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

 

ABSOLUTELY FUCKING USELESS! Predictions/'assessments' DO NOTHING to get survivors recovered! Solve the correct problem: prevent this from happening!

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

Yingli Zhang^1,2Yan Guo¹Zhenpeng Zhang³Jie Han^1*

• ¹The First Affiliated Hospital of Dalian Medical University, Dalian, China
• ²Panjin Central Hospital, Panjin, China
• ³Shanghai Medical College, Fudan University, Shanghai, China

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, 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.

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

1 Introduction

Severe acute ischemic stroke (AIS) is characterized by a sudden onset, rapid progression, and high severity, often leading to significant disability and mortality, imposing a substantial burden on patients’ families. Currently, endovascular treatment (EVT) is the frontline therapeutic strategy for patients with severe AIS (1). This approach is essential for timely vascular recanalization, restoration of blood flow to the infarcted area, and mitigation of brain tissue damage. However, despite successful recanalization of the occluded vessels, nearly half of these patients experience poor functional outcomes within 90 days post-stroke onset (2, 3). Patients were assigned to the favorable outcome group (90-day mRS ≤2) and the poor outcome group (90-day mRS >2). Identifying the factors that influence these functional outcomes is therefore crucial for improving prognosis. Due to the acute onset, severe condition, and high mortality of these patients, conducting clinical research in this population is extremely challenging. As a result, studies on this group remain limited (4, 5). The existing studies are predictive models generally limited to anterior or posterior circulation cases (6, 7). Endovascular treatment is currently the most effective approach for these patients; however, no systematic studies or comprehensive data are available to assess its benefit rate.

Several previous studies have analyzed clinical factors influencing prognosis, including age, NIHSS score, and symptomatic hemorrhage. However, these findings have often been limited to logistic regression with moderate predictive power (8–11). Although factors affecting the prognosis of endovascular treatment for acute severe ischemic stroke have been explored, no predictive models specifically targeting long-term outcomes in these patients have been developed. In recent years, various machine learning algorithms have been applied in clinical research, such as decision tree algorithms, support vector machines (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) (12, 13). Advancements in machine learning and deep learning technologies have significantly improved the performance of various predictive models, highlighting the need for a dedicated model to predict long-term outcomes in this critical patient population.

Previous studies have explored various factors influencing EVT outcomes in severe AIS but have yet to establish a predictive model applicable to clinical practice. In this study we analyzed clinical data using the Least Absolute Shrinkage and Selection Operator (LASSO) (14) regression to identify valuable predictors and established a predictive model for long-term poor prognosis following EVT in severe AIS. LASSO regression effectively handles multicollinearity and prevents overfitting. By employing this technique, we identified the most predictive variables from a broad range of potential risk factors, significantly enhancing the model’s precision and predictive power, which have been well-documented across various medical research fields. The model underwent both internal and external validation, providing new insights for early diagnosis of poor long-term outcomes in this patient population. Such a model would improve prognostic assessments and targeted clinical decisions. Additionally, it would encourage practitioners to enhance thrombectomy techniques and streamline treatment processes.

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