Construction and validation of a predictive model for poor long-term prognosis in severe acute ischemic stroke after endovascular treatment based on LASSO regression.

Objective: We aimed at establishing a predictive model for poor long-term prognosis (3 months post-treatment) following endovascular treatment (EVT) for severe acute ischemic stroke (AIS) and evaluating its predictive performance. Methods: The patients with severe AIS (NIHSS score ≥ 16) who received EVT were divided into a modeling group (178 patients), an internal validation group (76 patients), and an external validation group (193 patients). Internal and external validation were performed using cross-validation. Poor long-term prognosis was defined as a modified Rankin Scale (mRS) score > 2 at 3 months after the stroke. Univariate analysis and LASSO regression were used to select risk factors, and a logistic regression model was established to create a nomogram. The model's performance and clinical applicability were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves. Results: Five predictive factors were identified: baseline NIHSS score (OR = 1.096, 95% CI: 1.013-1.196, Conclusion: The predictive model for poor long-term prognosis following EVT for severe AIS has accurate predictive value and clinical application potential.