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

Figures

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  Figure 1. The flowchart of baseline analysis participant selection. Flow diagram of participant selection for the baseline analysis. Among 3,512 patients diagnosed with cerebral infarction, 689 with a modified Rankin Scale (mRS) score >2 were excluded, leaving 2,823 eligible patients. After excluding 568 patients with an onset-to-admission time >72 h and 1,104 patients with missing 90-day follow-up data, 1,151 patients constituted the final analytic cohort. mRS, modified Rankin Scale.

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  Figure 2. Restricted cubic spline (RCS) plots for different continuous variables. Restricted cubic spline (RCS) analysis of the relationship between age and the risk of early neurological deterioration (END). The solid curve shows the estimated odds ratio (OR) with its 95% confidence interval (shaded band); the dashed horizontal line indicates an OR of 1 (no association). The overall association was statistically significant (p = 0.001), whereas the non-linear component was not (p = 0.325). END, early neurological deterioration; OR, odds ratio; RCS, restricted cubic spline.

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  Figure 3. Cross-validation performance of nine machine learning (ML) models in the development set. Box-and-whisker plots showing the cross-validation performance of the nine machine learning (ML) models for predicting early neurological deterioration (END): multilayer perceptron (MLP), light gradient boosting machine (LightGBM), elastic net (ENet), logistic regression (Logistic), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), random forest (RF), reduced support vector machine (RSVM), and decision tree (DT). The three panels show the area under the receiver operating characteristic curve (AUC), sensitivity (Sens), and specificity (Spec) of each model, expressed as the mean ± SD with 95% confidence intervals, obtained by repeated cross-validation within the development set. END, early neurological deterioration; DT, decision tree; ENet, elastic net; KNN, K-nearest neighbors; LightGBM, light gradient boosting machine; MLP, multilayer perceptron; RF, random forest; AUC, area under the receiver operating characteristic curve; RSVM, reduced support vector machine; XGBoost, extreme gradient boosting.

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  Figure 4. Performance of machine learning (ML) models predicting early neurological deterioration in the development and validation sets. Performance of the nine machine learning (ML) models for predicting early neurological deterioration (END) in the development (n = 806) and validation (n = 345) sets. (A,B) Receiver operating characteristic (ROC) curves of the nine models in the development (A) and validation (B) sets; the area under the curve (AUC) for each model is indicated. (C,D) Calibration plots comparing the observed and predicted risk in the development (C) and validation (D) sets. (E,F) Comparison of the performance metrics (accuracy, Kappa, Matthews correlation coefficient [MCC], negative and positive predictive values, precision, recall, F-measure, J index, ROC AUC, sensitivity, and specificity) across the nine models in the development (E) and validation (F) sets. (G,H) Decision curve analysis showing the net benefit of the models across threshold probabilities in the development (G) and validation (H) sets; the grey lines represent the treat-all and treat-none strategies. AUC, area under the curve; END, early neurological deterioration; ML, machine learning; ROC, receiver…

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  Figure 5. Variable heatmap of the logistic regression model for predicting early neurological deterioration. Heatmap of the predictor variables of the logistic regression (LR) model for predicting early neurological deterioration (END). Each row represents a variable and each column represents an individual patient; the color scale indicates the value of each variable. The rows comprise the predicted probability from the LR model, the true outcome group (early versus non-early neurological deterioration), age, indicator variables for ischaemic stroke subtype and OCSP classification, history of atrial fibrillation and stroke, and the dataset origin (development or validation set). The heatmap allows the variables underlying the model predictions to be visualized across the patient cohort. END, early neurological deterioration; LR, logistic regression; OCSP, Oxfordshire Community Stroke Project.

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  Figure 6. Global and local model explanation by the SHapley Additive exPlanations (SHAP) method. SHapley Additive exPlanations (SHAP) analysis of the final machine learning model for predicting early neurological deterioration (END). (A) Global feature importance: Predictor variables ranked by their mean absolute SHAP value (ischaemic stroke subtype, age, OCSP classification, history of stroke, and history of atrial fibrillation). (B) SHAP summary (beeswarm) plot in which each point represents a patient and the color indicates the feature value (yellow, high; purple, low). (C) Local explanation: SHAP waterfall plot for an individual patient showing how each feature contributed to the model output on the log-odds scale (f(x) = 1.17) relative to the base value (E[f(x)] = 1.06). (D) SHAP dependence plots showing the effect of individual variables on the model output. END, early neurological deterioration; OCSP, Oxfordshire Community Stroke Project; SHAP, SHapley Additive exPlanations.

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  Figure 7. Web-based calculator for predicting early neurological deterioration. Screen capture of the web-based dynamic nomogram (risk calculator) for predicting early neurological deterioration (END). The interface allows clinicians to enter individual patient values for the model predictors and returns the corresponding predicted probability of END, supporting individualized risk assessment in clinical practice. END, early neurological deterioration.