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Journal Article

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


Wittchen,  Hans-Ulrich
Max Planck Institute of Psychiatry, Max Planck Society;

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Grammer, T. B., Dressel, A., Gergei, I., Kleber, M. E., Laufs, U., Scharnagl, H., et al. (2019). Cardiovascular risk algorithms in primary care: Results from the DETECT study. SCIENTIFIC REPORTS, 9: 1101. doi:10.1038/s41598-018-37092-7.

Cite as: https://hdl.handle.net/21.11116/0000-0009-61C8-8
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.