Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
2 other identifiers
interventional
443
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jan 2021
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 28, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
May 27, 2022
CompletedFirst Submitted
Initial submission to the registry
April 17, 2023
CompletedFirst Posted
Study publicly available on registry
May 1, 2023
CompletedResults Posted
Study results publicly available
June 28, 2023
CompletedJune 28, 2023
June 1, 2023
1.2 years
April 17, 2023
May 2, 2023
June 3, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Time From Emergency Room Arrival to Initiation of Endovascular Stroke Therapy ("Door-to-groin" Time)
from the time of emergency room arrival to the time of initiation of endovascular stroke therapy (about 97 minutes)
Secondary Outcomes (4)
Number of Patients Who Received With Endovascular Stroke Therapy
at the time of initiation of endovascular stroke therapy
Number of Patients With Good Functional Outcome Defined as Modified Rankin Score (mRS) of 0-2
90 days
Hospital Length of Stay
From the time of admission to the hospital to the time of discharge (about 7 days)
Number of Patients With Intracranial Hemorrhage (ICH)
From the time of admission to the hospital to the time of discharge (about 7 days)
Study Arms (4)
Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software
EXPERIMENTALHospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software
EXPERIMENTALHospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software
EXPERIMENTALHospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software
EXPERIMENTALInterventions
Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Eligibility Criteria
You may qualify if:
- Male or Female
- 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.
You may not qualify if:
- Patients with incomplete data on the electronic medical record.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
The University of Texas Health Science Center at Houston
Houston, Texas, 77030, United States
Related Publications (1)
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.
PMID: 37721738DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Results Point of Contact
- Title
- Sunil A. Sheth, MD
- Organization
- The University of Texas Health Science Center at Houston
Study Officials
- PRINCIPAL INVESTIGATOR
Sunil Sheth, MD
The University of Texas Health Science Center, Houston
Publication Agreements
- PI is Sponsor Employee
- Yes
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- DIAGNOSTIC
- Intervention Model
- CROSSOVER
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Associate Professor
Study Record Dates
First Submitted
April 17, 2023
First Posted
May 1, 2023
Study Start
January 1, 2021
Primary Completion
February 28, 2022
Study Completion
May 27, 2022
Last Updated
June 28, 2023
Results First Posted
June 28, 2023
Record last verified: 2023-06
Data Sharing
- IPD Sharing
- Will not share