Evidence map›Paper›PMID 37305764›Full record

ArticleFrontiers in neurology2023

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 and 5 more

Abstract read
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In one paragraph

Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jennifer E SounDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Anna ZolyanDepartment of Neurology, University of California, Irvine, Orange, CA, United States.
Joel McLouthDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Sebastian ElstrottDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Masaki NagamineDepartment of Neurology, University of California, Irvine, Orange, CA, United States.
Conan LiangDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Farideh H Dehkordi-VakilCenter for Statistical Consulting, School of Medicine, University of California, Irvine, Irvine, CA, United States.
Eleanor ChuDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
David FloriolliDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Edward KuoyDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
John JosephThe Paul Merage School of Business, School of Medicine, University of California, Irvine, Irvine, CA, United States.
Nadine Abi-JaoudehDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Peter D ChangDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.
Wengui YuDepartment of Neurology, University of California, Irvine, Orange, CA, United States.
Daniel S ChowDepartment of Radiological Sciences, University of California, Irvine, Orange, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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, 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.

Indexed as

artificial intelligenceCT angiographylarge vessel occlusionmachine learningstroke

Identifiers

PMID37305764
PMCPMC10248058

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.