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; et al. (Frontiers in Neurology, 2023)
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 is to evaluate an automated LVO detection tool’s impact on acute stroke workflow and clinical outcomes. Materials and Methods Consecutive patients with a CTA presenting with suspected acute ischemic stroke were compared before and after 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 NIH stroke scale (NIHSS) after treatment were evaluated. Results 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 sensitivity 0.96, specificity 0.85, negative predictive value 0.99, and positive predictive value 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI versus 22 min for post-AI, P < 0.0005), notably at the resident level (P < 0.0003) but not higher levels of expertise. There were no differences in door-to-treatment times, but 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.