Ignite Creation Date:
2025-12-24 @ 11:19 PM
Ignite Modification Date:
2025-12-25 @ 9:00 PM
Study NCT ID:
NCT05838456
Status:
COMPLETED
Last Update Posted:
2023-06-28
First Post:
2023-04-17
Is NOT Gene Therapy:
False
Has Adverse Events:
True
Brief Title:
Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals
Sponsor:
Organization:
Raw JSON
{'hasResults': True, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24', 'submissionTracking': {'firstMcpInfo': {'postDateStruct': {'date': '2023-06-02', 'type': 'ACTUAL'}}}}, 'conditionBrowseModule': {'meshes': [{'id': 'D000083242', 'term': 'Ischemic Stroke'}], 'ancestors': [{'id': 'D020521', 'term': 'Stroke'}, {'id': 'D002561', 'term': 'Cerebrovascular Disorders'}, {'id': 'D001927', 'term': 'Brain Diseases'}, {'id': 'D002493', 'term': 'Central Nervous System Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'resultsSection': {'moreInfoModule': {'pointOfContact': {'email': 'Sunil.A.Sheth@uth.tmc.edu', 'phone': '713-500-7897', 'title': 'Sunil A. Sheth, MD', 'organization': 'The University of Texas Health Science Center at Houston'}, 'certainAgreement': {'piSponsorEmployee': True}}, 'adverseEventsModule': {'timeFrame': 'From the time of admission to the hospital to the time of discharge (about 7 days)', 'eventGroups': [{'id': 'EG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented', 'otherNumAtRisk': 140, 'deathsNumAtRisk': 140, 'otherNumAffected': 17, 'seriousNumAtRisk': 140, 'deathsNumAffected': 44, 'seriousNumAffected': 7}, {'id': 'EG001', 'title': 'With Viz.AI Software', 'description': '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.', 'otherNumAtRisk': 103, 'deathsNumAtRisk': 103, 'otherNumAffected': 17, 'seriousNumAtRisk': 103, 'deathsNumAffected': 13, 'seriousNumAffected': 2}], 'otherEvents': [{'term': 'non-symptomatic intracerebral hemorrhage (ICH)', 'stats': [{'groupId': 'EG000', 'numAtRisk': 140, 'numEvents': 17, 'numAffected': 17}, {'groupId': 'EG001', 'numAtRisk': 103, 'numEvents': 17, 'numAffected': 17}], 'organSystem': 'Vascular disorders', 'assessmentType': 'SYSTEMATIC_ASSESSMENT'}], 'seriousEvents': [{'term': 'Symptomatic intracerebral hemorrhage (ICH)', 'stats': [{'groupId': 'EG000', 'numAtRisk': 140, 'numEvents': 7, 'numAffected': 7}, {'groupId': 'EG001', 'numAtRisk': 103, 'numEvents': 2, 'numAffected': 2}], 'organSystem': 'Vascular disorders', 'assessmentType': 'SYSTEMATIC_ASSESSMENT'}], 'frequencyThreshold': '0'}, 'outcomeMeasuresModule': {'outcomeMeasures': [{'type': 'PRIMARY', 'title': 'Time From Emergency Room Arrival to Initiation of Endovascular Stroke Therapy ("Door-to-groin" Time)', 'denoms': [{'units': 'Participants', 'counts': [{'value': '140', 'groupId': 'OG000'}, {'value': '103', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'OG001', 'title': 'With Viz.AI Software', 'description': '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.'}], 'classes': [{'categories': [{'measurements': [{'value': '100', 'groupId': 'OG000', 'lowerLimit': '81', 'upperLimit': '116'}, {'value': '88', 'groupId': 'OG001', 'lowerLimit': '65', 'upperLimit': '110'}]}]}], 'paramType': 'MEDIAN', 'timeFrame': 'from the time of emergency room arrival to the time of initiation of endovascular stroke therapy (about 97 minutes)', 'unitOfMeasure': 'minutes', 'dispersionType': 'Inter-Quartile Range', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Number of Patients Who Received With Endovascular Stroke Therapy', 'denoms': [{'units': 'Participants', 'counts': [{'value': '140', 'groupId': 'OG000'}, {'value': '103', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'OG001', 'title': 'With Viz.AI Software', 'description': '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.'}], 'classes': [{'categories': [{'measurements': [{'value': '140', 'groupId': 'OG000'}, {'value': '103', 'groupId': 'OG001'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'timeFrame': 'at the time of initiation of endovascular stroke therapy', 'unitOfMeasure': 'Participants', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Number of Patients With Good Functional Outcome Defined as Modified Rankin Score (mRS) of 0-2', 'denoms': [{'units': 'Participants', 'counts': [{'value': '90', 'groupId': 'OG000'}, {'value': '24', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'OG001', 'title': 'With Viz.AI Software', 'description': '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.'