Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data

In acute ischemic stroke due to large-vessel occlusion (AIS-LVO), late-window endovascular thrombectomy (EVT) decisions depend on penumbra-to-core (P:C) mismatch from computed tomographic perfusion (CTP). We developed multiple machine learning (ML) models to predict P:C ratios from a retrospectively-identified cohort of AIS-LVO patients who underwent CTP within 30 min of initial neuroimaging, using non-imaging electronic health record (EHR) data available prior to CTP evaluation. We extracted structured data and free-text clinical notes from the EHR, generating document embeddings as sums of BioWordVec vectors weighted by term-frequency-inverse-document-frequency scores. We identified 120 patients; an extreme-gradient-boosting model classified P:C ratios as ≥ or <1.8, achieving an AUROC of 0.80 (95% CI 0.57–0.92) with optimal performance using text limited to 500 characters. Sensitivity was 0.80, specificity 0.66, and F1 score 0.86. Our findings suggest that ML models leveraging real-world non-imaging data can potentially aid LVO-AIS triage, though further validation is needed.