Abstract
Background: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software can reduce the time interval between CTA at a primary stroke center (PSC) and arrival time at a comprehensive stroke center (CSC). Methods: We compared time interval between CTA and time of arrival for all LVO transfer patients from a single spoke PSC to our CSC prior to (Feb. 2017 to Nov. 2018) and after (Nov. 2018 to May 2019) incorporating Viz.ai. Using a prospectively collected stroke database at a CSC, demographics, transfer time (CTA time to time of arrival at CSC), modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital and neurological ICU, and intracranial hemorrhage rates were examined. Results: There were a total of 43 patients during the study period (average age 70.77 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-Viz.ai (average age 71.64 ± 12.28 yrs., 46.4% women), and 15 patients from the post-Viz.ai (average age 69.13 ± 13.29 yrs., 60.0% women); see Table 1 for comparison of baseline characteristics and outcomes. Following implementation of Viz.ai, CTA time at PSC to time of arrival at CSC was significantly reduced by an average of 66 min. (mean CTA to time of arrival, 171 min. vs 105 min; P= 0.0163); significant reductions were also noted in the overall LOS (9.7 days vs 7.2 days; P= 0.0324) and LOS in the neurological ICU (6.4 days vs 2.9 days; P= 0.0039). Conclusions: The incorporation of Viz.ai was associated with a significant improvement in transfer times for LVO patients as well as a significant reduction in the overall hospital LOS and LOS in the neurological ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.
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