Dynamic surveillance of the cardiovascular risk and identification of treatment responders in type 2 diabetes using a machine learning–based model

AIMS: To establish a model to predict cardiovascular risk and identify treatment response for patients with type 2 diabetes. METHODS: Data from 11677 patients without prior cardiovascular disease across four clinical trials: ACCORD and CANVAS (80% for training and 20% for holdout) and CANVAS-R and CREDENCE (for external testing) were used to develop and validate the machine learning (ML)-cardiovascular disease (CVD) model. The model utilized baseline and 1-year changes of interim factors for the dynamic prediction of the primary endpoint, including cardiovascular death, nonfatal myocardial infarction, nonfatal stroke. RESULTS: The ML-CVD Primary model demonstrated strong predictive performance with Harrell's C-index of 0.66-0.71 in the holdout and external testing datasets, outperforming traditional scores in primary-prevention subgroup. Intensive interventions (intensive glycemic control and canagliflozin) significantly mitigated the progression of ML-CVD Primary risk scores during the 1-year observation period compared to control treatments. Each standard deviation decrease in the ML-CVD Primary score was significantly associated with a reduced risk of primary cardiovascular outcomes. We stratified patients in the canagliflozin arms based on their score changes: 'Responders' were defined as individuals with a decrease in the ML-CVD Primary score, whereas 'Non-Responders' showed no change or an increase in the score. 'Responders' exhibited a 45% lower risk of primary cardiovascular outcomes compared to 'Non-Responders'. CONCLUSION: The ML-CVD Primary model enables dynamic prediction of cardiovascular events, facilitating ongoing risk surveillance and identification of individual drug responses. This approach holds promise for guiding personalized cardiovascular protective therapies in patients with type 2 diabetes.