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Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Goliasch, G; Silbernagel, G; Kleber, ME; Grammer, TB; Pilz, S; Tomaschitz, A; Bartko, PE; Maurer, G; Koenig, W; Niessner, A; März, W.
Refining Long-Term Prediction of Cardiovascular Risk in Diabetes - The VILDIA Score.
Sci Rep. 2017; 7(1):4700-4700 Doi: 10.1038/s41598-017-04935-8 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG


Leading authors Med Uni Graz
Silbernagel Günther
Co-authors Med Uni Graz
März Winfried
Pilz Stefan
Tomaschitz Andreas

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Cardiovascular risk assessment in patients with diabetes relies on traditional risk factors. However, numerous novel biomarkers have been found to be independent predictors of cardiovascular disease, which might significantly improve risk prediction in diabetic patients. We aimed to improve prediction of cardiovascular risk in diabetic patients by investigating 135 evolving biomarkers. Based on selected biomarkers a clinically applicable prediction algorithm for long-term cardiovascular mortality was designed. We prospectively enrolled 864 diabetic patients of the LUdwigshafen RIsk and Cardiovascular health (LURIC) study with a median follow-up of 9.6 years. Independent risk factors were selected using bootstrapping based on a Cox regression analysis. The following seven variables were selected for the final multivariate model: NT-proBNP, age, male sex, renin, diabetes duration, Lp-PLA2 and 25-OH vitamin D3. The risk score based on the aforementioned variables demonstrated an excellent discriminatory power for 10-year cardiovascular survival with a C-statistic of 0.76 (P < 0.001), which was significantly better than the established UKPDS risk engine (C-statistic = 0.64, P < 0.001). Net reclassification confirmed a significant improvement of individual risk prediction by 22% (95% confidence interval: 14-30%) compared to the UKPDS risk engine (P < 0.001). The VILDIA score based on traditional cardiovascular risk factors and reinforced with novel biomarkers outperforms previous risk algorithms.

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