}], 'classes': [{'categories': [{'measurements': [{'value': '29', 'groupId': 'OG000'}, {'value': '10', 'groupId': 'OG001'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'timeFrame': '90 days', 'description': '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.', 'unitOfMeasure': 'Participants', 'reportingStatus': 'POSTED', 'populationDescription': 'mRS data were not collected for 50 in the no Viz.AI software arm and 79 in the with Viz.AI software arm.'}, {'type': 'SECONDARY', 'title': 'Hospital Length of Stay', 'denoms': [{'units': 'Participants', 'counts': [{'value': '140', 'groupId': 'OG000'}, {'value': '103', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'OG001', 'title': 'With Viz.AI Software', 'description': '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.'}], 'classes': [{'categories': [{'measurements': [{'value': '7', 'groupId': 'OG000', 'lowerLimit': '4', 'upperLimit': '12'}, {'value': '6', 'groupId': 'OG001', 'lowerLimit': '3', 'upperLimit': '10'}]}]}], 'paramType': 'MEDIAN', 'timeFrame': 'From the time of admission to the hospital to the time of discharge (about 7 days)', 'description': 'The number of days of inpatient hospitalization.', 'unitOfMeasure': 'days', 'dispersionType': 'Inter-Quartile Range', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Number of Patients With Intracranial Hemorrhage (ICH)', 'denoms': [{'units': 'Participants', 'counts': [{'value': '140', 'groupId': 'OG000'}, {'value': '103', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'OG001', 'title': 'With Viz.AI Software', 'description': '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.'}], 'classes': [{'title': 'Non-Symptomatic ICH', 'categories': [{'measurements': [{'value': '17', 'groupId': 'OG000'}, {'value': '17', 'groupId': 'OG001'}]}]}, {'title': 'Symptomatic ICH', 'categories': [{'measurements': [{'value': '7', 'groupId': 'OG000'}, {'value': '2', 'groupId': 'OG001'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'timeFrame': 'From the time of admission to the hospital to the time of discharge (about 7 days)', 'description': 'Number of participants with any intracranial hemorrhage (ICH) and symptomatic ICH (Defined by ECASS II criteria)', 'unitOfMeasure': 'Participants', 'reportingStatus': 'POSTED'}]}, 'participantFlowModule': {'groups': [{'id': 'FG000', 'title': 'Hospital 1 - 3 Months With no Viz.AI Software, Then 12 Months With Viz.AI Software', 'description': '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.'}, {'id': 'FG001', 'title': 'Hospital 2 - 6 Months With no Viz.AI Software, Then 9 Months With Viz.AI Software', 'description': '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.'}, {'id': 'FG002', 'title': 'Hospital 3 - 9 Months With no Viz.AI Software, Then 6 Months With Viz.AI Software', 'description': '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.'}, {'id': 'FG003', 'title': 'Hospital 4 - 12 Months With no Viz.AI Software, Then 3 Months With Viz.AI Software', 'description': '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.'}], 'periods': [{'title': 'Step 1: Months 1-3', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '38'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '8'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '11'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '23'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '38'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '8'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '11'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '23'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG001', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG002', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG003', 'numUnits': '0', 'numSubjects': '0'}]}]}, {'title': 'Step 2: Months 4-6', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '9'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '3'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '4'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '20'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '9'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '3'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '4'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '20'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG001', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG002', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG003', 'numUnits': '0', 'numSubjects': '0'}]}]}, {'title': 'Step 3: Months 