急性缺血性脑卒中患者院前延迟的可干预危险因素识别及人群归因风险分析

doi:10.12183/j.scjpm.2026.0801

摘要/Abstract

摘要： 目的探讨急性缺血性脑卒中（AIS）患者院前延迟（PHD）的可干预危险因素及人群归因分析。方法回顾性分析2023年1月至2025年12月阜阳某三级医院神经内科AIS患者资料,分为PHD组（n=268）与非PHD组（n=129）,比较临床资料并进行人群归因分数（PAF）分析。结果 PHD组年龄≥65岁、男性、醒后卒中、未呼叫120、卒中知识知晓不足、发病时无家属陪伴、非工作时间发病、居住地农村占比均高于非PHD组,入院NIHSS评分低于非PHD组（均P<0.05）。多因素logistic回归显示,未呼叫120（OR=1.873）、卒中知识知晓不足（OR=2.345）、发病时无家属陪伴（OR=2.781）、非工作时间发病（OR=2.089）、居住地农村（OR=2.217）均为PHD独立影响因素（P<0.05）。5项可干预因素总体PAF为38.614（95% CI：26.410~51.705）%,按PAF排序依次为卒中知识知晓不足>未呼叫120>发病时无家属陪伴>居住地农村>非工作时间发病。敏感性分析显示,更换PHD切点为3.0、6.0 h,PAF排序无明显改变。结论 5项因素均为AIS患者PHD的可干预独立影响因素,卒中知识知晓不足与未呼叫120为人群层面核心防控靶点。

关键词: 急性缺血性脑卒中, 院前延迟, 危险因素, 人群归因分数分析

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

中图分类号:

R181.3

王文浩, 马丽婕, 康燕. 急性缺血性脑卒中患者院前延迟的可干预危险因素识别及人群归因风险分析[J]. 华南预防医学, 2026, 52(7): 801-806.

Wang Wenhao, Ma Lijie, Kang Yan. Identification of modifiable risk factors and population attributable risk analysis for prehospital delay in patients with acute ischemic stroke[J]. South China Journal of Preventive Medicine, 2026, 52(7): 801-806.

参考文献

[1] 唐春花, 郭露, 张莉莉. 2025年全球卒中报告数据解读: 卒中疾病负担的梯度演变与精准治理[J]. 诊断学理论与实践, 2025, 24(5): 485-497.
[2] Tu WJ, Wang LD.Special writing group of China stroke surveillance report. China stroke surveillance report 2021[J]. Mil Med Res, 2023, 10(1): 33.
[3] Ye Q, Zhai F, Chao B, et al.Rates of intravenous thrombolysis and endovascular therapy for acute ischaemic stroke in China between 2019 and 2020[J]. Lancet Reg Health West Pac, 2022, 21: 100406.
[4] Su Y, Qi W, Yu Y, et al.Analysis of prehospital delay in acute ischaemic stroke and its influencing factors: A multicentre prospective case registry study in China[J]. Stroke Vasc Neurol, 2025, 10(5): 637-647.
[5] Yuan J, Lu ZK, Xiong X, et al.Age and geographic disparities in acute ischaemic stroke prehospital delays in China: A cross-sectional study using national stroke registry data[J]. Lancet Reg Health West Pac, 2023, 33: 100693.
[6] Royan R, Stamm B, Lin T, et al.Disparities in emergency medical services use, prehospital notification, and symptom onset to arrival in patients with acute stroke[J]. Circulation, 2024, 150(18): 1428-1440.
[7] 中华医学会, 中华医学会杂志社, 中华医学会全科医学分会, 等. 缺血性卒中基层诊疗指南(2021年)[J]. 中华全科医师杂志, 2021, 20(9): 927-946.
[8] Eskioglou E, Huchmandzadeh Millotte M, Amiguet M, et al.National Institutes of Health Stroke Scale zero strokes[J]. Stroke, 2018, 49(12): 3057-3059.
[9] Antipova D, Eadie L, Macaden A, et al.Diagnostic accuracy of clinical tools for assessment of acute stroke: A systematic review[J]. BMC Emerg Med, 2019, 19(1): 49.
[10] 李玲玲, 任李媛, 马佩芬, 等. 急性缺血性脑卒中溶栓代理决策者决策困境的研究进展[J]. 中华急危重症护理杂志, 2026, 7(4): 503-507.
[11] 阎纯, 龚媛, 黄平, 等. 急性缺血性脑卒中患者早期预后不良及预测模型构建[J]. 华南预防医学, 2023, 49(10): 1213-1217.
[12] 陈煜凌, 陈佳露, 冉根, 等. 急性缺血性脑卒中患者院前延迟研究进展[J]. 中国急救复苏与灾害医学杂志, 2026, 21(2): 282-286.
[13] 高蕊, 贺诗蕊, 蒋敏, 等. 老年急性缺血性脑卒中患者院前延迟风险预测模型的构建及验证[J]. 成都医学院学报, 2025, 20(5): 840-845.
[14] 蒋敏, 王辉, 李培培, 等. 急性缺血性脑卒中患者院前延迟现状及影响因素分析[J]. 成都医学院学报, 2024, 19(1): 146-152.
[15] 黄锐仪, 张翠云, 万婷, 等. 急性脑梗死患者静脉溶栓治疗决策现状及影响因素分析[J]. 岭南急诊医学杂志, 2026, 31(2): 188-191.
[16] 钟莉, 张可, 贺志宇, 等. 急性心肌梗死患者院前延迟率及影响因素的Meta分析[J]. 中华急危重症护理杂志, 2025, 6(2): 181-187.
[17] 夏慧, 姜爱钟, 林福军, 等. 醒后卒中静脉溶栓患者预后影响因素及其预测模型构建[J]. 脑与神经疾病杂志, 2026, 34(2): 85-90.
[18] 王萧逸, 闫妍, 王宁, 等. 2019年—2020年洛阳市急性缺血性脑卒中病人院前就医延迟情况及影响因素[J]. 中西医结合心脑血管病杂志, 2023, 21(5): 913-917.
[19] 吴鹏程, 李尚品, 叶泽鸿, 等. 中山市急性缺血性脑卒中患者就医延迟因素调查及干预措施研究[J]. 中国急救复苏与灾害医学杂志, 2024, 19(3): 364-367.
[20] 杨丽, 邝金珂, 高子寒, 等. 基于系统动力学的急性缺血性脑卒中患者就医决策延迟影响因素研究[J]. 军事护理, 2024, 41(4): 46-50.
[21] 刘洁, 徐芳芳, 姚伟根, 等. 急性缺血性脑卒中多模态CT检查延迟的多维度影响因素分析及预测模型构建[J]. 中华急诊医学杂志, 2026, 35(3): 379-385.
[22] 倪佃丽, 陈晓兵, 张广慧, 等. 急性缺血性卒中院前延迟风险预测模型的构建及评价[J]. 中国卒中杂志, 2025, 20(4): 428-434.
[23] 黄红艳, 高西西, 茅新蕾. 急性缺血性脑卒中患者就医延迟情况及影响因素与预防策略探讨[J]. 实用预防医学, 2025, 32(8): 949-953.