title: Renal dysfunction in patients with heart failure with preserved versus reduced ejection fraction: impact of the new Chronic Kidney Disease-Epidemiology Collaboration Group formula.
abstract: BACKGROUND
  Prior studies in heart failure (HF) have used the Modification of Diet in Renal Disease (MDRD) equation to calculate estimated glomerular filtration rate (eGFR). The Chronic Kidney Disease-Epidemiology Collaboration Group (CKD-EPI) equation provides a more-accurate eGFR than the MDRD when compared against the radionuclide gold standard. The prevalence and prognostic import of renal dysfunction in HF if the CKD-EPI equation is used rather than the MDRD is uncertain.
  
  
  RESULTS
  We used individual patient data from 25 prospective studies to stratify patients with HF by eGFR using the CKD-EPI and the MDRD equations and examined survival across eGFR strata. In 20 754 patients (15 962 with HF with reduced ejection fraction [HF-REF] and 4792 with HF with preserved ejection fraction [HF-PEF]; mean age, 68 years; deaths per 1000 patient-years, 151; 95% CI, 146-155), 10 589 (51%) and 11 422 (55%) had an eGFR <60 mL/min using the MDRD and CKD-EPI equations, respectively. Use of the CKD-EPI equation resulted in 3760 (18%) patients being reclassified into different eGFR risk strata; 3089 (82%) were placed in a lower eGFR category and exhibited higher all-cause mortality rates (net reclassification improvement with CKD-EPI, 3.7%; 95% CI, 1.5%-5.9%). Reduced eGFR was a stronger predictor of all-cause mortality in HF-REF than in HF-PEF.
  
  
  CONCLUSIONS
  Use of the CKD-EPI rather than the MDRD equation to calculate eGFR leads to higher estimates of renal dysfunction in HF and a more-accurate categorization of mortality risk. Renal function is more closely related to outcomes in HF-REF than in HF-PEF.
authors.affiliation: Division of General Internal Medicine, University of Alberta, Edmonton, AB, Canada. Finlay.McAlister@ualberta.ca
authors.forename: Finlay A
authors.initials: FA
authors.lastname: McAlister
authors.forename: Justin
authors.initials: J
authors.lastname: Ezekowitz
authors.forename: Luigi
authors.initials: L
authors.lastname: Tarantini
authors.forename: Iain
authors.initials: I
authors.lastname: Squire
authors.forename: Michel
authors.initials: M
authors.lastname: Komajda
authors.forename: Antoni
authors.initials: A
authors.lastname: Bayes-Genis
authors.forename: Israel
authors.initials: I
authors.lastname: Gotsman
authors.forename: Gillian
authors.initials: G
authors.lastname: Whalley
authors.forename: Nikki
authors.initials: N
authors.lastname: Earle
authors.forename: Katrina K
authors.initials: KK
authors.lastname: Poppe
authors.forename: Robert N
authors.initials: RN
authors.lastname: Doughty
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology*; D006333:Heart Failure / Q000150:complications* / Q000401:mortality / Q000503:physiopathology*; D006801:Humans; D007668:Kidney / Q000503:physiopathology; D007674:Kidney Diseases / Q000453:epidemiology* / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015995:Prevalence; D011379:Prognosis; D011446:Prospective Studies; D012307:Risk Factors; D013318:Stroke Volume / Q000502:physiology*; D015996:Survival Rate