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Refining long-term prediction of cardiovascular risk in diabetes: the VILDIA score
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Veröffentlicht in: | Scientific reports 7(2017) Artikel-Nummer 4700, 9 Seiten |
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Titel: | Refining long-term prediction of cardiovascular risk in diabetes: the VILDIA score/ Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März |
Format: | E-Book-Kapitel |
Sprache: | Englisch |
veröffentlicht: |
05 July 2017
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Gesamtaufnahme: |
: Scientific reports, 7(2017) Artikel-Nummer 4700, 9 Seiten
, volume:7 |
Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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author | Goliasch, Georg, Kleber, Marcus E., Grammer, Tanja B., März, Winfried |
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contents | 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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spelling | Goliasch, Georg 1985- VerfasserIn (DE-588)1160054800 (DE-627)1023153912 (DE-576)505473356 aut, Refining long-term prediction of cardiovascular risk in diabetes the VILDIA score Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März, 05 July 2017, 9, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Gesehen am 24.05.2018, 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., Kleber, Marcus E. 1974- VerfasserIn (DE-588)1030135177 (DE-627)734830440 (DE-576)377941379 aut, Grammer, Tanja B. 1972- VerfasserIn (DE-588)1030134952 (DE-627)734830041 (DE-576)377941018 aut, März, Winfried 1958- VerfasserIn (DE-588)1027603599 (DE-627)729463605 (DE-576)373454635 aut, Enthalten in Scientific reports [London] : Macmillan Publishers Limited, part of Springer Nature, 2011 7(2017) Artikel-Nummer 4700, 9 Seiten Online-Ressource (DE-627)663366712 (DE-600)2615211-3 (DE-576)346641179 2045-2322 nnns, volume:7 year:2017 extent:9, http://dx.doi.org/10.1038/s41598-017-04935-8 Verlag Resolving-System kostenfrei Volltext, https://www.nature.com/articles/s41598-017-04935-8 Verlag kostenfrei Volltext, http://dx.doi.org/10.1038/s41598-017-04935-8 LFER, LFER 2018-06-06T00:00:00Z |
spellingShingle | Goliasch, Georg, Kleber, Marcus E., Grammer, Tanja B., März, Winfried, Refining long-term prediction of cardiovascular risk in diabetes: the VILDIA score, 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. |
swb_id_str | 505473690 |
title | Refining long-term prediction of cardiovascular risk in diabetes: the VILDIA score |
title_auth | 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 Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März |
title_fullStr | Refining long-term prediction of cardiovascular risk in diabetes the VILDIA score Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März |
title_full_unstemmed | Refining long-term prediction of cardiovascular risk in diabetes the VILDIA score Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März |
title_in_hierarchy | Refining long-term prediction of cardiovascular risk in diabetes: the VILDIA score / Georg Goliasch, Günther Silbernagel, Marcus E. Kleber, Tanja B. Grammer, Stefan Pilz, Andreas Tomaschitz, Philipp E. Bartko, Gerald Maurer, Wolfgang Koenig, Alexander Niessner and Winfried März, |
title_short | Refining long-term prediction of cardiovascular risk in diabetes |
title_sort | refining long term prediction of cardiovascular risk in diabetes the vildia score |
title_sub | the VILDIA score |
url | http://dx.doi.org/10.1038/s41598-017-04935-8, https://www.nature.com/articles/s41598-017-04935-8 |