Center for Stroke Research, Charité Universitätsmedizin Berlin, Hindenburgdamm 30, 12200 Berlin, Germany.
Stroke. 2012 Mar;43(3):776-81. doi: 10.1161/STROKEAHA.111.634980. Epub 2012 Jan 5.
Recent innovations such as CT installation in ambulances may lead to earlier start of stroke-specific treatments. However, such technically complex mobile facilities require effective methods of correctly identifying patients before deployment. We aimed to develop and validate a new dispatcher identification algorithm for stroke emergencies.
Dispatcher identification algorithm for stroke emergencies was informed by systematic qualitative analysis of the content of emergency calls to ambulance dispatchers for patients with stroke or transient ischemic attack (N=117) and other neurological (N=39) and nonneurological (N=51) diseases (Part A). After training of dispatchers, sensitivity and predictive values were determined prospectively in patients admitted to Charité hospitals by using the discharge diagnosis as reference standard (Part B).
Part A: Dysphasic/dysarthric symptoms (33%), unilateral symptoms (22%) and explicitly stated suspicion of stroke (47%) were typically identified in patients with stroke but infrequently in nonstroke cases (all <10%). Convulsive symptoms (41%) were frequent in other neurological diseases but not strokes (3%). Pain (26%) and breathlessness (31%) were often expressed in nonneurological emergencies (6% and 7% in strokes). Part B: Between October 15 and December 16, 2010, 5774 patients were admitted by ambulance with 246 coded with final stroke diagnoses. Sensitivity of dispatcher identification algorithm for stroke emergencies for detecting stroke was 53.3% and positive predictive value was 47.8% for stroke and 59.1% for stroke and transient ischemic attack. Of all 275 patients with stroke dispatcher codes, 215 (78.5%) were confirmed with neurological diagnosis.
Using dispatcher identification algorithm for stroke emergencies, more than half of all patients with stroke admitted by ambulance were correctly identified by dispatchers. Most false-positive stroke codes had other neurological diagnoses.
最近的创新，如在救护车上安装 CT 设备，可能会导致更早期开始针对卒中的特殊治疗。然而，这些技术复杂的移动设备在部署前需要有效的方法来正确识别患者。我们旨在开发和验证一种新的卒中紧急情况调度员识别算法。
卒中紧急情况调度员识别算法是通过对卒中或短暂性脑缺血发作（TIA）（n=117）患者以及其他神经疾病（n=39）和非神经疾病（n=51）患者的紧急呼叫内容进行系统的定性分析而得出的（A 部分）。在调度员接受培训后，前瞻性地确定了在 Charité 医院就诊的患者的敏感性和预测值，以出院诊断作为参考标准（B 部分）。
A 部分：构音障碍/言语不清（33%）、单侧症状（22%）和明确怀疑卒中（47%）是卒中患者的典型表现，但在非卒中病例中很少见（均<10%）。其他神经疾病中频繁出现惊厥症状（41%），但在卒中患者中不常见（3%）。疼痛（26%）和呼吸困难（31%）是常见的非神经急症表现（在卒中患者中分别为 6%和 7%）。B 部分：2010 年 10 月 15 日至 12 月 16 日，共有 5774 名患者通过救护车入院，其中 246 名患者的最终诊断为卒中。卒中紧急情况调度员识别算法检测卒中的敏感性为 53.3%，对卒中及 TIA 的阳性预测值为 47.8%。所有有卒中调度员编码的 275 名卒中患者中，215 名（78.5%）经神经科诊断得到证实。
使用卒中紧急情况调度员识别算法，通过救护车入院的卒中患者中，超过一半的患者被调度员正确识别。大多数假阳性卒中编码都有其他神经诊断。
