Validation of the ESC/EASD cardiovascular risk stratification model in diabetic patients. (25th November 2020) - Prausmueller, S; Resl, M; Arfsten, H; Spinka, G; Wurm, R; Neuhold, S; Bartko, P; Goliasch, G; Strunk, G; Pavo, N; Clodi, M; Huelsmann, M
Abstract: Background: Recently, the European Society of Cardiology (ESC) and European Association for the Study of Diabetes (EASD) introduced a new cardiovascular (CV) risk stratification model to aid further treatment decisions in individuals with diabetes. Our study aimed to validate the ESC/EASD CV risk assessment in type 2 diabetes mellitus (T2DM) patients for the first time and to compare the prognostic significance of NT-proBNP with the ESC/EASD CV risk stratification model. Methods and results: We prospectively included 2186 T2DM patients with a follow-up of 60 months. Patients were stratified according to ESC/EASD CV risk criteria, 33 (1.5%) were classified as moderate, 321 (14.9%) high and 1453 (67.6%) very high risk. Both, the ESC/EASD risk stratification model and NT-proBNP were associated with CV outcome (CV events: adj. HR: 1.47 [1.09–1.98], p=0.011 and adj. HR per IQR: 2.61 [2.27–3.01], p<0.0005; cardiac events: adj. HR: 1.57 [1.09–2.25], p=0.016 and adj. HR per IQR: 2.94 [2.50–3.47], p<0.0005; respectively) but only NT-proBNP proved to independently predict all-cause mortality (adj. HR per IQR: 2.32 [1.93–2.80], p<0.0005). NT-proBNP outperformed the ESC/EASD CV risk stratification model as determined by C-statistic (CV events: 0.74 vs 0.55, p<0.001; cardiac events: 0.76 vs 0.54, p<0.001; all-cause mortality: 0.73 vs 0.52, p<0.001; Figure 1) and net reclassification improvement of 69% for CV events, 73% for cardiac events and 67% for all-cause death (p<0.001 for all). Conclusion: NT-proBNP measurement provides a valid tool to identify T2DM patients at risk and shows superiority in terms of risk prediction and discriminatory power compared to the ESC/EASD CV assessment. Funding Acknowledgement: Type of funding source: Public grant(s) – National budget only. Main funding source(s): Grant from the Austrian Science Fund