Healthcare Access, Healthcare Disparities, Practice, Stroke, Thrombectomy
Conditions
Keywords
thrombectomy access, SABI, Stroke Access Barriers Index
Brief 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.
Interventions
BEHAVIORALTargeted 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.
Sponsors
Middle East North Africa Stroke and Interventional Neurotherapies Organization
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation of SABI Score with Infarct Core Volume (The Biological Anchor) | Baseline (Admission Imaging) | To validate if subjective barriers correlate with objective physiological damage. The total score on the SABI questionnaire (Scale 0-100, higher scores indicate higher barriers) will be correlated with the admission Infarct Core Volume (measured in milliliters via automated CT-Perfusion software). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Functional Outcome (mRS) at 90 Days | 90 Days post-discharge | Correlation between baseline SABI Barrier Score and the Modified Rankin Scale (mRS) score at 90 days. The mRS is a scale from 0 (no symptoms) to 6 (dead). |
| Predictive Accuracy of ML Model for High-Risk Delay | Baseline through Study Completion (12 months) | Sensitivity and Specificity of the XGBoost Machine Learning model in classifying patients as Early Arrivers vs. Late Arrivers (defined as \>4.5 hours from Last Known Well) using combined clinical and SABI variables. |
| Agreement between Subjective Transport Barriers and GIS Metrics | Baseline | Cohen's Kappa coefficient measuring agreement between patient-reported Difficulty with Transport (SABI Domain 2) and objective Network Drive Time calculated via ArcGIS using historical traffic data. |
Countries
Egypt
Outcome results
None listed