Precision in Stroke Care: Novel Model for Predicting Functional Independence in Urgent Carotid Intervention Patients.

Abstract

Background: Stroke requires timely intervention, with carotid endarterectomy (CEA) and carotid artery stenting (CAS) increasingly used in select acute carotid-related stroke patients. We aimed to build a model to predict neurologic functional independence (modified Rankin scale [mRS] ≤ 2) in this high-risk group.

Study design: We analyzed data from 302 stroke patients undergoing urgent CEA or CAS between 2015 and 2023 at a tertiary comprehensive stroke center. Predictors included (1) stroke severity (NIH Stroke Scale), (2) time to intervention (≤48 hours), (3) thrombolysis use, and (4) frailty risk score. Two-way interactions were included to enhance generalizability without overfitting. Multiple models were constructed and selected based on the area under the receiver operating characteristic curve. The primary endpoint was discharge neurological functional independence (mRS ≤ 2).

Results: Presenting clinical factors and neurological outcomes data from 302 patients undergoing urgent CEA and CAS during the index hospitalization from 2015 to 2023 at a tertiary comprehensive stroke center formed the model's foundation. Most patients (72.8%, 220 of 302) were discharged functionally independent (mRS ≤ 2). The combined 30-day rate of stroke, death, and MI was 8.3% (25 of 302), 6.5% (14 of 214) for CEA alone, and 12.5% (11 of 88) for CAS. The model, incorporating thrombolysis, time to intervention, stroke severity (NIH Stroke Scale), and frailty risk, correctly predicted 93% of functional independence outcomes (area under the receiver operating characteristic curve 0.808).

Conclusions: We present a novel model using 4 clinical factors-stroke severity, time to intervention, thrombolysis use, and frailty risk-to predict functional neurologic independence with 93% accuracy in patients undergoing urgent carotid interventions for acute stroke. This high predictive capability can enhance clinical decision-making and improve patient outcomes by identifying those most likely to benefit from timely carotid revascularization.