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.

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.

Inclusion Criteria:

• Diagnosis of Acute Ischemic Stroke (AIS) confirmed by neuroimaging (CT or MRI). Age $\geq$ 18 years. Presentation to the Emergency Department within 7 days of symptom onset (to ensure recall accuracy).

Patient or Legally Authorized Representative (LAR) able to provide informed consent.

Verifiable residential address (required for GIS analysis).

Exclusion Criteria:

• In-hospital stroke onset. Stroke mimics (e.g., seizure, complex migraine, hypoglycemia). Hemorrhagic stroke. Homelessness or lack of fixed address (precludes geospatial analysis). Severe aphasia or cognitive deficit without an available surrogate/caregiver to complete the questionnaire.

The source population comprises patients recruited from 12 tertiary centers across the MENA region: Alexandria University, Ain Shams University, and Cairo University (Egypt); Eskisehir Osmangazi University and Dr. Lutfi Kirdar City Hospital (Turkey); Amman Specialized IR Center (Jordan); King Khalid University, King Abdullah Medical City, and Imam Abdulrahman Al Faisal University (Saudi Arabia); Institute National de Neurology (Tunisia); Cleveland Clinic Abu Dhabi (UAE); and Weill Cornell Medicine (Qatar).

This multinational design ensures significant geographic heterogeneity-ranging from the dense urban traffic of Istanbul and Cairo to the mountainous terrain of Abha-which is critical for GIS transport analysis. Additionally, the inclusion of diverse economic and cultural backgrounds supports robust SABI analysis regarding stroke awareness and health-seeking behaviors across the region.