Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
NCT ID: NCT05838456
Last Updated: 2023-06-28
Study Results
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View full resultsBasic Information
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COMPLETED
NA
443 participants
INTERVENTIONAL
2021-01-01
2022-05-27
Brief Summary
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The purpose of this study is to assess the effect of incorporating Viz.AI software, which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions (LVO) on CT angiography (CTA) images and alerts the AIS care team (diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician, per routine standard of care). The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment. A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
CROSSOVER
DIAGNOSTIC
NONE
Study Groups
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Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software
Viz.AI software
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Hospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software
Viz.AI software
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Hospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software
Viz.AI software
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Hospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software
Viz.AI software
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Interventions
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Viz.AI software
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Eligibility Criteria
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Inclusion Criteria
* 18 years of age or older.
* Patients who present to the emergency department with signs and/or symptoms concerning for acute ischemic stroke.
* Patients who undergo CT angiography imaging
* Patients determined to have a large vessel occlusion acute ischemic stroke. This determination will be made based on official radiology report for the CT angiography imaging.
Exclusion Criteria
18 Years
ALL
No
Sponsors
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National Center for Advancing Translational Sciences (NCATS)
NIH
The University of Texas Health Science Center, Houston
OTHER
Responsible Party
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Sunil A. Sheth
Associate Professor
Principal Investigators
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Sunil Sheth, MD
Role: PRINCIPAL_INVESTIGATOR
The University of Texas Health Science Center, Houston
Locations
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The University of Texas Health Science Center at Houston
Houston, Texas, United States
Countries
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References
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Martinez-Gutierrez JC, Kim Y, Salazar-Marioni S, Tariq MB, Abdelkhaleq R, Niktabe A, Ballekere AN, Iyyangar AS, Le M, Azeem H, Miller CC, Tyson JE, Shaw S, Smith P, Cowan M, Gonzales I, McCullough LD, Barreto AD, Giancardo L, Sheth SA. Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: A Cluster Randomized Clinical Trial. JAMA Neurol. 2023 Nov 1;80(11):1182-1190. doi: 10.1001/jamaneurol.2023.3206.
Provided Documents
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Document Type: Study Protocol
Other Identifiers
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HSC-MS-19-0630
Identifier Type: -
Identifier Source: org_study_id
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