Comparing machine learning to existing risk scores when predicting CVD in type 2 diabetes patients

Music City Center 

Type 2 diabetes (T2D) increases risk of cardiovascular disease (CVD). Several calculators have been developed to estimate risk of CVD; however, they may underestimate risk in populations such as people with T2D. The Look AHEAD randomized clinical trial tested a behavioral weight-loss intervention in overweight/obese adults with T2D. Fatal and non-fatal CVD was the primary outcome and congestive heart failure (CHF) was a secondary outcome. We use repository data from 4685 Look AHEAD participants to build models to predict survival probability for the time to primary outcome (number of events, ne=763) and CHF (ne=201) using different machine learning (ML) algorithms. The best model from ML algorithms is chosen by comparing their discrimination, calibration and overall accuracy using the C-index, the D-calibration index, and the integrated Brier score, respectively. We then use data from the ACCORD study to validate our ML model and check whether it is better than the PREVENT calculator, the Framingham risk score, and the ACC/AHA pooled cohort equations calculator in predicting these two outcomes. This lets us determine if these calculators need to be improved for people with T2D.

survival outcome prediction

model selection

model validation

Risk prediction 

Section on Statistics in Epidemiology