Brown Aliza, Onteddu Sanjeeva, Sharma Rohan, Kapoor Nidhi, Nalleballe Krishna, Balamurugan Appathurai, Gundapaneni Sukumar, Bianchi Nicolas, Skinner Robert, Culp William
Department of Neurology, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.
Department of Radiology, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR.
J Neurol Neurosurg Psychiatry Res. 2019 Jan-Jun;1(1). Epub 2019 Jan 17.
Delays in recognizing stroke during pre-hospital emergency medical system (EMS) care may affect triage and transport time to an appropriate stroke ready hospital and may preclude patients from receiving time dependent treatment. All EMS transports in a large urban area in the stroke belt were evaluated for transport destinations, triage and transport time and stroke recognition following distribution ofan educational training video to local EMS services.
Following video training, local paramedics will improve stroke recognition and shorten triage and transport time to appropriate stroke centers of care.
A training module (<10 min) containing a stroke triage scenario, instruction on the Cincinnati Prehospital Stroke Score (CPSS) and the Los Angeles Prehospital Stroke Score (LAPSS) and 'where to transport' stroke patients was distributed and viewed by 96 paramedics. Data was collected from February to October 2016. Stroke recognition was determined from one primary stroke center (PSC) hospital's confirmation of EMS delivered patients (Site A). Yearly stroke recognition percentages of 44% from Site A in 2014 were used as baseline.
A total of 34,833 emergency 911 response transports were made with a total of 502 (1.4%) suspected strokes identified by paramedics. Median [IQR] triage and transport time for stroke transports was 33 [27-41] min. The PSC hospitals received a 5% increase in stroke transports and non-specific care facilities decreased by 7%. From 8,554 transports to site A (PSC) confirmed strokes totalled 107 transports with 139 suspected strokes by paramedics. Of these transports, 60 were correctly identified by paramedics (positive predictive value of 43%, sensitivity of 56%). By the second month following training, recognition percentages increased from baseline to 64%. At five months, percentages of correct stroke identification had dropped to 36%.
Video based training improved stroke recognition by an additional 19%, but continual monthly or quarterly training is recommended for maintenance of increased stroke recognition.
在院前急救医疗系统（EMS）护理过程中，识别中风的延迟可能会影响分诊和转运至合适的具备中风治疗条件医院的时间，并且可能使患者无法接受具有时间依赖性的治疗。在向当地EMS服务机构分发了一段教育培训视频后，对中风带一个大城市地区的所有EMS转运情况进行了评估，包括转运目的地、分诊和转运时间以及中风识别情况。
经过视频培训后，当地护理人员将提高中风识别能力，并缩短分诊和转运至合适中风护理中心的时间。
一个包含中风分诊场景、辛辛那提院前中风评分（CPSS）和洛杉矶院前中风评分（LAPSS）的指导以及中风患者“转运地点”的培训模块（<10分钟）被分发给96名护理人员并供其观看。数据收集于2016年2月至10月。中风识别情况由一家主要中风中心（PSC）医院对EMS送来患者的确认来确定（A地点）。2014年A地点44%的年度中风识别率被用作基线。
共进行了34,833次911紧急响应转运，护理人员共识别出502例（1.4%）疑似中风患者。中风转运的中位[四分位间距]分诊和转运时间为33[27 - 41]分钟。PSC医院接收的中风转运增加了5%，非特定护理设施减少了7%。在送往A地点（PSC）的8554次转运中，护理人员确认的中风患者共有107例转运，疑似中风患者139例。在这些转运中，护理人员正确识别出60例（阳性预测值为43%，敏感性为56%）。到培训后的第二个月，识别率从基线提高到了64%。在五个月时，正确中风识别的比例降至36%。
基于视频的培训使中风识别率额外提高了19%，但建议每月或每季度进行持续培训以维持提高后的中风识别率。
