Widely used risk equations for cardiovascular (CV) outcomes for individuals with type 2 diabetes mellitus (T2DM) have been incapable of predicting cardioprotective effects observed in recent cardiovascular outcomes trials (CVOTs) involving individuals with T2DM and at high risk for or with established CV disease. We developed CV and mortality risk equations using patient-level data from the CANagliflozin cardioVascular Assessment Study (CANVAS) Program to address this shortcoming.
Data from 10,142 T2DM patients randomized to canagliflozin + SoC or SoC alone with high risk for or with established CV disease followed for a mean duration of 3.6 years in the CANVAS Program were used to derive parametric risk equations for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death. Accumulated knowledge from the widely used United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) was leveraged and any departures in parameterization were limited to those necessary to provide adequate goodness-of-fit. Candidate explanatory covariates were selected using only the placebo arm to minimize confounding effects. Internal validation was performed separately by study treatment arm.
UKPDS-OM2 predicted CANVAS Program outcomes poorly. Recalibrating UKPDS-OM2 intercepts improved calibration in some cases. Refitting the coefficients but otherwise preserving the UKPDS-OM2 structure improved the fit substantially, which was sufficient for stroke and death. For MI, reselecting UKPDS-OM2 covariates and functional form proved sufficient. For HHF, selection from a broad set of candidate covariates and inclusion of a canagliflozin indicator was required.
These risk equations address some of the limitations of widely used risk equations like UKPDS-OM2 for modeling cardioprotective treatments for individuals with T2DM and high CV risk, including derivation from overly healthy patients treated with agents that lack cardioprotection and have been described as reflecting a different therapeutic era. Future work is needed to examine external validity.
PharmacoEconomics 2021 Apr;39(4):447-461