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Cardiovascular risk algorithms in primary care: results from the DETECT study
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| Veröffentlicht in: | Scientific reports 9(2019) Artikel-Nummer 1101, 12 Seiten |
|---|---|
| Personen und Körperschaften: | , , , , |
| Titel: | Cardiovascular risk algorithms in primary care: results from the DETECT study/ 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 |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
31 January 2019
|
| Gesamtaufnahme: |
: Scientific reports, 9(2019) Artikel-Nummer 1101, 12 Seiten
, volume:9 |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 520 | |a 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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| contents | 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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| spelling | Grammer, Tanja B. 1972- VerfasserIn (DE-588)1030134952 (DE-627)734830041 (DE-576)377941018 aut, Cardiovascular risk algorithms in primary care results from the DETECT study 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, 31 January 2019, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Gesehen am 04.02.2019, 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., Dressel, Alexander 1964- VerfasserIn (DE-588)140815465 (DE-627)703796755 (DE-576)320908615 aut, Gergei, Ingrid 1986- VerfasserIn (DE-588)1158600623 (DE-627)1020582227 (DE-576)503994405 aut, Kleber, Marcus E. 1974- VerfasserIn (DE-588)1030135177 (DE-627)734830440 (DE-576)377941379 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 9(2019) Artikel-Nummer 1101, 12 Seiten Online-Ressource (DE-627)663366712 (DE-600)2615211-3 (DE-576)346641179 2045-2322 nnns, volume:9 year:2019, http://dx.doi.org/10.1038/s41598-018-37092-7 Verlag Resolving-System kostenfrei Volltext, https://www.nature.com/articles/s41598-018-37092-7 Verlag kostenfrei Volltext, http://dx.doi.org/10.1038/s41598-018-37092-7 LFER, LFER 2019-02-07T00:00:00Z |
| spellingShingle | Grammer, Tanja B., Dressel, Alexander, Gergei, Ingrid, Kleber, Marcus E., März, Winfried, Cardiovascular risk algorithms in primary care: results from the DETECT study, 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. |
| swb_id_str | 517254638 |
| title | Cardiovascular risk algorithms in primary care: results from the DETECT study |
| title_auth | Cardiovascular risk algorithms in primary care results from the DETECT study |
| title_full | Cardiovascular risk algorithms in primary care results from the DETECT study 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 |
| title_fullStr | Cardiovascular risk algorithms in primary care results from the DETECT study 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 |
| title_full_unstemmed | Cardiovascular risk algorithms in primary care results from the DETECT study 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 |
| title_in_hierarchy | Cardiovascular risk algorithms in primary care: results from the DETECT study / 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, |
| title_short | Cardiovascular risk algorithms in primary care |
| title_sort | cardiovascular risk algorithms in primary care results from the detect study |
| title_sub | results from the DETECT study |
| url | http://dx.doi.org/10.1038/s41598-018-37092-7, https://www.nature.com/articles/s41598-018-37092-7 |