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Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System
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 [s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
S. Karger AG
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999083/ https://www.ncbi.nlm.nih.gov/pubmed/36787716 http://dx.doi.org/10.1159/000529077 |
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author | Matsoukas, Stavros Stein, Laura K. Fifi, Johanna |
author_facet | Matsoukas, Stavros Stein, Laura K. Fifi, Johanna |
author_sort | Matsoukas, Stavros |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9999083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | S. Karger AG |
record_format | MEDLINE/PubMed |
spelling | pubmed-99990832023-03-11 Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System Matsoukas, Stavros Stein, Laura K. Fifi, Johanna Cerebrovasc Dis Extra Original Paper 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. S. Karger AG 2023-02-14 /pmc/articles/PMC9999083/ /pubmed/36787716 http://dx.doi.org/10.1159/000529077 Text en © 2023 The Author(s). Published by S. Karger AG, Basel https://creativecommons.org/licenses/by-nc/4.0/This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC) (http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission. |
spellingShingle | Original Paper Matsoukas, Stavros Stein, Laura K. Fifi, Johanna Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title | Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title_full | Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title_fullStr | Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title_full_unstemmed | Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title_short | Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System |
title_sort | artificial intelligence-assisted software significantly decreases all workflow metrics for large vessel occlusion transfer patients, within a large spoke and hub system |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999083/ https://www.ncbi.nlm.nih.gov/pubmed/36787716 http://dx.doi.org/10.1159/000529077 |
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