title: Comparison of Risk Prediction With the CKD-EPI and MDRD Equations in Non-ST-Segment Elevation Acute Coronary Syndrome.
abstract: BACKGROUND
  Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations estimate glomerular filtration rate (GFR) more accurately than the Modification of Diet in Renal Disease (MDRD) equation.
  
  
  OBJECTIVE
  New CKD-EPI equations improve risk stratification in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) and provide complementary information to the Global Registry of Acute Coronary Events (GRACE) risk score.
  
  
  METHODS
  We studied 350 subjects (mean age, 68 ± 12 years; 70% male) with NSTE-ACS. Estimated GFR was calculated using the MDRD and new CKD-EPI equations based on serum creatinine (SCr) and/or cystatin C (CysC) concentrations obtained within 48 hours of hospital admission. The primary endpoint was all-cause death during follow-up.
  
  
  RESULTS
  Over the study period (median, 648 days [interquartile range, 236-1042 days]), 31 patients died (0.05% events per person-year). Decedents had poorer renal-function parameters (P < 0.001). Both CysC-based CKD-EPI equations had the highest areas under the receiver operating characteristic curve for the prediction of all-cause mortality. After multivariate adjustment, only CysC-based CKD-EPI equations were independent predictors of all-cause mortality (CKD-EPISCr - CysC , per mL/min/1.73 m(2) : hazard ratio: 0.975, 95% confidence interval: 0.956-0.994, P = 0.009; CKD-EPICysC , per mL/min/1.73 m(2) : hazard ratio: 0.976, 95% confidence interval: 0.959-0.993, P = 0.005). Reclassification analyses showed that only CysC-based CKD-EPI equations improved predictive accuracy of the GRACE risk score.
  
  
  CONCLUSIONS
  In patients with NSTE-ACS, CysC-based CKD-EPI equations improved clinical risk stratification for mortality and added complementary prognostic information to the GRACE risk score.
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain. pedrofb1986@gmail.com.
authors.forename: Pedro J
authors.identifier: http://orcid.org/0000-0002-6394-9841
authors.initials: PJ
authors.lastname: Flores-Blanco
authors.affiliation: Department of Cardiology, Hospital de la Vega Lorenzo Guirao, Cieza, Murcia, Spain.
authors.forename: Ángel
authors.initials: Á
authors.lastname: López-Cuenca
authors.affiliation: Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: James L
authors.initials: JL
authors.lastname: Januzzi
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.|Department of Cardiology, School of Medicine, University of Murcia, Murcia, Spain.
authors.forename: Francisco
authors.initials: F
authors.lastname: Marín
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.
authors.forename: Marianela
authors.initials: M
authors.lastname: Sánchez-Martínez
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.
authors.forename: Miriam
authors.initials: M
authors.lastname: Quintana-Giner
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.
authors.forename: Ana I
authors.initials: AI
authors.lastname: Romero-Aniorte
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.|Department of Cardiology, School of Medicine, University of Murcia, Murcia, Spain.
authors.forename: Mariano
authors.initials: M
authors.lastname: Valdés
authors.affiliation: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.|Department of Cardiology, School of Medicine, University of Murcia, Murcia, Spain.
authors.forename: Sergio
authors.initials: S
authors.lastname: Manzano-Fernández
mesh_terms: D054058:Acute Coronary Syndrome / Q000150:complications* / Q000175:diagnosis / Q000401:mortality / Q000503:physiopathology; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D003404:Creatinine / Q000097:blood; D055316:Cystatin C / Q000097:blood; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D053208:Kaplan-Meier Estimate; D007668:Kidney / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D000072658:Non-ST Elevated Myocardial Infarction / Q000150:complications* / Q000175:diagnosis / Q000401:mortality / Q000503:physiopathology; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D051436:Renal Insufficiency, Chronic / Q000150:complications / Q000175:diagnosis* / Q000401:mortality / Q000503:physiopathology; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
chemical_list: D015415:Biomarkers; C525030:CST3 protein, human; D055316:Cystatin C; D003404:Creatinine