Centre for Rural Health, University of Aberdeen, Inverness, United Kingdom.
Department of Radiology, Raigmore Hospital, NHS Highland, Inverness, United Kingdom.
PLoS One. 2020 Oct 2;15(10):e0239653. doi: 10.1371/journal.pone.0239653. eCollection 2020.
Rapid endovascular thrombectomy, which can only be delivered in specialist centres, is the most effective treatment for acute ischaemic stroke due to large vessel occlusion (LVO). Pre-hospital selection of these patients is challenging, especially in remote and rural areas due to long transport times and limited access to specialist clinicians and diagnostic facilities. We investigated whether combined transcranial ultrasound and clinical assessment ("TUCA" model) could accurately triage these patients and improve access to thrombectomy. We recruited consecutive patients within 72 hours of suspected stroke, and performed non-contrast transcranial colour-coded ultrasonography within 24 hours of brain computed tomography. We retrospectively collected clinical information, and used hospital discharge diagnosis as the "gold standard". We used binary regression for diagnosis of haemorrhagic stroke, and an ordinal regression model for acute ischaemic stroke with probable LVO, without LVO, transient ischaemic attacks (TIA) and stroke mimics. We calculated sensitivity, specificity, positive and negative predictive values and performed a sensitivity analysis. We recruited 107 patients with suspected stroke from July 2017 to December 2019 at two study sites: 13/107 (12%) with probable LVO, 50/107 (47%) with acute ischaemic stroke without LVO, 18/107 (17%) with haemorrhagic stroke, and 26/107 (24%) with stroke mimics or TIA. The model identified 55% of cases with probable LVO who would have correctly been selected for thrombectomy and 97% of cases who would not have required this treatment (sensitivity 55%, specificity 97%, positive and negative predictive values 75% and 93%, respectively). Diagnostic accuracy of the proposed model was superior to the clinical assessment alone. These data suggest that our model might be a useful tool to identify pre-hospital patients requiring mechanical thrombectomy, however a larger sample is required with the use of CT angiogram as a reference test.
血管内取栓术是治疗大血管闭塞（LVO）所致急性缺血性脑卒中最有效的方法，但只能在专业中心进行。由于转运时间长，且能获得的专科临床医生和诊断设备有限，因此对这些患者进行院前选择具有挑战性，特别是在偏远和农村地区。我们研究了联合经颅超声和临床评估（“TUCA”模型）是否可以准确对这些患者进行分诊，并改善取栓术的获得途径。我们在疑似脑卒中后 72 小时内连续招募患者，并在脑部计算机断层扫描后 24 小时内进行非对比经颅彩色编码超声检查。我们回顾性地收集了临床信息，并将医院出院诊断作为“金标准”。我们使用二项回归诊断脑出血，使用有序回归模型诊断急性缺血性脑卒中伴可能 LVO、不伴 LVO、短暂性脑缺血发作（TIA）和脑卒中模拟。我们计算了灵敏度、特异性、阳性和阴性预测值，并进行了灵敏度分析。我们在两个研究地点从 2017 年 7 月至 2019 年 12 月共招募了 107 名疑似脑卒中患者：13/107（12%）患者伴可能 LVO，50/107（47%）患者伴不伴 LVO 的急性缺血性脑卒中，18/107（17%）患者伴脑出血，26/107（24%）患者伴脑卒中模拟或 TIA。该模型识别出 55%的可能 LVO 患者，他们将被正确选择进行取栓治疗，97%的患者将不需要这种治疗（敏感性 55%，特异性 97%，阳性和阴性预测值分别为 75%和 93%）。与单独临床评估相比，该模型的诊断准确性更高。这些数据表明，我们的模型可能是一种有用的工具，可用于识别需要机械取栓治疗的院前患者，但需要更大的样本量，并使用 CT 血管造影作为参考测试。
