Macrovascular Risk Equations Based on the CANVAS Program.

Area of Science:

• Cardiology
• Endocrinology
• Biostatistics

Background:

• Existing cardiovascular risk equations for type 2 diabetes mellitus (T2DM) fail to predict cardioprotective effects seen in recent trials.
• These limitations are particularly evident in high-risk patients with established cardiovascular disease (CVD).

Purpose of the Study:

• To develop novel cardiovascular and mortality risk equations using patient-level data from the CANVAS Program.
• To address the shortcomings of current risk prediction models in individuals with T2DM and high CVD risk.

Main Methods:

• Patient-level data from 10,142 individuals with T2DM and high CVD risk from the CANVAS Program were analyzed.
• Parametric risk equations were derived for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death, leveraging UKPDS-OM2 structure.
• Internal validation was performed separately by study treatment arm (canagliflozin + standard of care vs. standard of care alone).

Main Results:

• The UKPDS-OM2 model poorly predicted outcomes in the CANVAS Program cohort.
• Refitting coefficients and reselecting covariates within the UKPDS-OM2 framework substantially improved model fit for stroke, death, and MI.
• Predicting HHF required a broader covariate selection and inclusion of a canagliflozin indicator.

Conclusions:

• The newly developed risk equations improve upon limitations of existing models like UKPDS-OM2 for T2DM patients at high cardiovascular risk.
• These equations are better suited for modeling cardioprotective treatments, unlike older models derived from different patient populations and therapeutic eras.
• Further research is necessary to validate these equations externally.