Development and validation of the eMCI-CHD tool: A multivariable prediction model for the risk of mild cognitive impairment in patients with coronary heart disease.

Abstract

This study aimed to develop and validate an eMCI-CHD tool based on clinical data to predict mild cognitive impairment (MCI) risk in patients with coronary heart disease (CHD). This cross-sectional study prospectively collected data from 400 patients with coronary heart disease (aged 55-90 years, 62% men) from July 2022 to September 2023 and randomized (7:3 ratio) them into training and validation sets. After determining the modeling variables through least absolute shrinkage and selection operator regression analysis, four ML classifiers were developed: logistic regression, extreme gradient boosting (XGBoost), support vector machine, and random forest. The performance of the models was evaluated using area under the ROC curve, accuracy, sensitivity, specificity, and F1 score. Decision curve analysis was used to assess the clinical performance of the established models. The SHapley Additive exPlanations (SHAP) method was applied to determine the significance of the features, the predictive model was visualized with a nomogram, and an online web-based calculator for predicting CHD-MCI risk scores was developed. Of 400 CHD patients (average age 70.86±8.74 years), 220 (55%) had MCI. The XGBoost model demonstrated superior performance (AUC: 0.86, accuracy: 78.57%, sensitivity: 0.74, specificity: 0.84, F1: 0.79) and underwent validation. An online tool (https://mr.cscps.com.cn/mci/index.html) with seven predictive variables (APOE gene typing, age, education, TyG index, NT-proBNP, C-reactive protein, and occupation) assessed MCI risk in CHD patients. This study highlights the potential for predicting MCI risk among CHD patients using an ML model-driven nomogram and risk scoring tool based on clinical data.