Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes

Jennifer E Soun; Anna Zolyan; Joel McLouth; Sebastian Elstrott; Masaki Nagamine; Conan Liang; Farideh H Dehkordi-Vakil; Eleanor Chu; David Floriolli; Edward Kuoy; John Joseph; Nadine Abi-Jaoudeh; Peter D Chang; Wengui Yu; Daniel S Chow

doi:10.3389/fneur.2023.1179250

Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes

Front Neurol. 2023 May 25:14:1179250. doi: 10.3389/fneur.2023.1179250. eCollection 2023.

Authors

Jennifer E Soun¹, Anna Zolyan², Joel McLouth¹, Sebastian Elstrott¹, Masaki Nagamine², Conan Liang¹, Farideh H Dehkordi-Vakil³, Eleanor Chu¹, David Floriolli¹, Edward Kuoy¹, John Joseph⁴, Nadine Abi-Jaoudeh¹, Peter D Chang^{1

5}, Wengui Yu², Daniel S Chow^{1

5}

Affiliations

¹ Department of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
² Department of Neurology, University of California, Irvine, Orange, CA, United States.
³ Center for Statistical Consulting, School of Medicine, University of California, Irvine, Irvine, CA, United States.
⁴ The Paul Merage School of Business, School of Medicine, University of California, Irvine, Irvine, CA, United States.
⁵ Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, CA, United States.

Abstract

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.

Keywords: CT angiography; artificial intelligence; large vessel occlusion; machine learning; stroke.