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...
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| Format: | Article |
| Language: | English |
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Karger Publishers
2023-02-01
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| Series: | Cerebrovascular Diseases Extra |
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| Online Access: | https://www.karger.com/Article/FullText/529077 |
| _version_ | 1811157649220173824 |
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| author | Stavros Matsoukas Laura K. Stein Johanna Fifi |
| author_facet | Stavros Matsoukas Laura K. Stein Johanna Fifi |
| author_sort | Stavros Matsoukas |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T05:10:51Z |
| format | Article |
| id | doaj.art-bf102fcd304349ee87fb1f913152a4f6 |
| institution | Directory Open Access Journal |
| issn | 1664-5456 |
| language | English |
| last_indexed | 2024-04-10T05:10:51Z |
| publishDate | 2023-02-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Cerebrovascular Diseases Extra |
| spelling | doaj.art-bf102fcd304349ee87fb1f913152a4f62023-03-09T08:59:36ZengKarger PublishersCerebrovascular Diseases Extra1664-54562023-02-01131414610.1159/000529077529077Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub SystemStavros Matsoukas0https://orcid.org/0000-0001-5902-0637Laura K. Stein1https://orcid.org/0000-0002-8268-616XJohanna Fifi2Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USADepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USADepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USAIntroduction: 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.https://www.karger.com/Article/FullText/529077stroke transfersartificial intelligenceartificial intelligence-assisted diagnosisendovascular thrombectomydrip and ship |
| spellingShingle | Stavros Matsoukas Laura K. Stein Johanna Fifi Artificial Intelligence-Assisted Software Significantly Decreases All Workflow Metrics for Large Vessel Occlusion Transfer Patients, within a Large Spoke and Hub System Cerebrovascular Diseases Extra stroke transfers artificial intelligence artificial intelligence-assisted diagnosis endovascular thrombectomy drip and ship |
| 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 | stroke transfers artificial intelligence artificial intelligence-assisted diagnosis endovascular thrombectomy drip and ship |
| url | https://www.karger.com/Article/FullText/529077 |
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