Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patients with type 2 diabetes mellitus (T2DM) and subclinical atherosclerosis remains limited; therefore, this study aimed to develop and validate a machine learning-based model for predicting carotid plaque regression in this population.

METHODS: This retrospective study included a development cohort of 204 patients with T2DM and subclinical atherosclerosis receiving combined statin and evolocumab therapy, with external validation performed in an independent cohort. The primary outcome was the change in mean carotid plaque thickness. Thirteen predictors were selected using the Boruta algorithm to construct a Super Learner model, which was validated through repeated five-fold cross-validation. Model interpretability was assessed using SHapley Additive exPlanations analysis.

RESULTS: After 24 weeks of treatment, carotid plaque regression was observed in 52.9% of patients. The model demonstrated strong discriminatory performance in the internal validation set (area under the curve [AUC] = 0.958; sensitivity = 0.881; specificity = 0.908) and moderate performance in the external validation cohort (AUC = 0.755; sensitivity = 0.875; specificity = 0.560). Low-density lipoprotein cholesterol, total cholesterol, high-sensitivity C-reactive protein, high-density lipoprotein cholesterol, and alanine aminotransferase were identified as the most influential predictors.

CONCLUSIONS: This study demonstrates the feasibility of applying machine learning algorithms to predict responses to evolocumab in patients with T2DM and subclinical atherosclerosis. Machine learning may support individualized risk stratification by identifying individuals most likely to benefit from therapy, thereby supporting personalized strategies for secondary cardiovascular disease prevention.