Smart-SABI: Digital Phenotyping of Stroke Access Barriers

Middle East North Africa Stroke and Interventional Neurotherapies Organization

Status: Invitation-only

Conditions: Thrombectomy; Stroke; Practice; Healthcare Disparities; Healthcare Access

Treatments: Behavioral: Targeted Stroke Systems of Care Training (SABI-Guided)

Study type: Observational

Funder types: Other

Identifiers

NCT07257146

NALAregistrySABI2026 (Registry Identifier)

MENASINO105

Details and patient eligibility

About

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.

Full 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.

Enrollment: 250 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

250 participants in 1 patient group

Acute Ischemic Stroke (AIS) Patients

Description:

Acute Ischemic Stroke (AIS) Patients This cohort consists of adult patients presenting to the Emergency Department with a confirmed clinical and radiological diagnosis of Acute Ischemic Stroke. The group encompasses a continuous spectrum of arrival times, subsequently stratified during analysis into "Early Arrivers" (presenting within the therapeutic window, typically \< 4.5 hours) and "Late Arrivers" (presenting after the therapeutic window).

Treatment:

Behavioral: Targeted Stroke Systems of Care Training (SABI-Guided)