- 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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