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Sci Rep. 2019 Jan 31;9(1):1101. doi: 10.1038/s41598-018-37092-7.

Cardiovascular risk algorithms in primary care: Results from the DETECT study.

Author information

1
University of Heidelberg, Mannheim Medical Faculty, Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim, Germany. tanja.grammer@medma.uni-heidelberg.de.
2
University of Heidelberg, Mannheim Medical Faculty, Department of Internal Medicine V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Mannheim, Germany. tanja.grammer@medma.uni-heidelberg.de.
3
University of Heidelberg, Mannheim Medical Faculty, Department of Internal Medicine V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Mannheim, Germany.
4
Clinic and Polyclinic of Cardiology, University Clinic Leipzig, Leipzig, Germany.
5
Medical University of Graz, Clinical Institute of Medical and Chemical Laboratory Diagnostics, Graz, Austria.
6
European Prevention Center, EPC GmbH, Düsseldorf, Germany. nixdorff@epccheckup.de.
7
German Research Center of Rheumatology Berlin, Leibnitz Institute, Berlin, Germany.
8
Charité Universitätsmedizin Berlin, Institute of Social Medicine, Epidemiology and Health Economics, Berlin, Germany.
9
Technical University Dresden, Medical Faculty, Institute of Clinical Pharmacology, Dresden, Germany.
10
Cardiology Outpatient Clinic Tal, Munich, Germany.
11
Technical University Dresden, Institute of Clinical Psychology and Psychotherapy, Dresden, Germany.
12
Max-Planck- Institute of Psychiatry, Munich, Germany.
13
Synlab Services GmbH, Synlab Academy, Mannheim, Augsburg, Germany.

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.

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