Nor Azlisham Mohd, Davis John, Sen Bas, Shipsey Dean, Louw Stephen J, Dyker Alexander G, Davis Michelle, Ford Gary A
The Freeman Hospital Stroke Service, Newcastle Hospitals NHS Trust, Newcastle upon Tyne, UK.
Lancet Neurol. 2005 Nov;4(11):727-34. doi: 10.1016/S1474-4422(05)70201-5.
In patients with acute stroke, rapid intervention is crucial to maximise early treatment benefits. Stroke patients commonly have their first contact with medical staff in the emergency room (ER). We designed and validated a stroke recognition tool-the Recognition of Stroke in the Emergency Room (ROSIER) scale-for use by ER physicians.
We prospectively collected data for 1 year (development phase) on the clinical characteristics of patients with suspected acute stroke who were admitted to hospital from the ER. We used logistic regression analysis and clinical reasoning to develop a stroke recognition instrument for application in this setting. Patients with suspected transient ischaemic attack (TIA) with no symptoms or signs when assessed in the ER were excluded from the analysis. The instrument was assessed using the baseline 1-year dataset and then prospectively validated in a new cohort of ER patients admitted over a 9-month period.
In the development phase, 343 suspected stroke patients were assessed (159 stroke, 167 non-stroke, 32 with TIA [17 with symptoms when seen in ER]). Common stroke mimics were seizures (23%), syncope (23%), and sepsis (10%). A seven-item (total score from -2 to +5) stroke recognition instrument was constructed on the basis of clinical history (loss of consciousness, convulsive fits) and neurological signs (face, arm, or leg weakness, speech disturbance, visual field defect). When internally validated at a cut-off score greater than zero, the instrument showed a diagnostic sensitivity of 92%, specificity of 86%, positive predictive value (PPV) of 88%, and negative predictive value (NPV) of 91%. Prospective validation in 173 consecutive suspected stroke referrals (88 stroke, 59 non-stroke, 26 with TIA [13 with symptoms]) showed sensitivity of 93% (95% CI 89-97), specificity 83% (77-89), PPV 90% (85-95), and NPV 88% (83-93). The ROSIER scale had greater sensitivity than existing stroke recognition instruments in this population.
The ROSIER scale was effective in the initial differentiation of acute stroke from stroke mimics in the ER. Introduction of the instrument improved the appropriateness of referrals to the stroke team.
对于急性中风患者，快速干预对于最大化早期治疗益处至关重要。中风患者通常在急诊室首次接触医护人员。我们设计并验证了一种中风识别工具——急诊室中风识别（ROSIER）量表，供急诊室医生使用。
我们前瞻性地收集了1年（开发阶段）内从急诊室入院的疑似急性中风患者的临床特征数据。我们使用逻辑回归分析和临床推理来开发一种适用于此情况的中风识别工具。在急诊室评估时无症状或体征的疑似短暂性脑缺血发作（TIA）患者被排除在分析之外。该工具使用基线1年数据集进行评估，然后在一个新的连续9个月入院的急诊室患者队列中进行前瞻性验证。
在开发阶段，评估了343例疑似中风患者（159例中风，167例非中风，32例TIA [17例在急诊室就诊时有症状]）。常见的中风模仿疾病有癫痫（23%）、晕厥（23%）和败血症（10%）。基于临床病史（意识丧失、抽搐发作）和神经体征（面部、手臂或腿部无力、言语障碍、视野缺损）构建了一个七项（总分从 -2到 +5）的中风识别工具。当以大于零的临界值进行内部验证时，该工具显示诊断敏感性为92%，特异性为86%，阳性预测值（PPV）为88%，阴性预测值（NPV）为91%。对173例连续的疑似中风转诊患者（88例中风，59例非中风，26例TIA [13例有症状]）进行前瞻性验证，显示敏感性为93%（95%CI 89 - 97），特异性为83%（77 - 89），PPV为90%（85 - 95），NPV为88%（83 - 93）。在该人群中，ROSIER量表比现有的中风识别工具具有更高的敏感性。
ROSIER量表在急诊室对急性中风与中风模仿疾病的初始鉴别中有效。该工具的引入提高了转诊至中风团队的适宜性。
