title: Major bleeding risk prediction using Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations in acute coronary syndrome.
abstract: BACKGROUND
  Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations estimate glomerular filtration rate more accurately than the Modification of Diet in Renal Disease (MDRD) Study equation. Our aim was to evaluate whether CKD-EPI equations based on serum creatinine and/or cystatin C (CysC) predict risk for major bleeding (MB) more accurately than the MDRD Study equation in patients with non-ST-segment elevation acute coronary syndromes (ACS).
  
  
  METHODS
  Three hundred and fifty consecutive subjects with non-ST-segment elevation ACS (68 ± 12 years, 70% male) were studied. Glomerular filtration rate was estimated using the CKD-EPI and MDRD Study equations. The primary endpoint was the occurrence of MB during the follow-up, which was defined according to the Bleeding Academic Research Consortium Definition criteria as bleeding types 3-5.
  
  
  RESULTS
  During the median follow-up of 589 days (interquartile range, 390-986), 27 patients had MB (0.04% events per person year). Patients with MB had worse kidney function parameters, regardless of the estimating equation used (P < 0.001). After multivariate Cox regression adjustment, both CysC-based CKD-EPI equations were independent predictors of MB (CKD-EPI(creatinine-cystatin) C per mL/min/1.73 m(2), HR = 0.973 (95%CI 0.955-0.991; P = 0.003) and CKD-EPI(cystatin) C per mL/min/1.73 m(2), HR = 0.976 (95%CI 0.976-0.992; P = 0.003), while the CKD-EPI(creatinine) and MDRD equations did not achieve statistical significance. Both CKD-EPI(creatine-cystatin) C and CKD-EPI(cystatin) C were associated with a significant improvement in MB risk reclassification.
  
  
  CONCLUSIONS
  In this cohort of non-ST-segment elevation ACS patients with relatively preserved renal function, both CysC-based CKD-EPI equations improved ability to predict risk for MB and were superior to other equations for this application.
authors: Department of Cardiology, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain.
authors: Pedro J
authors: PJ
authors: Flores-Blanco
authors: Ángel
authors: Á
authors: López-Cuenca
authors: James L
authors: JL
authors: Januzzi
authors: Francisco
authors: F
authors: Marín
authors: Marianela
authors: M
authors: Sánchez-Martínez
authors: Miriam
authors: M
authors: Quintana-Giner
authors: Ana I
authors: AI
authors: Romero-Aniorte
authors: Mariano
authors: M
authors: Valdés
authors: Sergio
authors: S
authors: Manzano-Fernández
mesh_terms: D054058:Acute Coronary Syndrome / Q000453:epidemiology / Q000378:metabolism*; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D003404:Creatinine / Q000378:metabolism*; D055316:Cystatin C / Q000378:metabolism*; D003661:Decision Support Techniques; D005260:Female; D005919:Glomerular Filtration Rate; D006470:Hemorrhage / Q000453:epidemiology / Q000378:metabolism*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D013030:Spain
chemical_list: D055316:Cystatin C; D003404:Creatinine
keywords: Acute coronary syndrome; haemorrhage; kidney; prognosis