Cardiovascular risk algorithms in primary care: Results from the DETECT study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Tanja B. Grammer, Alexander Dressel, Ingrid Gergei, Marcus E. Kleber, Ulrich Laufs, Hubert Scharnagl, Uwe Nixdorff, Jens Klotsche, Lars Pieper, David Pittrow, Sigmund Silber, Hans-Ulrich Wittchen, Winfried März

ABSTRACT

Guidelines for prevention of cardiovascular diseases use risk scores to guide the intensity of treatment. A comparison of these scores in a German population has not been performed. We have evaluated the correlation, discrimination and calibration of ten commonly used risk equations in primary care in 4044 participants of the DETECT (Diabetes and Cardiovascular Risk Evaluation: Targets and Essential Data for Commitment of Treatment) study. The risk equations correlate well with each other. All risk equations have a similar discriminatory power. Absolute risks differ widely, in part due to the components of clinical endpoints predicted: The risk equations produced median risks between 8.4% and 2.0%. With three out of 10 risk scores calculated and observed risks well coincided. At a risk threshold of 10 percent in 10 years, the ACC/AHA atherosclerotic cardiovascular disease (ASCVD) equation has a sensitivity to identify future CVD events of approximately 80%, with the highest specificity (69%) and positive predictive value (17%) among all the equations. Due to the most precise calibration over a wide range of risks, the large age range covered and the combined endpoint including non-fatal and fatal events, the ASCVD equation provides valid risk prediction for primary prevention in Germany. More... »

PAGES

1101

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-37092-7

DOI

http://dx.doi.org/10.1038/s41598-018-37092-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111774155

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30705337


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37092-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37092-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37092-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-37092-7'


 

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299 https://www.grid.ac/institutes/grid.6363.0 schema:alternateName Charité
300 schema:name Charité Universitätsmedizin Berlin, Institute of Social Medicine, Epidemiology and Health Economics, Berlin, Germany
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302 https://www.grid.ac/institutes/grid.7700.0 schema:alternateName Heidelberg University
303 schema:name University of Heidelberg, Mannheim Medical Faculty, Department of Internal Medicine V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Mannheim, Germany
304 University of Heidelberg, Mannheim Medical Faculty, Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim, Germany
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