Smith Brett H, Wolfe Jackson G, Karam Alvina, Demarkschalk Bart M, Hrdlicka Courtney M, Nasr Deena M, Chukwudelunzu Felix E, Nord Charisse A, Pahl Emily A, Fernandez Claire, Wood Sam, Woodhead Zoe Vj, Carone Davide, Harston George, English Stephen W
Department of Neurology, Mayo Clinic, Jacksonville, FL.
Department of Neurology, Mayo Clinic, Pheonix, AZ.
Mayo Clin Proc Innov Qual Outcomes. 2025 May 30;9(4):100631. doi: 10.1016/j.mayocpiqo.2025.100631. eCollection 2025 Aug.
To explore the real-world impact of artificial intelligence-driven decision support imaging software for patients with acute ischemic stroke in a mature telestroke network in the United States.
We conducted a prospective evaluation of stroke imaging support software in a robust, predominantly rural telestroke network (17 sites in Minnesota and Wisconsin). Data was collected from all patients who underwent video telestroke evaluation in a 3-month preimplementation period before installation of the software (from February 10, 2024 to May 9, 2024) and a 3-month postimplementation period while the software was in use (from May 10, 2024 to August 9, 2024). The preimplementation and postimplementation cohorts were directly compared (no control group included). Primary outcome measures were treatment rates and time to treatment (both treatment decision and delivery) for intravenous thrombolysis (IVT) and endovascular therapy (EVT); secondary outcomes included transfer rates, transfer times, and end user survey results.
Total of 444 telestroke cases were included in the preimplementation period, and 463 in the postimplementation period. Comparing preimplementation and postimplementation periods, the rate of IVT treatment delivery rose from 26.6% to 35.0% of potentially eligible patients (=.24), whereas EVT treatment delivery remained at 31%. Time to IVT delivery reduced from 47 minutes to 41 minutes (=.772), and time to EVT treatment rose from 156 minutes to 157 minutes (=.771). Overall rates of treatment (IVT or EVT) rose from 23.1% to 23.9% of potentially eligible patients (=.944). Although none of the clinical outcomes reached statistical significance, the survey results reported good user satisfaction with algorithm performance and image viewing.
This study reported a nonsignificant increase in treatment rates and a decrease in time to treatment decisions. Future trials with larger sample sizes are needed to validate the real-world benefits of decision support software for acute ischemic stroke in an established telestroke network.
在美国一个成熟的远程卒中网络中,探讨人工智能驱动的决策支持成像软件对急性缺血性卒中患者的实际影响。
我们在一个强大的、主要为农村地区的远程卒中网络(明尼苏达州和威斯康星州的17个站点)中对卒中成像支持软件进行了前瞻性评估。收集了在软件安装前3个月的预实施期(从2024年2月10日至2024年5月9日)和软件使用期间的3个月后实施期(从2024年5月10日至2024年8月9日)接受视频远程卒中评估的所有患者的数据。直接比较了预实施期和后实施期的队列(未设对照组)。主要结局指标是静脉溶栓(IVT)和血管内治疗(EVT)的治疗率及治疗时间(包括治疗决策和治疗实施);次要结局包括转诊率、转诊时间和最终用户调查结果。
预实施期共纳入444例远程卒中病例,后实施期纳入463例。比较预实施期和后实施期,IVT治疗实施率从潜在 eligible 患者的26.6%升至35.0%(P =.24),而EVT治疗实施率保持在31%。IVT治疗实施时间从47分钟降至41分钟(P =.772),EVT治疗时间从156分钟升至157分钟(P =.771)。总体治疗率(IVT或EVT)从潜在 eligible 患者的23.1%升至23.9%(P =.944)。尽管所有临床结局均未达到统计学显著性,但调查结果显示用户对算法性能和图像查看满意度良好。
本研究报告治疗率有非显著性增加,治疗决策时间减少。需要进行更大样本量的未来试验,以验证决策支持软件在既定远程卒中网络中对急性缺血性卒中的实际益处。