English Stephen W, Rabinstein Alejandro A, Mandrekar Jay, Klaas James P
Department of Neurology, Mayo Clinic, Rochester, Minnesota.
Department of Neurology, Mayo Clinic, Rochester, Minnesota.
J Stroke Cerebrovasc Dis. 2018 Apr;27(4):919-925. doi: 10.1016/j.jstrokecerebrovasdis.2017.10.036. Epub 2017 Dec 6.
Although prehospital stroke notification has improved stroke treatment, incorporation of these systems into existing infrastructure has resulted in new challenges. The goal of our study was to design an effective prehospital notification system that allows for early and accurate identification of patients presenting with acute stroke.
We conducted a retrospective single-center cohort study of patients presenting with suspicion of acute stroke from 2014 to 2015. Data recorded included patient demographics, time of symptom onset, Cincinnati Prehospital Stroke Scale (CPSS) score, Glasgow Coma Scale score, National Institutes of Health Stroke Scale (NIHSS) score, emergency medical services (EMS) impression, acute stroke pager activation, acute intervention, and discharge diagnosis. Univariate logistic regression was performed with discharge diagnosis of stroke as the end point.
A total of 130 patients were included in the analysis; 96 patients were discharged with a diagnosis of stroke or transient ischemic attack. Both NIHSS and the presence of face, arm and speech abnormalities on CPSS were significantly higher in patients with stroke (P < .05). EMS correctly recognized stroke in 77.1% of cases but falsely identified stroke in 85.3% of negative cases. CPSS identified 75% of acute stroke cases, but specificity was poor at only 20.6%. All patients receiving intervention had acute stroke pager activation in Emergency Department.
Prehospital stroke notification systems utilizing EMS impressions and stroke screening tools are sensitive but lack appropriate specificity required for modern acute stroke systems of care. Better solutions must be explored so that prehospital notification can keep pace with advances in acute stroke treatment.
尽管院前卒中通知改善了卒中治疗,但将这些系统整合到现有基础设施中带来了新的挑战。我们研究的目的是设计一种有效的院前通知系统,以便早期准确识别急性卒中患者。
我们对2014年至2015年疑似急性卒中患者进行了一项回顾性单中心队列研究。记录的数据包括患者人口统计学信息、症状发作时间、辛辛那提院前卒中量表(CPSS)评分、格拉斯哥昏迷量表评分、美国国立卫生研究院卒中量表(NIHSS)评分、紧急医疗服务(EMS)诊断、急性卒中寻呼机激活、急性干预和出院诊断。以卒中出院诊断为终点进行单因素逻辑回归分析。
共有130例患者纳入分析;96例患者出院诊断为卒中或短暂性脑缺血发作。卒中患者的NIHSS评分以及CPSS上的面部、手臂和言语异常情况均显著更高(P < 0.05)。EMS在77.1%的病例中正确识别出卒中,但在85.3%的阴性病例中错误识别为卒中。CPSS识别出75%的急性卒中病例,但特异性较差,仅为20.6%。所有接受干预的患者在急诊科均激活了急性卒中寻呼机。
利用EMS诊断和卒中筛查工具的院前卒中通知系统敏感度较高,但缺乏现代急性卒中护理系统所需的适当特异性。必须探索更好的解决方案,以便院前通知能够跟上急性卒中治疗的进展。