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Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes
PURPOSE: Automated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool’s impact on ac...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248058/ https://www.ncbi.nlm.nih.gov/pubmed/37305764 http://dx.doi.org/10.3389/fneur.2023.1179250 |
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author | Soun, Jennifer E. Zolyan, Anna McLouth, Joel Elstrott, Sebastian Nagamine, Masaki Liang, Conan Dehkordi-Vakil, Farideh H. Chu, Eleanor Floriolli, David Kuoy, Edward Joseph, John Abi-Jaoudeh, Nadine Chang, Peter D. Yu, Wengui Chow, Daniel S. |
author_facet | Soun, Jennifer E. Zolyan, Anna McLouth, Joel Elstrott, Sebastian Nagamine, Masaki Liang, Conan Dehkordi-Vakil, Farideh H. Chu, Eleanor Floriolli, David Kuoy, Edward Joseph, John Abi-Jaoudeh, Nadine Chang, Peter D. Yu, Wengui Chow, Daniel S. |
author_sort | Soun, Jennifer E. |
collection | PubMed |
description | PURPOSE: Automated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool’s impact on acute stroke workflow and clinical outcomes. MATERIALS AND METHODS: Consecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated. RESULTS: A total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI, p < 0.0005), notably at the resident level (p < 0.0003) but not at higher levels of expertise. There were no differences in door-to-treatment times, but the NIHSS at discharge was improved for the pre-AI group adjusted for confounders (parameter estimate = 3.97, p < 0.01). CONCLUSION: Implementation of an automated LVO detection tool improved radiology TAT but did not translate to improved stroke metrics and outcomes in a real-world setting. |
format | Online Article Text |
id | pubmed-10248058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102480582023-06-09 Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes Soun, Jennifer E. Zolyan, Anna McLouth, Joel Elstrott, Sebastian Nagamine, Masaki Liang, Conan Dehkordi-Vakil, Farideh H. Chu, Eleanor Floriolli, David Kuoy, Edward Joseph, John Abi-Jaoudeh, Nadine Chang, Peter D. Yu, Wengui Chow, Daniel S. Front Neurol Neurology PURPOSE: Automated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool’s impact on acute stroke workflow and clinical outcomes. MATERIALS AND METHODS: Consecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated. RESULTS: A total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI, p < 0.0005), notably at the resident level (p < 0.0003) but not at higher levels of expertise. There were no differences in door-to-treatment times, but the NIHSS at discharge was improved for the pre-AI group adjusted for confounders (parameter estimate = 3.97, p < 0.01). CONCLUSION: Implementation of an automated LVO detection tool improved radiology TAT but did not translate to improved stroke metrics and outcomes in a real-world setting. Frontiers Media S.A. 2023-05-25 /pmc/articles/PMC10248058/ /pubmed/37305764 http://dx.doi.org/10.3389/fneur.2023.1179250 Text en Copyright © 2023 Soun, Zolyan, McLouth, Elstrott, Nagamine, Liang, Dehkordi-Vakil, Chu, Floriolli, Kuoy, Joseph, Abi-Jaoudeh, Chang, Yu and Chow. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Soun, Jennifer E. Zolyan, Anna McLouth, Joel Elstrott, Sebastian Nagamine, Masaki Liang, Conan Dehkordi-Vakil, Farideh H. Chu, Eleanor Floriolli, David Kuoy, Edward Joseph, John Abi-Jaoudeh, Nadine Chang, Peter D. Yu, Wengui Chow, Daniel S. Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title | Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title_full | Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title_fullStr | Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title_full_unstemmed | Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title_short | Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
title_sort | impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248058/ https://www.ncbi.nlm.nih.gov/pubmed/37305764 http://dx.doi.org/10.3389/fneur.2023.1179250 |
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