Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

• Sponsor: The University of Texas Health Science Center, Houston (Other)
• Overall Status: Completed
• CT.gov ID: NCT05838456
• Collaborator: National Center for Advancing Translational Sciences (NCATS) (NIH)
• Enrollment: 443
• Location: 1
• Arms: 4
• Actual Duration (Months): 16.8
• Patients Per Site Per Month: 26.4

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Acute Ischemic Stroke (AIS)	Device: Viz.AI software	N/A

Study Design

• Study Type: Interventional
• Actual Enrollment: 443 participants
• Allocation: Randomized
• Intervention Model: Crossover Assignment
• Intervention Model Description: 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.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.
• Masking: None (Open Label)
• Primary Purpose: Diagnostic
• Official Title: Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
• Actual Study Start Date: Jan 1, 2021
• Actual Primary Completion Date: Feb 28, 2022
• Actual Study Completion Date: May 27, 2022

Arms and Interventions

Arm	Intervention/Treatment
Experimental: Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software	Device: Viz.AI software; Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Experimental: Hospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software	Device: Viz.AI software; Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Experimental: Hospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software	Device: Viz.AI software; Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.
Experimental: Hospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software	Device: Viz.AI software; Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.

Outcome Measures

Primary Outcome Measures

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 90 minutes)]

Secondary Outcome Measures

1. Number of patients treated with endovascular stroke therapy [at the time of initiation of endovascular stroke therapy]

2. Number of patients with good functional outcome defined as modified Rankin score (mRS) of 0-2 [90 days]

   The modified Rankin Scale (mRS) is used to assess the degree of disability or dependence in the daily activities of people who have suffered a stroke or other causes of neurological disability. The scales ranges from 0-6, as follows: 0 = No symptoms; 1 = No significant disability. Able to carry out all usual activities, despite some symptoms; 2 = Slight disability. Able to look after own affairs without assistance, but unable to carry out all previous activities; 3 = Moderate disability. Requires some help, but able to walk unassisted; 4 = Moderately severe disability. Unable to attend to own bodily needs without assistance, and unable to walk unassisted; 5 = Severe disability. Requires constant nursing care and attention, bedridden, incontinent; 6 = Dead.

3. Hospital Length of Stay [From the time of admission to the hospital to the time of discharge (about 7 days)]

   The number of days of inpatient hospitalization.

4. Number of patients with intracranial hemorrhage (ICH) [From the time of admission to the hospital to the time of discharge (about 7 days)]

   Number of participants with any intracranial hemorrhage (ICH) and symptomatic ICH (Defined by ECASS II criteria)

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	The University of Texas Health Science Center at Houston	Houston	Texas	United States	77030

Sponsors and Collaborators

• The University of Texas Health Science Center, Houston
• National Center for Advancing Translational Sciences (NCATS)

Investigators

• Principal Investigator: Sunil Sheth, MD, The University of Texas Health Science Center, Houston

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Sunil A. Sheth, Associate Professor, The University of Texas Health Science Center, Houston
• ClinicalTrials.gov Identifier: NCT05838456
• Other Study ID Numbers: HSC-MS-19-0630; UL1TR003167
• First Posted: May 1, 2023
• Last Update Posted: May 1, 2023
• Last Verified: Apr 1, 2023

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: Yes
• Product Manufactured in and Exported from the U.S.: No
• Additional relevant MeSH terms: Stroke; Ischemic Stroke

Study Results

No Results Posted as of May 1, 2023