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• W2914276632 abstract "Background: Accurate and rapid identification of large vessel occlusions (LVOs) in acute ischemic stroke patients is critical for early notification of specialists and access to reperfusion therapy. Recent advances in artificial intelligence have enabled the development of a fully automated convolutional neural network (Viz LVO) to detect LVOs on CT angiography (CTA) imaging and notify specialists. Early specialist notification of LVOs may benefit patients who initially present at non-interventional centers and must be transferred, as well as patients who present directly to interventional centers. Methods: A multicenter observational trial compared a prospective cohort of CTA-proven LVO patients (exposed to Viz LVO) to a retrospective cohort of patients (controls). Patients were segmented based on initial presentation at (1) a non-interventional center or (2) an interventional center. Clinical performance was measured through time-based workflow metrics, rates of treatment, and patient outcome endpoints. Further, an economic analysis was performed based on internal financial data. Results: Preliminary results of 231 retrospective control patients not exposed to Viz LVO and who initially presented directly to interventional centers between 2016 - 2018 had a mean door-in to groin puncture (DTG) time of 95 minutes. In comparison, 21 prospective patients exposed to Viz LVO and presenting directly to interventional centers showed a mean picture to specialist notification time of 6 minutes, and all had DTG times of less than 95 minutes. Further, the mean contribution margin and net revenue for MS-DRG Codes 023-024 were higher than DRG 061-063 or 064-066 in the prospective patients. Conclusions: This initial cohort of patients demonstrated improved DTG times for LVO patients initially arriving at interventional centers. Although these improvements were not statistically significant, we are continuing additional data collection to provide sufficient power for this study. Preliminary economic analysis demonstrates that Viz LVO can improve net revenue for hospitals through increased volume of a positive contribution margin procedure." @default.
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• W2914276632 date "2019-02-01" @default.
• W2914276632 modified "2023-10-14" @default.
• W2914276632 title "Abstract TP273: DISTINCTION: Automated Detection, Identification, Selection, and Triage using Artificial Intelligence in Large Vessel Occlusions Requiring Critical and Timely InterventION" @default.
• W2914276632 doi "https://doi.org/10.1161/str.50.suppl_1.tp273" @default.
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