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

## TL;DR

This study developed a model to predict poor long-term outcomes in severe stroke patients after a specific treatment, using patient data and statistical methods.

## Contribution

A novel predictive model using LASSO regression and clinical factors to assess long-term prognosis after stroke treatment.

## Key findings

- Five factors were identified as predictors of poor prognosis: NIHSS score, intracranial hemorrhage, reperfusion time, age, and collateral circulation.
- The model showed strong predictive accuracy with AUC values above 0.78 in all validation groups.
- The model demonstrated good calibration and clinical utility for predicting outcomes after stroke treatment.

## Abstract

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.

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.

Five predictive factors were identified: baseline NIHSS score (OR = 1.096, 95% CI: 1.013–1.196, p = 0.0279), symptomatic intracranial hemorrhage (OR = 6.912, 95% CI: 1.758–46.902, p = 0.0156), time from puncture to reperfusion (OR = 1.015, 95% CI: 1.003–1.028, p = 0.0158), age (OR = 1.037, 95% CI: 1.002–1.076, p = 0.0412), which were found to be risk factors for poor long-term prognosis after EVT for severe AIS. Collateral circulation was identified as a protective factor (OR = 0.629, 95% CI: 0.508–0.869, p = 0.0055). Based on these five factors, a nomogram was constructed to predict poor long-term prognosis after EVT. The ROC curve showed that the AUC for predicting poor long-term prognosis was 0.7886 (95% CI: 0.7225–0.8546) in the modeling group, 0.8337 (95% CI: 0.7425–0.9249) in the internal validation group, and 0.8357 (95% CI: 0.7793–0.8921) in the external validation group. The calibration curve and clinical decision curve demonstrated good consistency and clinical utility of the model.

The predictive model for poor long-term prognosis following EVT for severe AIS has accurate predictive value and clinical application potential.