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Renal function estimation and Cockcroft–Gault formulas for predicting cardiovascular mortality in population-based, cardiovascular risk, heart failure and post-myocardial infarction cohorts: The…

Overview of attention for article published in BMC Medicine, November 2016
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2 tweeters

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Title
Renal function estimation and Cockcroft–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
Published in
BMC Medicine, November 2016
DOI 10.1186/s12916-016-0731-2
Pubmed ID
Authors

João Pedro Ferreira, Nicolas Girerd, Pierpaolo Pellicori, Kevin Duarte, Sophie Girerd, Marc A. Pfeffer, John J. V. McMurray, Bertram Pitt, Kenneth Dickstein, Lotte Jacobs, Jan A. Staessen, Javed Butler, Roberto Latini, Serge Masson, Alexandre Mebazaa, Hans Peter Brunner-La Rocca, Christian Delles, Stephane Heymans, Naveed Sattar, J. Wouter Jukema, John G. Cleland, Faiez Zannad, Patrick Rossignol

Abstract

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. 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. All formulas were strongly and independently associated with CVM. Lower eGFR/CrCl was associated with increasing CVM rates for values below 60 mL/min/m(2). Categorical renal function stages diverged in a more pronounced manner with the CG-BSA formula in all populations (higher χ(2) values), 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. 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 17%
Student > Master 9 9%
Student > Postgraduate 7 7%
Other 6 6%
Student > Bachelor 6 6%
Other 22 23%
Unknown 30 31%
Readers by discipline Count As %
Medicine and Dentistry 40 42%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Nursing and Health Professions 3 3%
Biochemistry, Genetics and Molecular Biology 2 2%
Psychology 2 2%
Other 11 11%
Unknown 34 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2016.
All research outputs
#18,480,433
of 22,899,952 outputs
Outputs from BMC Medicine
#3,219
of 3,443 outputs
Outputs of similar age
#236,926
of 312,766 outputs
Outputs of similar age from BMC Medicine
#67
of 74 outputs
Altmetric has tracked 22,899,952 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,443 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,766 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.