Department of Neurology, Semmelweis University, Budapest, Hungary.
Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Cerebrovasc Dis Extra. 2022;12(1):28-32. doi: 10.1159/000522423. Epub 2022 Feb 8.
Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management.
We evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre.
We retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical 7-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy.
399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018, thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8-4.8%). There was a trend towards shorter door-to-needle times (44-42 min) and CT-to-groin puncture times (174-145 min). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow.
Use of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care.
再灌注治疗的患者选择需要在神经影像学方面有丰富的专业知识。越来越多的机器学习分析被用于更快和标准化的患者选择。然而，关于此类软件如何影响现实世界中的患者管理的信息却很少。
我们评估了在一家高容量初级卒中中心实施自动化分析后，溶栓和血栓切除术的实施情况发生了哪些变化。
我们回顾性地收集了 2017 年和 2018 年两个相同的 7 个月期间，在一家大型大学卒中中心连续收治的卒中患者的数据，在此期间实施了 e-Stroke Suite（Brainomix，牛津，英国）来分析非对比 CT 和 CT 血管造影结果。卒中治疗的其他方面保持不变。患者被转至血栓切除术中心。我们收集了接受静脉溶栓和/或血栓切除术的患者数量、治疗时间；以及血栓切除术 90 天的转归。
2017 年纳入 399 例患者，2018 年纳入 398 例患者。2017 年至 2018 年，溶栓率从 11.5%增加到 18.1%，血栓切除术的趋势类似（2.8%-4.8%）。门到针时间（44-42 分钟）和 CT 到腹股沟穿刺时间（174-145 分钟）有缩短的趋势。血栓切除术的转归有改善的趋势，但无统计学意义。医生的反馈表明，人工智能决策支持在超急性期卒中通路中的应用增加了决策的信心，并改善了枢纽辐射式护理系统中再灌注治疗的速度和时间。
在急性卒中治疗途径中使用人工智能决策支持有助于决策，并可改善在枢纽辐射式医疗系统中再灌注治疗的速度和时间。
