Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

 

Totally wrong objective; Survivors actually want early neurological deterioration prevented! Predictions DO NOTHING TOWARDS RECOVERY! 

What prevents early neurological deterioration is the needed research, not this crapola! You've known of the need for almost a decade but INCOMPETENTLY did this instead! You're fired!

• Early neurological deterioration (36 posts to January 2017)

Development and validation of a machine learning model based on multi-source clinical data for predicting the risk of early neurological deterioration in patients with ischemic stroke

• 

  Yue Li

• W

  Wei Wang

• Y

  Yilan Wei

• J

  Jing Han

• Y

  Yuan Shi

• Q

  Quping Ouyang *

• Neurological Disease Center, Beijing Shunyi District Hospital, Beijing, China

Abstract

Background and objective: 

Early neurological deterioration (END) is a critical clinical event associated with poor patient outcomes after acute ischemic stroke. Early identification of high-risk patients is crucial for timely clinical management. This study aimed to develop and validate a model for predicting END risk for acute ischemic stroke patients using machine learning algorithms.

Methods: 

This study retrospectively and consecutively enrolled 1,151 patients with acute ischemic stroke from the Stroke Center of Beijing Shunyi District Hospital between January 2021 and December 2024. END was defined as progressive worsening of neurological deficit symptoms after onset. Predictive variables were screened using univariate analysis and multiple feature selection methods (Treebag, Boruta, Bayesian). Nine machine learning algorithms (Decision Tree, Efficient Neural Network, K-Nearest Neighbors, Light Gradient Boosting Machine, Logistic Regression, Multilayer Perceptron, Random Forest, Simplified Support Vector Machine, Extreme Gradient Boosting) were employed to construct prediction models. Hyperparameters were optimized via 10-fold cross-validation, and model performance was evaluated in an internal validation cohort (30% of the sample). Primary evaluation metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F1 score, and net benefit from decision curve analysis (DCA). The SHAP method was used to interpret the optimal model.

Results: 

A total of 161 patients (14.0%) developed END. Feature selection ultimately identified five key predictors: ischemic stroke etiological subtype, Oxford Community Stroke Project (OCSP) classification, age, atrial fibrillation history, and prior stroke history. In both the development and internal validation cohorts, the logistic regression model demonstrated favorable and stable performance (development cohort AUC: 0.787, 95% CI: 0.735–0.839; internal validation cohort AUC: 0.751, 95% CI: 0.668–0.834), with a low log-loss value. DCA suggested potential clinical utility of the logistic regression model. SHAP analysis revealed that the etiological subtype of ischemic stroke and age were the features contributing most to the model’s predictions.

Conclusion: 

This study successfully developed and validated a logistic regression model for predicting END risk. The model incorporates five routinely available clinical variables and demonstrates satisfactory predictive performance and interpretability. The developed online tool may assist clinicians in early risk stratification, providing a reference for personalized intervention.