7-9', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '11'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '2'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '5'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '19'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '11'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '2'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '5'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '19'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG001', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG002', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG003', 'numUnits': '0', 'numSubjects': '0'}]}]}, {'title': 'Step 4: Months 10-12', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '7'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '2'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '5'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '9'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '7'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '2'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '5'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '9'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG001', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG002', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG003', 'numUnits': '0', 'numSubjects': '0'}]}]}, {'title': 'Step 4: Months 13-15', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '16'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '7'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '10'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '34'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '1', 'numSubjects': '16'}, {'groupId': 'FG001', 'numUnits': '1', 'numSubjects': '7'}, {'groupId': 'FG002', 'numUnits': '1', 'numSubjects': '10'}, {'groupId': 'FG003', 'numUnits': '1', 'numSubjects': '34'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG001', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG002', 'numUnits': '0', 'numSubjects': '0'}, {'groupId': 'FG003', 'numUnits': '0', 'numSubjects': '0'}]}]}], 'typeUnitsAnalyzed': 'hospitals', 'preAssignmentDetails': '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.'}, 'baselineCharacteristicsModule': {'denoms': [{'units': 'Participants', 'counts': [{'value': '140', 'groupId': 'BG000'}, {'value': '103', 'groupId': 'BG001'}, {'value': '243', 'groupId': 'BG002'}]}], 'groups': [{'id': 'BG000', 'title': 'no Viz.AI Software', 'description': 'Time before Viz.AI software was implemented'}, {'id': 'BG001', 'title': 'With Viz.AI Software', 'description': '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.'}, {'id': 'BG002', 'title': 'Total', 'description': 'Total of all reporting groups'}], 'measures': [{'title': 'Age, Continuous', 'classes': [{'categories': [{'measurements': [{'value': '69.5', 'groupId': 'BG000', 'lowerLimit': '58.5', 'upperLimit': '78'}, {'value': '71', 'groupId': 'BG001', 'lowerLimit': '57', 'upperLimit': '79'}, {'value': '70', 'groupId': 'BG002', 'lowerLimit': '58', 'upperLimit': '79'}]}]}], 'paramType': 'MEDIAN', 'unitOfMeasure': 'years', 'dispersionType': 'INTER_QUARTILE_RANGE'}, {'title': 'Sex: Female, Male', 'classes': [{'categories': [{'title': 'Female', 'measurements': [{'value': '73', 'groupId': 'BG000'}, {'value': '49', 'groupId': 'BG001'}, {'value': '122', 'groupId': 'BG002'}]}, {'title': 'Male', 'measurements': [{'value': '67', 'groupId': 'BG000'}, {'value': '54', 'groupId': 'BG001'}, {'value': '121', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Race/Ethnicity, Customized', 'classes': [{'title': 'Race and Ethnicity', 'categories': [{'title': 'White', 'measurements': [{'value': '58', 'groupId': 'BG000'}, {'value': '50', 'groupId': 'BG001'}, {'value': '108', 'groupId': 'BG002'}]}, {'title': 'Black', 'measurements': [{'value': '42', 'groupId': 'BG000'}, {'value': '27', 'groupId': 'BG001'}, {'value': '69', 'groupId': 'BG002'}]}, {'title': 'Hispanic', 'measurements': [{'value': '25', 'groupId': 'BG000'}, {'value': '16', 'groupId': 'BG001'}, {'value': '41', 'groupId': 'BG002'}]}, {'title': 'Asian', 'measurements': [{'value': '7', 'groupId': 'BG000'}, {'value': '6', 'groupId': 'BG001'}, {'value': '13', 'groupId': 'BG002'}]}, {'title': 'Other', 'measurements': [{'value': '8', 'groupId': 'BG000'}, {'value': '4', 'groupId': 'BG001'}, {'value': '12', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Region of Enrollment', 'classes': [{'title': 'United States', 'categories': [{'measurements': [{'value': '140', 'groupId': 'BG000'}, {'value': '103', 'groupId': 