Prediction model for postoperative delirium risk in elderly hypertensive patients: machine learning-based development and validation

Figure 1. Patient screening process and study design diagram. (A) Patient screening process. (B) Data collection and feature selection flowchart. (C) The study cohort was divided into training and validation sets in a 7:3 ratio, followed by the development of ten machine learning models. (D) After model comparison, the best model was selected, visualized, and the risk factors contributing most to the outcome were identified. Based on these risk factors, survival analysis and nomogram model construction were further conducted.

Figure 2. LASSO feature selection for model construction. (A) Binomial deviance as a function of the logarithm of the λ parameter for LASSO regression. (B) Coefficient path of the LASSO regression model as a function of the regularization parameter, λ. LASSO, Least Absolute Shrinkage and Selection Operator.

Figure 3. Machine learning model evaluation curves. (A) Comparison of ROC curves for machine learning models. Logistic, Logistic Regression; SVM, Support Vector Machine; GBM, Gradient Boosting Machine; Neural Network, Neural Network; Random Forest, Random Forest; XGBoost, Extreme Gradient Boosting; KNN, K-Nearest Neighbours; AdaBoost, Adaptive Boosting; LightGBM, Lightweight Gradient Boosting Machine; CatBoost, Categorical Boosting Algorithm. (B) Clinical Decision Curve Analysis (DCA). The x-axis represents the probability threshold for intervention selected by the clinician or decision maker, and the y-axis represents the net benefit of the decision strategy at that threshold. The higher the curve, the more effective the model.

Figure 4. Demographic and clinical feature selection using the LASSO regression. (A) SHAP summary plot of the GBM model. The color represents the value of the variable. (B) SHAP importance for each feature of the GBM model. (C) SHAP force plot of the GBM model.

Figure 5. Nomogram development and logistic regression model-based application for POD. (A) To estimate the POD risk, the value for each clinical variable is aligned with its corresponding “Points” axis at the top to obtain an individual score. The scores for all six variables (MMSE, HADS, Age, CFS, Frail, PSQI) are summed to yield a total points value. This total is then projected downward to the “Risk of POD” axis at the bottom to read the corresponding predicted probability. (B) The patient’s clinical parameters are entered into an online tool developed using the underlying logistic regression model. The platform automatically processes the inputs and generates a real-time POD risk estimate.

Figure 6. Kaplan-Meier survival curves comparing three-year survival probabilities in patients with POD. (A) Survival probability stratified by MMSE score. (B) Survival probability stratified by HADS score. (C) Survival probability stratified by age. (D) Survival probability stratified by CFS score. (E) Survival probability stratified by Frail Scale score. (F) Survival probability stratified by PSQI score. Red texts indicate P-values from Log-rank tests. Abbreviation: POD, postoperative delirium; MMSE, Mini-Mental State Examination; HADS, Hospital Anxiety and Depression Scale; CFS, Clinical Frailty Scale; PSQI, Pittsburgh Sleep Quality Index.