Abstract: Objective To investigate the modifiable risk factors associated with prehospital delay (PHD) among patients with acute ischemic stroke (AIS) and to perform population attributable fraction (PAF) analysis. Methods A retrospective analysis was conducted on 397 AIS patients admitted to the Neurology Department of a tertiary hospital in Fuyang from January 2023 to December 2025. These patients were divided into the PHD group (n=268) and non‑PHD group (n=129). Clinical data were compared, and PAF analysis was performed to assess the proportion of PHD attributable to specific factors. Results The proportions of age ≥65 years, male sex, experience awakenings after stroke onset, fail to call 120, have insufficient stroke knowledge, lack family accompaniment at the time of symptom onset, suffer from stroke during non-working hours, and reside in rural areas were significantly higher in the PHD group than in the non‑PHD group, while the admission NIHSS score was lower in the PHD group (all P<0.05). Multivariate logistic regression showed that failure to call 120 (OR=1.873), insufficient stroke knowledge (OR=2.345), lack of family accompaniment at symptom onset (OR=2.781), stroke occurrence during non-working hours (OR=2.089), and rural residence (OR=2.217) were independent influencing factors for PHD (all P<0.05). The overall PAF for the five modifiable factors was 38.614 (95% CI: 26.410%-51.705%). Based on PAF ranking, the most significant contributors were insufficient stroke knowledge, failure to call 120, lack of family accompaniment, rural residence, and stroke occurrence during non-working hours. Sensitivity analysis, with PHD thresholds adjusted to 3.0 and 6.0 h, revealed no substantial change in the ranking of PAF contributors. Conclusion These five factors are independent modifiable risk factors for PHD among AIS patients. Insufficient stroke knowledge and failure to call 120 were identified as core population-level targets for prevention.

Key words: Acute ischemic stroke, Prehospital delay, Risk factors, Population attributable fraction analysis