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| ID | Type | Description | Link |
|---|---|---|---|
| UL1TR003167 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Center for Advancing Translational Sciences (NCATS) | NIH |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software | Experimental |
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| Hospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software | Experimental |
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| Hospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software | Experimental |
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| Hospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software | Experimental |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Viz.AI software | Device | Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team. |
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| 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) |
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Sunil Sheth, MD | The University of Texas Health Science Center, Houston | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The University of Texas Health Science Center at Houston | Houston | Texas | 77030 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37721738 | Derived | 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. |
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443 were enrolled, but 200 were excluded before assignment to groups. 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). Each participant was only part of the study for one single period, in other words, participants did not progress to future periods.
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| ID | Title | Description |
|---|---|---|
| FG000 | Hospital 1 - 3 Months With no Viz.AI Software, Then 12 Months With Viz.AI Software | Hospital will have 3 months with no Viz.AI software then 12 months with Viz.AI software integrated into the care pathway. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions on CT angiography (CTA) images and alerts the AIS care team. |
| FG001 | Hospital 2 - 6 Months With no Viz.AI Software, Then 9 Months With Viz.AI Software | Hospital will have 6 months with no Viz.AI software then 9 months with Viz.AI software integrated into the care pathway. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions on CT angiography (CTA) images and alerts the AIS care team. |
| FG002 | Hospital 3 - 9 Months With no Viz.AI Software, Then 6 Months With Viz.AI Software | Hospital will have 9 months with no Viz.AI software then 6 months with Viz.AI software integrated into the care pathway. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions on CT angiography (CTA) images and alerts the AIS care team. |
| FG003 | Hospital 4 - 12 Months With no Viz.AI Software, Then 3 Months With Viz.AI Software | Hospital will have 12 months with no Viz.AI software then 3 months with Viz.AI software integrated into the care pathway. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions on CT angiography (CTA) images and alerts the AIS care team. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Step 1: Months 1-3 |
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| Step 2: Months 4-6 |
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| Step 3: Months 7-9 |
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| Step 4: Months 10-12 |
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| Step 4: Months 13-15 |
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| ID | Title | Description |
|---|---|---|
| BG000 | no Viz.AI Software | Time before Viz.AI software was implemented |
| BG001 | With Viz.AI Software | Time after Viz.AI software was implemented. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team. |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Median |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Time From Emergency Room Arrival to Initiation of Endovascular Stroke Therapy ("Door-to-groin" Time) | Posted | Median | Inter-Quartile Range | minutes | from the time of emergency room arrival to the time of initiation of endovascular stroke therapy (about 97 minutes) |
|
From the time of admission to the hospital to the time of discharge (about 7 days)
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | no Viz.AI Software | Time before Viz.AI software was implemented | 44 |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Symptomatic intracerebral hemorrhage (ICH) | Vascular disorders | Systematic Assessment |
| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| non-symptomatic intracerebral hemorrhage (ICH) | Vascular disorders | Systematic Assessment |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Sunil A. Sheth, MD | The University of Texas Health Science Center at Houston | 713-500-7897 | Sunil.A.Sheth@uth.tmc.edu |
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Feb 9, 2023 | May 1, 2023 | Prot_000.pdf |
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| ID | Term |
|---|---|
| D000083242 | Ischemic Stroke |
| ID | Term |
|---|---|
| D020521 | Stroke |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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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.
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| 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) |
| COMPLETED |
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| NOT COMPLETED |
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| COMPLETED |
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| NOT COMPLETED |
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| COMPLETED |
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| NOT COMPLETED |
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| COMPLETED |
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| NOT COMPLETED |
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| BG002 | Total | Total of all reporting groups |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
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| Race/Ethnicity, Customized | Count of Participants | Participants |
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| Region of Enrollment | Count of Participants | Participants |
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| Number of participants with prior stroke | Count of Participants | Participants |
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| Number of participants with prior transient ischemic attack (TIA) | Count of Participants | Participants |
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| Number of Participants with hypertension | Count of Participants | Participants |
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| Number of participants with hyperlipidemia | Count of Participants | Participants |
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| Number of participants with atrial fibrillation | Count of Participants | Participants |
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| Number of participants with diabetes | Count of Participants | Participants |
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| Number of participants with history of smoking | Count of Participants | Participants |
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| Number of participants with congestive heart failure | Count of Participants | Participants |
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| Time from last known well to time of hospital arrival | "Last known well" is the time prior to hospital arrival at which it was witnessed or reported that the patient was last known to be without the signs and symptoms of the current stroke or at his or her baseline state of health. | Median | Inter-Quartile Range | minutes |
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| Score on the NIH Stroke Scale (NIHSS) | NIHSS indicates stroke severity. The score on NIHSS ranges from 0 to 42, with a higher score indicating greater stroke severity: Very Severe: >25 Severe: 15 - 24 Mild to Moderately Severe: 5 - 14 Mild: 1 - 5 | Median | Inter-Quartile Range | score on a scale |
|
| Score on the Alberta stroke program early CT score (ASPECTS) | The Alberta stroke program early CT score (ASPECTS) is a 10-point quantitative topographic CT scan score used for stroke patients. Total score ranges from 0-10, with a lower score indicating a greater number of brain regions affected by stroke. An ASPECTS score less than or equal to 7 predicts a worse functional outcome at 3 months as well as symptomatic hemorrhage. | Median | Inter-Quartile Range | score on a scale |
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| Number of participants who received intravenous tissue plasminogen activator (tPA) | Count of Participants | Participants |
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| Units | Counts |
|---|
| Participants |
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| Secondary | Number of Patients Who Received With Endovascular Stroke Therapy | Posted | Count of Participants | Participants | at the time of initiation of endovascular stroke therapy |
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|
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| Secondary | 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. | mRS data were not collected for 50 in the no Viz.AI software arm and 79 in the with Viz.AI software arm. | Posted | Count of Participants | Participants | 90 days |
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| Secondary | Hospital Length of Stay | The number of days of inpatient hospitalization. | Posted | Median | Inter-Quartile Range | days | From the time of admission to the hospital to the time of discharge (about 7 days) |
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| Secondary | Number of Patients With Intracranial Hemorrhage (ICH) | Number of participants with any intracranial hemorrhage (ICH) and symptomatic ICH (Defined by ECASS II criteria) | Posted | Count of Participants | Participants | From the time of admission to the hospital to the time of discharge (about 7 days) |
|
|
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| 140 |
| 7 |
| 140 |
| 17 |
| 140 |
| EG001 | With Viz.AI Software | Time after Viz.AI software was implemented. Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team. | 13 | 103 | 2 | 103 | 17 | 103 |
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| D009422 |
| Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |