title: Comparison of modification of diet in renal disease and chronic kidney disease epidemiology collaboration formulas in predicting long-term outcomes in patients undergoing stent implantation due to stable coronary artery disease.
abstract: BACKGROUND
  The aim was to assess the predictive value of estimated glomerular filtration rate (eGFR) using two formulas: modification of diet in renal disease (MDRD) and chronic kidney disease epidemiology collaboration (CKD-EPI), in a population with stable coronary artery disease (SCAD) undergoing percutaneous coronary revascularization (PCI).
  
  
  METHODS
  The analyzed cohort included 3,141 consecutive patients with SCAD who underwent PCI, between January 2006 and December 2011. Follow-up data were available for 3,123 (99.4 %) patients.
  
  
  RESULTS
  The median follow-up was 1,127 days (interquartile range 566-1,634 days). During the observation period, 330 deaths were reported. In patients with serum creatinine (S-Cr) within normal range, eGFR by CKD-EPI equation predicted long-term outcome more accurately, than eGFR by MDRD formula-continuous Net Reclassification Improvement: 0.296 (95 % CI, 0.08-0.5 p = 0.03). In patients with elevated S-CR, eGFR calculated by both formulae had similar efficacy in assessing death risk. After adjustment for differences in clinical characteristics, both formulae were associated with mortality, but only in patients with elevated S-Cr: eGFR by MDRD (per 10 ml/min/1.73 m(2)) HR: 0.74 [95 % CI, 0.61-0.89, p = 0.002], eGFR by CKD-EPI (per 10 ml/min/1.73 m(2)) HR: 0.75 (95 % CI, 0.63-0.89, p = 0.001). After adjustment for covariates, eGFR by CKD-EPI equation did not offer more appropriate categorization of individuals with respect to long-term mortality.
  
  
  CONCLUSIONS
  Our results indicate that in multivariable analysis eGFR calculated by MDRD and CKD-EPI equations has similar predictive value. In a population of patients with SCAD and S-Cr within normal range, eGFR calculated by CKD-EPI equation outperforms eGFR calculated by MDRD equation in assessing death risk.
authors.affiliation: IIIrd Department of Cardiology, Medical University of Silesia in Katowice, Medical Faculty in Zabrze, Silesian Centre for Heart Diseases, Ul. Marii Skłodowskiej Curie 9, 41-800, Zabrze, Poland, tadeusz.osadnik@sccs.pl.
authors.forename: Tadeusz
authors.initials: T
authors.lastname: Osadnik
authors.forename: Jarosław
authors.initials: J
authors.lastname: Wasilewski
authors.forename: Andrzej
authors.initials: A
authors.lastname: Lekston
authors.forename: Joanna
authors.initials: J
authors.lastname: Strzelczyk
authors.forename: Anna
authors.initials: A
authors.lastname: Kurek
authors.forename: Aleksander Rafał
authors.initials: AR
authors.lastname: Gutowski
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Dyrbuś
authors.forename: Kamil
authors.initials: K
authors.lastname: Bujak
authors.forename: Rafał
authors.initials: R
authors.lastname: Reguła
authors.forename: Piotr
authors.initials: P
authors.lastname: Rozentryt
authors.forename: Bożena
authors.initials: B
authors.lastname: Szyguła-Jurkiewicz
authors.forename: Lech
authors.initials: L
authors.lastname: Poloński
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D003324:Coronary Artery Disease / Q000401:mortality / Q000601:surgery*; D003404:Creatinine / Q000097:blood; D005260:Female; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D062645:Percutaneous Coronary Intervention / Q000379:methods*; D011237:Predictive Value of Tests; D012307:Risk Factors; D015607:Stents
chemical_list: D003404:Creatinine