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Smart-SABI: Digital Phenotyping of Stroke Access Barriers
Sponsor: Middle East North Africa Stroke and Interventional Neurotherapies Organization
Summary
This study aims to identify and quantify the non-clinical barriers (social, transport, and knowledge-based) that delay patient arrival at the hospital during an Acute Ischemic Stroke. By utilizing a multimodal approach that combines a validated patient questionnaire (SABI Tool), Geographic Information Systems (GIS) analysis, and biological markers (infarct volume), the investigators seek to develop a Machine Learning model capable of predicting high-risk phenotypes for pre-hospital delay. The ultimate goal is to validate "Social Determinants of Health" against objective biological outcomes.
Official title: Machine Learning Identification of Modifiable Access Barriers in Acute Ischemic Stroke: A Multimodal "Digital Phenotyping" Approach
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
250
Start Date
2025-10-11
Completion Date
2027-04-11
Last Updated
2025-12-02
Healthy Volunteers
Yes
Interventions
Targeted Stroke Systems of Care Training (SABI-Guided)
Implementation of targeted barrier-reduction strategies at selected stroke centers based on baseline SABI profiles. The primary intervention consists of EMS Training Programs focused on stroke recognition, triage protocols, and rapid transport to Mechanical Thrombectomy (MT) capable centers. Comparator/Control: Pre-intervention period (historical control) where standard of care was utilized without the targeted SABI-guided training. Post-Intervention: Assessment of MT utilization rates and SABI scores following the implementation of the training modules.
Locations (1)
Alexandria Stroke and Neurointervention Center
Alexandria, Egypt