All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease

Copetti, Massimiliano; Biancalana, Edoardo; Fontana, Andrea; Parolini, Federico; Garofolo, Monia; Lamacchia, Olga; De Cosmo, Salvatore; Trischitta, Vincenzo; Solini, Anna

AIMS: The rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden. METHODS: We tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up. RESULTS: ENFORCE’s survival C-statistic was 0.81 (95%CI: 0.72–0.89) and 0.78 (95%CI: 0.68–0.87) in both samples. Calibration was also good. Very similar results were obtained with RECODe, an alternative prediction model of all-cause mortality in type 2 diabetes. CONCLUSIONS: In conclusion, our data show that two well-established prediction models of all-cause mortality in type 2 diabetes can also be successfully applied in the early stage of the disease, thus becoming powerful tools for educated and timely prevention strategies for high-risk patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00592-021-01746-2.