Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes
Research Design and Methods: We constructed a topological representation of the Canagliflozin Cardiovascular Assessment Study (CANVAS) using 75 baseline variables collected from 4327 patients with type 2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n=2886) or placebo (n=1441). Within each patient’s 5% neighborhood, we calculated age- and sex-adjusted risk estimates for major adverse cardiovascular events (MACE). An extreme gradient boosting algorithm was trained to predict the personalized ASCVD effect of canagliflozin using features most predictive of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal (CANVAS-R) trial, comprising 5808 patients with type 2 diabetes randomized 1:1 to canagliflozin or placebo.
Results: In CANVAS (mean age 60.9±8.1 years, 33.9% women) 1605 (37.1%) patients had a neighborhood hazard ratio more protective than the effect estimate of 0.86 reported for MACE in the original trial. A 15-variable tool, INSIGHT, trained to predict the personalized ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age 62.4±8.4 years, 2164 (37.3%) women) where it identified patient phenotypes with higher ASCVD effect of canagliflozin (adj. HR 0.60 [95%CI:0.41-0.89] versus 0.99 [95%CI:0.76-1.29]; P for interaction=0.04).
Conclusions: We present an evidence-based, machine learning-guided algorithm to personalize the prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effect.
