Renal function estimation and Cockroft-Gault formulas for predicting cardiovascular mortality in population-based, cardiovascular risk, heart failure and post-myocardial infarction cohorts: The Heart 'OMics' in AGEing (HOMAGE) and the high-risk myocardial infarction database initiatives.
BACKGROUND
Renal impairment is a major risk factor for mortality in various populations. Three formulas are frequently used to assess both glomerular filtration rate (eGFR) or creatinine clearance (CrCl) and mortality prediction: body surface area adjusted-Cockcroft-Gault (CG-BSA), Modification of Diet in Renal Disease Study (MDRD4), and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The CKD-EPI is the most accurate eGFR estimator as compared to a "gold-standard"; however, which of the latter is the best formula to assess prognosis remains to be clarified. This study aimed to compare the prognostic value of these formulas in predicting the risk of cardiovascular mortality (CVM) in population-based, cardiovascular risk, heart failure (HF) and post-myocardial infarction (MI) cohorts.


METHODS
Two previously published cohorts of pooled patient data derived from the partners involved in the HOMAGE-consortium and from four clinical trials - CAPRICORN, EPHESUS, OPTIMAAL and VALIANT - the high risk MI initiative, were used. A total of 54,111 patients were included in the present analysis: 2644 from population-based cohorts; 20,895 from cardiovascular risk cohorts; 1801 from heart failure cohorts; and 28,771 from post-myocardial infarction cohorts. Participants were patients enrolled in the respective cohorts and trials. The primary outcome was CVM.


RESULTS
All formulas were strongly and independently associated with CVM. Lower eGFR/CrCl was associated with increasing CVM rates for values below 60 mL/min/m2. Categorical renal function stages diverged in a more pronounced manner with the CG-BSA formula in all populations (higher χ2values), with lower stages showing stronger associations. The discriminative improvement driven by the CG-BSA formula was superior to that of MDRD4 and CKD-EPI, but remained low overall (increase in C-index ranging from 0.5 to 2 %) while not statistically significant in population-based cohorts. The integrated discrimination improvement and net reclassification improvement were higher (P < 0.05) for the CG-BSA formula compared to MDRD4 and CKD-EPI in CV risk, HF and post-MI cohorts, but not in population-based cohorts. The CKD-EPI formula was superior overall to MDRD4.


CONCLUSIONS
The CG-BSA formula was slightly more accurate in predicting CVM in CV risk, HF, and post-MI cohorts (but not in population-based cohorts). However, the CG-BSA discriminative improvement was globally low compared to MDRD4 and especially CKD-EPI, the latter offering the best compromise between renal function estimation and CVM prediction.
INSERM, Centre d'Investigations Cliniques Plurithématique 1433, INSERM U1116, Université de Lorraine, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France.
João Pedro
JP
Ferreira
INSERM, Centre d'Investigations Cliniques Plurithématique 1433, INSERM U1116, Université de Lorraine, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France.
Nicolas
N
Girerd
Academic Cardiology Unit, University of Hull, Castle Hill Hospital, Kingston upon Hull, UK.
Pierpaolo
P
Pellicori
Université de Lorraine, Institut Elie Cartan de Lorraine, UMR 7502,, Vandoeuvre-lès-Nancy, F-54506, France.|CNRS, Institut Elie Cartan de Lorraine, UMR 7502,, Vandoeuvre-lès-Nancy, F-54506, France.|Team BIGS, INRIA, Villers-lès-Nancy, F-54600, France.
Kevin
K
Duarte
INSERM, Centre d'Investigations Cliniques Plurithématique 1433, INSERM U1116, Université de Lorraine, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France.
Sophie
S
Girerd
Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA.
Marc A
MA
Pfeffer
BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK.
John J V
JJ
McMurray
Department of Medicine, University of Michigan School of Medicine, Ann Arbor, MA, USA.|ASH Comprehensive Hypertension Center, Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, The University of Chicago, Chicago, IL, USA.
Bertram
B
Pitt
Department of Cardiology, University of Bergan, Stavanger University Hospital, Stavanger, Norway.
Kenneth
K
Dickstein
Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Lotte
L
Jacobs
Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Jan A
JA
Staessen
Cardiology Division, Stony Brook University, Stony Brook, NY, USA.
Javed
J
Butler
Laboratory of Cardiovascular Clinical Pharmacology, IRCCS - Istituto di Ricerche Farmacologiche 'Mario Negri', Milan, Italy.
Roberto
R
Latini
Laboratory of Cardiovascular Clinical Pharmacology, IRCCS - Istituto di Ricerche Farmacologiche 'Mario Negri', Milan, Italy.
Serge
S
Masson
Hôpital Lariboisière, Université Paris Diderot, Inserm 942, Paris, France.
Alexandre
A
Mebazaa
Cardiovascular Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, The Netherlands Department of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands.
Hans Peter Brunner-La
HP
Rocca
BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK.
Christian
C
Delles
Department of Cardiology, Maastricht University Medical Center, Postbox 5800, 6202, AZ, Maastricht, The Netherlands.
Stephane
S
Heymans
BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, UK.
Naveed
N
Sattar
Department of Cardiology and Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands.|Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands.
J Wouter
JW
Jukema
National Heart and Lung Institute, Imperial College London (Royal Brompton and Harefield Hospitals) Department of Cardiology, Castle Hill Hospital, University of Hull, Hull, UK.
John G
JG
Cleland
INSERM, Centre d'Investigations Cliniques Plurithématique 1433, INSERM U1116, Université de Lorraine, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France.
Faiez
F
Zannad
INSERM, Centre d'Investigations Cliniques Plurithématique 1433, INSERM U1116, Université de Lorraine, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France. p.rossignol@chru-nancy.fr.|Centre d'Investigations Cliniques-INSERM CHU de Nancy, Institut Lorrain du Cœur et des Vaisseaux Louis Mathieu, 4 Rue du Morvan, 54500, Vandoeuvre lès Nancy, France. p.rossignol@chru-nancy.fr.
Patrick
P
Rossignol
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000401:mortality / Q000503:physiopathology; D015331:Cohort Studies; D016208:Databases, Factual / Q000639:trends; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D006801:Humans; D007668:Kidney / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D011159:Population Surveillance / Q000379:methods; D011237:Predictive Value of Tests; D051436:Renal Insufficiency, Chronic / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D012307:Risk Factors
Cardiovascular mortality prediction; Cardiovascular risk; Glomerular filtration rate formulas; Heart failure and post-myocardial infarction cohorts; Population based; Renal function