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. 2023;13(1):41-46.
doi: 10.1159/000529077. Epub 2023 Feb 14.

Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System

Stavros Matsoukas  1 , Laura K Stein  2 , Johanna Fifi  1   2
Affiliations

Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System

Stavros Matsoukas et al. Cerebrovasc Dis Extra. 2023.

Abstract

Introduction: Artificial intelligence (AI) software is increasingly applied in stroke diagnostics. Viz LVO (large vessel occlusion) is an AI-based software that is FDA-approved for LVO detection in CT angiography (CTA) scans. We sought to investigate differences in transfer times (from peripheral [spoke] to central [hub] hospitals) for LVO patients between spoke hospitals that utilize Viz LVO and those that do not.

Methods: In this retrospective cohort study, we used our institutional database to identify all suspected/confirmed LVO-transferred patients from spokes (peripheral hospitals) within and outside of our healthcare system, from January 2020 to December 2021. The "Viz-transfers" group includes all LVO transfers from spokes within our system where Viz LVO is readily available, while the "Non-Viz-transfers" group (control group) is comprised of all LVO transfers from spokes outside our system, without Viz LVO. Primary outcome included all available time metrics from peripheral CTA commencement.

Results: In total, 78 patients required a transfer. Despite comparable peripheral hospital door to peripheral hospital CTA times (20.5 [24.3] vs. 32 [45] min, p = 0.28) and transfer (spoke to hub) time (23 [18] vs. 26 [13.5], p = 0.763), all workflow metrics were statistically significantly shorter in the Viz-transfers group. Peripheral CTA to interventional neuroradiology team notification was 12 (16.8) versus 58 (59.5), p < 0.001, and peripheral CTA to peripheral departure was 91.5 (37) versus 122.5 (68.5), p < 0.001. Peripheral arrival to peripheral departure was 116.5 (75.5) versus 169 (126.8), p = 0.002, and peripheral arrival to central arrival was 145 (62.5) versus 207 (97.8), p < 0.001. In addition, peripheral CTA to angiosuite arrival was 121 (41) versus 207 (92.5), p < 0.001, peripheral CTA to arterial puncture was 146 (53) versus 234 (99.8), p < 0.001, and peripheral CTA to recanalization was 198 (25) versus 253.5 (86), p < 0.001.

Conclusion: Within our spoke and hub system, Viz LVO significantly decreased all workflow metrics for patients who were transferred from spokes with versus without Viz.

Keywords: Artificial intelligence; Artificial intelligence-assisted diagnosis; Drip and ship; Endovascular thrombectomy; Stroke transfers.

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Conflict of interest statement

Laura Stein: research funding from the American Heart Association. Johanna Fifi: personal Fees – Penumbra, Stryker, Microvention, and Cerenovus. Ownership interest: Imperative Care and Cerebrotech. Grant: Viz.

Figures

Fig. 1.
Fig. 1.
a–d Comparative illustration of selected time metrics. Median times are provided for all metrics. Please note that most time metrics are calculated with peripheral CTA as the starting point. Therefore, only selected combinations of time metrics were able to be presented as continuous timelines. CTA, computed tomography angiography; INR, interventional radiology; ED, emergency department.
See this image and copyright information in PMC

References

    1. Warner JJ, Harrington RA, Sacco RL, Elkind MSV. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 Guidelines for the early management of acute ischemic stroke. Stroke. 2019 Dec;50(12):3331–2. 10.1161/STROKEAHA.119.027708. - DOI - PubMed
    1. Khatri P, Abruzzo T, Yeatts SD, Nichols C, Broderick JP, Tomsick TA, et al. . Good clinical outcome after ischemic stroke with successful revascularization is time-dependent. Neurology. 2009 Sep 29;73(13):1066–72. 10.1212/WNL.0b013e3181b9c847. - DOI - PMC - PubMed
    1. Kunz WG, Hunink MG, Almekhlafi MA, Menon BK, Saver JL, Dippel DWJ, et al. . Public health and cost consequences of time delays to thrombectomy for acute ischemic stroke. Neurology. 2020 Nov 3;95(18):e2465–75. 10.1212/WNL.0000000000010867. - DOI - PubMed
    1. Fuentes B, Alonso de Leciñana M, Ximénez-Carrillo A, Martínez-Sánchez P, Cruz-Culebras A, Zapata-Wainberg G, et al. . Futile interhospital transfer for endovascular treatment in acute ischemic stroke: the Madrid stroke network experience. Stroke. 2015 Aug;46(8):2156–61. 10.1161/STROKEAHA.115.009282. - DOI - PubMed
    1. Hassan AE, Ringheanu VM, Rabah RR, Preston L, Tekle WG, Qureshi AI. Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interv Neuroradiol. 2020 Oct;26(5):615–22. 10.1177/1591019920953055. - DOI - PMC - PubMed

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