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.
Study Type
OBSERVATIONAL
Enrollment
250
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.
Alexandria Stroke and Neurointervention Center
Alexandria, Egypt
Correlation of SABI Score with Infarct Core Volume (The Biological Anchor)
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).
Time frame: Baseline (Admission Imaging)
Predictive Accuracy of ML Model for "High-Risk" Delay
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.
Time frame: Baseline through Study Completion (12 months)
Agreement between Subjective Transport Barriers and GIS Metrics
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.
Time frame: Baseline
Functional Outcome (mRS) at 90 Days
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).
Time frame: 90 Days post-discharge
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