Table 1_Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study.docx

BackgroundEarly detection of subclinical atherosclerosis progression is crucial for preventing atherosclerotic cardiovascular disease (ASCVD). Carotid intima–media thickness (CIMT) is a recognized surrogate marker for atherosclerosis, but accurate prediction of its progression remains challenging. This study aimed to develop and validate machine learning models for predicting CIMT progression via routine clinical biomarkers.
MethodsIn this three-year prospective cohort study, we analyzed data from 904 participants from the Third Xiangya Hospital of Central South University Health Examination Cohort who underwent three consecutive annual CIMT measurements. The participants were categorized into CIMT thickening and nonthickening groups on the basis of a final CIMT ≥1.0 mm or an increase ≥0.1 mm across consecutive measurements. We evaluated seven machine learning algorithms: logistic regression, random forest, XGBoost, support vector machine (SVM), elastic net, decision tree, and neural network. Model performance was assessed through discrimination (AUC, sensitivity, specificity) and calibration metrics, with Platt scaling applied to optimize probability estimates. Clinical utility was evaluated through decision curve analysis.
ResultsCompared with the more complex algorithms, the elastic net model demonstrated superior performance (AUC 0.754). Baseline CIMT, absolute monocyte count, sex, age, and LDL-C were identified as the most influential predictors. After Platt scaling, the calibration improved significantly across all the models. Decision curve analysis revealed a positive net benefit across a wide threshold range (0.01–0.5). On the basis of calibrated probabilities, we developed a three-tier risk stratification framework that identified distinct groups with progressively higher event rates: medium-risk (13.9%), high-risk (50.0%), and very-high-risk (60.0%). Subgroup analysis revealed better predictive performance in younger participants (<50 years), those with lower baseline CIMT (<0.8 mm), and females.
ConclusionMachine learning approaches, particularly the elastic net model, can effectively identify individuals at high risk for CIMT progression via routine clinical biomarkers. The superior performance of simpler models suggests predominantly linear relationships between predictors and CIMT progression. Following appropriate calibration, the model demonstrated strong clinical utility across diverse decision thresholds, supporting a stratified approach to atherosclerosis prevention.