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Ischemic stroke
Original research
Viz LVO versus Rapid LVO in detection of large vessel occlusion on CT angiography for acute stroke
  1. Adam Delora1,
  2. Christopher Hadjialiakbari1,
  3. Eryn Percenti2,
  4. Jordan Torres2,
  5. Yazan J Alderazi3,
  6. Rime Ezzeldin4,
  7. Ameer E Hassan5,
  8. Mohamad Ezzeldin6,7
  1. 1 Emergency Medicine, HCA Houston, Kingwood, Texas, USA
  2. 2 Internal Medicine, HCA Houston, Kingwood, Texas, USA
  3. 3 HCA Gulf Coast Division, HCA Houston, Webster, Texas, USA
  4. 4 Medicine, Jordan University of Science and Technology, Irbid, Jordan
  5. 5 Department of Neurology, University of Texas Rio Grande Valley, Harlingen, Texas, USA
  6. 6 Department of Clinical Sciences, College of Medicine, University of Houston, Houston, Texas, USA
  7. 7 Neuroendovascular Surgery, HCA Houston, Houston, Texas, USA
  1. Correspondence to Dr Mohamad Ezzeldin, Department of Clinical Sciences, College of Medicine, University of Houston, Houston, TX, USA; mohamadezzeldin{at}hotmail.com

Abstract

Background Endovascular thrombectomy improves outcomes and reduces mortality for large vessel occlusion (LVO) and is time-sensitive. Computer automation may aid in the early detection of LVOs, but false values may lead to alarm desensitization. We compared Viz LVO and Rapid LVO for automated LVO detection.

Methods Data were retrospectively extracted from Rapid LVO and Viz LVO running concurrently from January 2022 to January 2023 on CT angiography (CTA) images compared with a radiologist interpretation. We calculated diagnostic accuracy measures and performed a McNemar test to look for a difference between the algorithms’ errors. We collected demographic data, comorbidities, ejection fraction (EF), and imaging features and performed a multiple logistic regression to determine if any of these variables predicted the incorrect classification of LVO on CTA.

Results 360 participants were included, with 47 large vessel occlusions. Viz LVO and Rapid LVO had a specificity of 0.96 and 0.85, a sensitivity of 0.87 and 0.87, a positive predictive value of 0.75 and 0.46, and a negative predictive value of 0.98 and 0.97, respectively. A McNemar test on correct and incorrect classifications showed a statistically significant difference between the two algorithms’ errors (P=0.00000031). A multiple logistic regression showed that low EF (Viz P=0.00125, Rapid P=0.0286) and Modified Woodcock Score >1 (Viz P=0.000198, Rapid P=0.000000975) were significant predictors of incorrect classification.

Conclusion Rapid LVO produced a significantly larger number of false positive values that may contribute to alarm desensitization, leading to missed alarms or delayed responses. EF and intracranial atherosclerosis were significant predictors of incorrect predictions.

  • CT Angiography
  • Technology
  • CT
  • Stroke
  • Thrombectomy

Data availability statement

Data are available upon reasonable request.

https://doi.org/10.1136/jnis-2023-020445

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    Data availability statement

    Data are available upon reasonable request.

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    Footnotes

    • X @jordantorres_md

    • Correction notice Since this article first published, the following has been added to the COI section. 'HCA Healthcare has a minority equity stake in VIZ.ai and also purchases products from the company for use in its facilities'.

    • Contributors ME, YA, AH, AD and RE all contributed to the design of the study and formation of the data dictionary. ME and YA were involved in data collection. AD performed the statistics. All authors contributed to interpreting these results. AD, CH, EP, and JT were involved in drafting the manuscript. AH, ME and YA performed revisions of this manuscript. All authors were involved in data interpretation and article revision. ME is the primary investigator and coordinated data collection at multiple sites. ME is the guarantor and accepts full responsibility for the finished work and the conduct of the study, had access to the data, and controlled the decision to publish.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Disclaimer This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

    • Competing interests HCA Healthcare has a minority equity stake in VIZ.ai and also purchases products from the company for use in its facilities. AH is a consultant and a speaker for Viz.ai, which develops the Viz LVO algorithm that we tested, and he is also involved in a study sponsored by Viz.ai called the LVO Synchronise Study, which looks at the impact of a Viz LVO implementation on patient timing and outcomes. Our other authors do not have any direct conflicts of interest. In the interest of full disclosure, here is a list of conflicts of related companies that are in a related field that we do not believe directly impact this study. YA has stock in Sanavention inc. ME has stock in Galaxy therapeutics. AH is a Consultant/Speaker for Medtronic, Microvention, Stryker, Penumbra, Cerenovus, Genentech, GE Healthcare, Scientia, Balt, Viz.ai, Insera therapeutics, Proximie, NeuroVasc, NovaSignal, Vesalio, Rapid Medical, Imperative Care and Galaxy Therapeutics. AH is also a principal investigator of COMPLETE study – Penumbra, LVO SYNCHRONISE – Viz.ai, Millipede Stroke Trial - Perfuze, RESCUE - ICAD - Medtronic. AH is also on the Steering Committee/Publication Committee of SELECT, DAWN, SELECT 2, EXPEDITE II, EMBOLISE, CLEAR, ENVI, DELPHI, and DISTALS. He is also DSMB of the COMAND trial.

    • Provenance and peer review Not commissioned; externally peer reviewed.