NCT07257146

Smart-SABI: Digital Phenotyping of Stroke Access Barriers

Machine Learning Identification of Modifiable Access Barriers in Acute Ischemic Stroke: A Multimodal "Digital Phenotyping" Approach

• Status: Enrolling By Invitation
• Phase: —
• Study type: Observational
• Enrollment: 250 (estimated)
• Sponsor: Middle East North Africa Stroke and Interventional Neurotherapies Organization · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Detailed description

Despite advances in stroke reperfusion therapies (thrombectomy and thrombolysis), pre-hospital delays remain the primary cause of preventable disability. Current triage systems rely heavily on clinical severity scales but fail to account for Social Determinants of Health (SDOH) that dictate onset-to-door times. This is a prospective, observational, single-center cohort study designed to validate the "Stroke Access Barrier Identification" (SABI) tool using a "Triangulation Strategy." The study employs three distinct data sources: Subjective: Administration of the SABI questionnaire to assess cognitive, physical, and structural barriers. Geospatial (Objective): Network-based GIS analysis to calculate precise drive-time isochrones and public transit density, validating patient reports of transport difficulty. Biological (The "Anchor"): Correlation of barrier scores with Infarct Core Volume (measured via CT-Perfusion/MRI) and 90-day functional outcomes. Data will be processed using interpretable Machine Learning algorithms (Random Forest / XGBoost) and SHAP (SHapley Additive exPlanations) values to identify the specific social features that most strongly predict delayed presentation and increased brain tissue loss.

Conditions

• Stroke
• Practice
• Healthcare Disparities
• Thrombectomy
• Healthcare Access

Interventions

Type	Name	Description
BEHAVIORAL	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.

Timeline

• Start date: 2025-10-11
• Primary completion: 2027-02-11
• Completion: 2027-04-11
• First posted: 2025-12-02
• Last updated: 2025-12-02

Locations

1 site across 1 country: Egypt