Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

NCT ID: NCT05838456

Last Updated: 2023-06-28

Study Results

Results available

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Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

443 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-01-01

Study Completion Date

2022-05-27

Brief Summary

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After onset of Acute Ischemic Stroke (AIS), every minute of delay to treatment reduces the likelihood of a good clinical outcome. A key delay occurs in the time between completion of computed tomography (CT) angiography of the head and neck and interpretation in the setting of AIS care.

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.

Detailed Description

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Conditions

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Acute Ischemic Stroke (AIS)

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

This is a stepped wedge cluster-randomized trial with 4 clusters (4 different hospitals). In a stepped wedge fashion over 3 month intervals, the 4 clusters will initiate use of the software package (Viz.AI). The order of implementation of the Viz.AI software at the four hospitals will be randomly determined.
Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software

Group Type EXPERIMENTAL

Viz.AI software

Intervention Type DEVICE

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

Group Type EXPERIMENTAL

Viz.AI software

Intervention Type DEVICE

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

Group Type EXPERIMENTAL

Viz.AI software

Intervention Type DEVICE

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

Group Type EXPERIMENTAL

Viz.AI software

Intervention Type DEVICE

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.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Male or Female
* 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

* Patients with incomplete data on the electronic medical record.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Center for Advancing Translational Sciences (NCATS)

NIH

Sponsor Role collaborator

The University of Texas Health Science Center, Houston

OTHER

Sponsor Role lead

Responsible Party

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Sunil A. Sheth

Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

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

Site Status

Countries

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United States

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.

Reference Type DERIVED
PMID: 37721738 (View on PubMed)

Provided Documents

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Document Type: Study Protocol

View Document

Other Identifiers

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UL1TR003167

Identifier Type: NIH

Identifier Source: secondary_id

View Link

HSC-MS-19-0630

Identifier Type: -

Identifier Source: org_study_id

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