and first-time AIS patients, highlighting a pronounced knowledge gap in the former group. This discovery underscores the urgent need for enhanced stroke education and management.

Practice implication: The similarity in prehospital delay patterns between recurrent and first-time AIS patients emphasizes the necessity for public health initiatives and tailored educational programs. These strategies aim to improve stroke response times and outcomes for all patients.

Keywords: Prehospital delay; Recurrent stroke; SHAP values; Stroke awareness; XGBoost.