Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model

Abstract

Context: We previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life patients with type 2 diabetes.

Objective: This model, now named ENFORCE (EstimatioN oF mORtality risk in type 2 diabetiC patiEnts), was investigated in terms of (i) prediction performance at 6 years, a more clinically useful time-horizon; (ii) further validation in an independent sample; and (iii) performance comparison in a real-life vs a clinical trial setting.

Design: Observational prospective randomized clinical trial.

Setting: White patients with type 2 diabetes.

Patients: Gargano Mortality Study (GMS; n = 1019), Foggia Mortality Study (FMS; n = 1045), and Pisa Mortality Study (PMS; n = 972) as real-life samples and the standard glycemic arm of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) clinical trial (n = 3150).

Main outcome measure: The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the model's performances.

Results: ENFORCE yielded 6-year mortality C-statistics of 0.79, 0.78, and 0.75 in GMS, FMS, and PMS, respectively (P heterogeneity = 0.71). Pooling the three cohorts showed a 6-year mortality C-statistic of 0.80. In the ACCORD trial, ENFORCE achieved a C-statistic of 0.68, a value significantly lower than that obtained in the pooled real-life samples (P < 0.0001). This difference resembles that observed with other models comparing real-life vs clinical trial settings, thus suggesting it is a true, replicable phenomenon.

Conclusions: The time horizon of ENFORCE has been extended to 6 years and validated in three independent samples. ENFORCE is a free and user-friendly risk calculator of all-cause mortality in white patients with type 2 diabetes from a real-life setting.

Copyright © 2019 Endocrine Society.

Figures

Figure 1.

Calibration plot. Expected Kaplan-Meier mortality rate, based on the proposed 6-year all-cause mortality prediction model in the Italian pooled sample, vs observed Kaplan-Meier mortality rate.

Figure 2.

Kaplan-Meier survival curves for the four risk categories as determined by the survival conditional tree analysis. Asterisk indicates upper bound included.