Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score

Abstract 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...

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Autores principales:	Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner, Winfried März
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d23345ba0f04e5e87a08600de888918
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spelling	oai:doaj.org-article:8d23345ba0f04e5e87a08600de8889182021-12-02T16:07:06ZRefining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score10.1038/s41598-017-04935-82045-2322https://doaj.org/article/8d23345ba0f04e5e87a08600de8889182017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04935-8https://doaj.org/toc/2045-2322Abstract 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.Georg GoliaschGünther SilbernagelMarcus E. KleberTanja B. GrammerStefan PilzAndreas TomaschitzPhilipp E. BartkoGerald MaurerWolfgang KoenigAlexander NiessnerWinfried MärzNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Georg Goliasch Günther Silbernagel Marcus E. Kleber Tanja B. Grammer Stefan Pilz Andreas Tomaschitz Philipp E. Bartko Gerald Maurer Wolfgang Koenig Alexander Niessner Winfried März Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
description	Abstract 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.
format	article
author	Georg Goliasch Günther Silbernagel Marcus E. Kleber Tanja B. Grammer Stefan Pilz Andreas Tomaschitz Philipp E. Bartko Gerald Maurer Wolfgang Koenig Alexander Niessner Winfried März
author_facet	Georg Goliasch Günther Silbernagel Marcus E. Kleber Tanja B. Grammer Stefan Pilz Andreas Tomaschitz Philipp E. Bartko Gerald Maurer Wolfgang Koenig Alexander Niessner Winfried März
author_sort	Georg Goliasch
title	Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
title_short	Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
title_full	Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
title_fullStr	Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
title_full_unstemmed	Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score
title_sort	refining long-term prediction of cardiovascular risk in diabetes – the vildia score
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/8d23345ba0f04e5e87a08600de888918
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Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score

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