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Abstract
The cardiac lipid panel (CLP) is a novel panel of metabolomic biomarkers that has previously shown to improve the diagnostic and prognostic value for CHF patients. Several prognostic scores have been developed for cardiovascular disease risk, but their use is limited to specific populations and precision is still inadequate. We compared a risk score using the CLP plus NT-proBNP to four commonly used risk scores: The Seattle Heart Failure Model (SHFM), Framingham risk score (FRS), Barcelona bio-HF (BCN Bio-HF) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. We included 280 elderly CHF patients from the Cardiac Insufficiency Bisoprolol Study in Elderly trial. Cox Regression and hierarchical cluster analysis was performed. Integrated area under the curves (IAUC) was used as criterium for comparison. The mean (SD) follow-up period was 81 (33) months, and 95 (34%) subjects met the primary endpoint. The IAUC for FRS was 0.53, SHFM 0.61, BCN Bio-HF 0.72, MAGGIC 0.68, and CLP 0.78. Subjects were partitioned into three risk clusters: low, moderate, high with the CLP score showing the best ability to group patients into their respective risk cluster. A risk score composed of a novel panel of metabolite biomarkers plus NT-proBNP outperformed other common prognostic scores in predicting 10-year cardiovascular death in elderly ambulatory CHF patients. This approach could improve the clinical risk assessment of CHF patients.
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1 Charité – Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662); Baptist Health South Florida, Coral Gables, USA (GRID:grid.418212.c) (ISNI:0000 0004 0465 0852)
2 Baptist Health South Florida, Coral Gables, USA (GRID:grid.418212.c) (ISNI:0000 0004 0465 0852)
3 Charité – Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662)
4 Government Medical College, Department of Biochemistry, Kozhikode, India (GRID:grid.253527.4) (ISNI:0000 0001 0705 6304)
5 Baptist Health South Florida, Coral Gables, USA (GRID:grid.418212.c) (ISNI:0000 0004 0465 0852); Florida International University, Department of Biostatistics, Miami, USA (GRID:grid.65456.34) (ISNI:0000 0001 2110 1845); Emory University School of Medicine, Division of Cardiology, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502)
6 Heart Center Leipzig at the University of Leipzig, Department of Cardiology and Internal Medicine, Leipzig, Germany (GRID:grid.9647.c) (ISNI:0000 0004 7669 9786)
7 University of Belgrade, Institute for Cardiovascular Diseases Dedinje, Department of Cardioloy, Faculty of Medicine, Belgrade, Serbia (GRID:grid.7149.b) (ISNI:0000 0001 2166 9385)
8 University of Niš, Apostolovic Clinic for Cardiovascular Diseases, Clinical Centre Nis, Niš, Serbia (GRID:grid.11374.30) (ISNI:0000 0001 0942 1176)
9 Charité – Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662); DZHK (German Centre for Cardiovascular Research), Berlin, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237); Berlin Institute of Health (BIH), Berlin, Germany (GRID:grid.484013.a)
10 Charité – Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662); DZHK (German Centre for Cardiovascular Research), Berlin, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237); Berlin Institute of Health (BIH), Berlin, Germany (GRID:grid.484013.a); German Heart Centre Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.484013.a)
11 Charité – Universitätsmedizin Berlin, Department of Internal Medicine and Cardiology, Berlin, Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662); DZHK (German Centre for Cardiovascular Research), Berlin, Germany (GRID:grid.452396.f) (ISNI:0000 0004 5937 5237)