'BG001'}, {'value': '243', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with prior stroke', 'classes': [{'categories': [{'measurements': [{'value': '24', 'groupId': 'BG000'}, {'value': '19', 'groupId': 'BG001'}, {'value': '43', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with prior transient ischemic attack (TIA)', 'classes': [{'categories': [{'measurements': [{'value': '11', 'groupId': 'BG000'}, {'value': '5', 'groupId': 'BG001'}, {'value': '16', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of Participants with hypertension', 'classes': [{'categories': [{'measurements': [{'value': '107', 'groupId': 'BG000'}, {'value': '75', 'groupId': 'BG001'}, {'value': '182', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with hyperlipidemia', 'classes': [{'categories': [{'measurements': [{'value': '55', 'groupId': 'BG000'}, {'value': '33', 'groupId': 'BG001'}, {'value': '88', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with atrial fibrillation', 'classes': [{'categories': [{'measurements': [{'value': '41', 'groupId': 'BG000'}, {'value': '30', 'groupId': 'BG001'}, {'value': '71', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with diabetes', 'classes': [{'categories': [{'measurements': [{'value': '46', 'groupId': 'BG000'}, {'value': '23', 'groupId': 'BG001'}, {'value': '69', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with history of smoking', 'classes': [{'categories': [{'measurements': [{'value': '28', 'groupId': 'BG000'}, {'value': '23', 'groupId': 'BG001'}, {'value': '51', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Number of participants with congestive heart failure', 'classes': [{'categories': [{'measurements': [{'value': '15', 'groupId': 'BG000'}, {'value': '12', 'groupId': 'BG001'}, {'value': '27', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Time from last known well to time of hospital arrival', 'classes': [{'categories': [{'measurements': [{'value': '131.5', 'groupId': 'BG000', 'lowerLimit': '61', 'upperLimit': '472'}, {'value': '147', 'groupId': 'BG001', 'lowerLimit': '65', 'upperLimit': '569'}, {'value': '132', 'groupId': 'BG002', 'lowerLimit': '61', 'upperLimit': '498'}]}]}], 'paramType': 'MEDIAN', 'description': '"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.', 'unitOfMeasure': 'minutes', 'dispersionType': 'INTER_QUARTILE_RANGE'}, {'title': 'Score on the NIH Stroke Scale (NIHSS)', 'classes': [{'categories': [{'measurements': [{'value': '17', 'groupId': 'BG000', 'lowerLimit': '11', 'upperLimit': '23'}, {'value': '16', 'groupId': 'BG001', 'lowerLimit': '11', 'upperLimit': '21'}, {'value': '17', 'groupId': 'BG002', 'lowerLimit': '11', 'upperLimit': '22'}]}]}], 'paramType': 'MEDIAN', 'description': 'NIHSS indicates stroke severity. The score on NIHSS ranges from 0 to 42, with a higher score indicating greater stroke severity:\n\nVery Severe: \\>25 Severe: 15 - 24 Mild to Moderately Severe: 5 - 14 Mild: 1 - 5', 'unitOfMeasure': 'score on a scale', 'dispersionType': 'INTER_QUARTILE_RANGE'}, {'title': 'Score on the Alberta stroke program early CT score (ASPECTS)', 'classes': [{'categories': [{'measurements': [{'value': '9', 'groupId': 'BG000', 'lowerLimit': '7', 'upperLimit': '10'}, {'value': '10', 'groupId': 'BG001', 'lowerLimit': '8', 'upperLimit': '10'}, {'value': '9', 'groupId': 'BG002', 'lowerLimit': '7', 'upperLimit': '10'}]}]}], 'paramType': 'MEDIAN', 'description': '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.', 'unitOfMeasure': 'score on a scale', 'dispersionType': 'INTER_QUARTILE_RANGE'}, {'title': 'Number of participants who received intravenous tissue plasminogen activator (tPA)', 'classes': [{'categories': [{'measurements': [{'value': '63', 'groupId': 'BG000'}, {'value': '48', 'groupId': 'BG001'}, {'value': '111', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}]}}, 'documentSection': {'largeDocumentModule': {'noSap': True, 'largeDocs': [{'date': '2023-02-09', 'size': 452944, 'label': 'Study Protocol', 'hasIcf': False, 'hasSap': False, 'filename': 'Prot_000.pdf', 'typeAbbrev': 'Prot', 'uploadDate': '2023-05-01T20:20', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'CROSSOVER', 'interventionModelDescription': '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.'