Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

Study Overview
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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.

Official Title
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Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

Conditions
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Acute Ischemic Stroke (AIS)

Intervention / Treatment
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* Device: Viz.AI software


Participation Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Crossover Assignment
Interventional 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.
Masking: None (Open Label)


Arms and Interventions

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

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 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. | 90 days | 
| Hospital Length of Stay | The number of days of inpatient hospitalization. | From the time of admission to the hospital to the time of discharge (about 7 days) | 
| Number of Patients With Intracranial Hemorrhage (ICH) | Number of participants with any intracranial hemorrhage (ICH) and symptomatic ICH (Defined by ECASS II criteria) | From the time of admission to the hospital to the time of discharge (about 7 days) |