An interpretable machine learning model for predicting prognosis in acute ischemic stroke with large vessel occlusion.

Objective: To develop and validate an interpretable nomogram that integrates an imaging score, clinical characteristics, and machine-learning models to predict 90-day functional outcomes in patients with acute ischemic stroke (AIS) and large-vessel occlusion (LVO). Methods: We analyzed 240 AIS patients with anterior circulation LVO who underwent one-stop multimodal CT between October 2019 and December 2024. Fifty-two variables, including pretreatment clinical features, conventional and advanced imaging, and angiographic characteristics, were assessed. The 90-day modified Rankin Scale (mRS-90) was used as the prognostic endpoint; good and poor outcomes were defined as mRS-90 ≤ 2 and > 2, respectively. Patients were randomly assigned to training (80%, Results: Among 240 patients, 162 (67.5%) had unfavorable 90-day functional outcomes. Patients with unfavorable outcomes were generally older, had higher admission NIHSS scores, and showed less favorable collateral and perfusion profiles. Age, admission NIHSS score, and onset-to-CT time were retained as clinical predictors, while eight imaging features were integrated into the imaging score. Adding the imaging score did not significantly improve discrimination in the testing cohort (ΔAUC, 0.0078; 95% CI, -0.0071 to 0.0227; Conclusions: An interpretable model combining routine clinical predictors with a multimodal CT-derived imaging score demonstrates promising predictive potential for 90-day functional outcome in patients with AIS-LVO.