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 443}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-06', 'completionDateStruct': {'date': '2022-05-27', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-06-03', 'studyFirstSubmitDate': '2023-04-17', 'resultsFirstSubmitDate': '2023-05-02', 'studyFirstSubmitQcDate': '2023-04-27', 'lastUpdatePostDateStruct': {'date': '2023-06-28', 'type': 'ACTUAL'}, 'resultsFirstSubmitQcDate': '2023-06-03', 'studyFirstPostDateStruct': {'date': '2023-05-01', 'type': 'ACTUAL'}, 'resultsFirstPostDateStruct': {'date': '2023-06-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-02-28', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Time From Emergency Room Arrival to Initiation of Endovascular Stroke Therapy ("Door-to-groin" Time)', 'timeFrame': 'from the time of emergency room arrival to the time of initiation of endovascular stroke therapy (about 97 minutes)'}], 'secondaryOutcomes': [{'measure': 'Number of Patients Who Received With Endovascular Stroke Therapy', 'timeFrame': 'at the time of initiation of endovascular stroke therapy'}, {'measure': 'Number of Patients With Good Functional Outcome Defined as Modified Rankin Score (mRS) of 0-2', 'timeFrame': '90 days', 'description': '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.'}, {'measure': 'Hospital Length of Stay', 'timeFrame': 'From the time of admission to the hospital to the time of discharge (about 7 days)', 'description': 'The number of days of inpatient hospitalization.'}, {'measure': 'Number of Patients With Intracranial Hemorrhage (ICH)', 'timeFrame': 'From the time of admission to the hospital to the time of discharge (about 7 days)', 'description': 'Number of participants with any intracranial hemorrhage (ICH) and symptomatic ICH (Defined by ECASS II criteria)'}]}, 'oversightModule': {'isUsExport': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'conditions': ['Acute Ischemic Stroke (AIS)']}, 'referencesModule': {'references': [{'pmid': '37721738', 'type': 'DERIVED', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Male or Female\n* 18 years of age or older.\n* Patients who present to the emergency department with signs and/or symptoms concerning for acute ischemic stroke.\n* Patients who undergo CT angiography imaging\n* 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.\n\nExclusion Criteria:\n\n* Patients with incomplete data on the electronic medical record.'}, 'identificationModule': {'nctId': 'NCT05838456', 'briefTitle': 'Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals', 'organization': {'class': 'OTHER', 'fullName': 'The University of Texas Health Science Center, Houston'}, 'officialTitle': 'Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals', 'orgStudyIdInfo': {'id': 'HSC-MS-19-0630'}, 'secondaryIdInfos': [{'id': 'UL1TR003167', 'link': 'https://reporter.nih.gov/quickSearch/UL1TR003167', 'type': 'NIH'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software', 'interventionNames': ['Device: Viz.AI software']}, {'type': 'EXPERIMENTAL', 'label': 'Hospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software', 'interventionNames': ['Device: Viz.AI software']}, {'type': 'EXPERIMENTAL', 'label': 'Hospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software', 'interventionNames': ['Device: Viz.AI software']}, {'type': 'EXPERIMENTAL', 'label': 'Hospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software', 'interventionNames': ['Device: Viz.AI software']}], 'interventions': [{'name': 'Viz.AI software', 'type': 'DEVICE', 'description': 'Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.', 'armGroupLabels': ['Hospital 1 - 3 months with no Viz.AI software, then 12 months with Viz.AI software', 'Hospital 2 - 6 months with no Viz.AI software, then 9 months with Viz.AI software', 'Hospital 3 - 9 months with no Viz.AI software, then 6 months with Viz.AI software', 'Hospital 4 - 12 months with no Viz.AI software, then 3 months with Viz.AI software']}]}, 'contactsLocationsModule': {'locations': [{'zip': '77030', 'city': 'Houston', 'state': 'Texas', 'country': 'United States', 'facility': 'The University of Texas Health Science Center at Houston', 'geoPoint': {'lat': 29.76328, 'lon': -95.36327}}], 'overallOfficials': [{'name': 'Sunil Sheth, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The University of Texas Health Science Center, Houston'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The University of Texas Health Science Center, Houston', 'class': 'OTHER'}, 'collaborators': [{'name': 'National Center for Advancing Translational Sciences (NCATS)', 'class': 'NIH'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Sunil A. Sheth', 'investigatorAffiliation': 'The University of Texas Health Science Center, Houston'}}}}