title: Assessing glomerular filtration rate in hospitalized patients: a comparison between CKD-EPI and four cystatin C-based equations.
abstract: OBJECTIVE
  A specific method is required for estimating glomerular filtration rate GFR in hospitalized patients. Our objective was to validate the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and four cystatin C (CysC)-based equations in this setting.
  
  
  METHODS
  This was an epidemiologic, cross-sectional study in a random sample of hospitalized patients (n = 3114). We studied the accuracy of the CKD-EPI and four CysC-based equations--based on (1) CysC alone or (2) adjusted by gender; (3) age, gender, and race; and (4) age, gender, race, and creatinine, respectively--compared with GFR measured by iohexol clearance (mGFR). Clinical, biochemical, and nutritional data were also collected.
  
  
  RESULTS
  The CysC equation 3 significantly overestimated the GFR (bias of 7.4 ml/min per 1.73 m(2)). Most of the error in creatinine-based equations was attributable to calculated muscle mass, which depended on patient's nutritional status. In patients without malnutrition or reduced body surface area, the CKD-EPI equation adequately estimated GFR. Equations based on CysC gave more precise mGFR estimates when malnutrition, extensive reduction of body surface area, or loss of muscle mass were present (biases of 1 and 1.3 ml/min per 1.73 m(2) for equations 2 and 4, respectively, versus 5.9 ml/min per 1.73 m(2) for CKD-EPI).
  
  
  CONCLUSIONS
  These results suggest that the use of equations based on CysC and gender, or CysC, age, gender, and race, is more appropriate in hospitalized patients to estimate GFR, since these equations are much less dependent on patient's nutritional status or muscle mass than the CKD-EPI equation.
authors.affiliation: Nephrology Department, Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebron, 119 to 129. 08035 Barcelona, Spain. alsegarr@gmail.com
authors.forename: Alfonso
authors.initials: A
authors.lastname: Segarra
authors.forename: Judith
authors.initials: J
authors.lastname: de la Torre
authors.forename: Natalia
authors.initials: N
authors.lastname: Ramos
authors.forename: Augusto
authors.initials: A
authors.lastname: Quiroz
authors.forename: Maria
authors.initials: M
authors.lastname: Garjau
authors.forename: Irina
authors.initials: I
authors.lastname: Torres
authors.forename: M Antonia
authors.initials: MA
authors.lastname: Azancot
authors.forename: Montserrat
authors.initials: M
authors.lastname: López
authors.forename: Ana
authors.initials: A
authors.lastname: Sobrado
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood; D001830:Body Surface Area; D016009:Chi-Square Distribution; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D003430:Cross-Sectional Studies; D055316:Cystatin C / Q000032:analysis*; D005260:Female; D005919:Glomerular Filtration Rate; D006760:Hospitalization; D006801:Humans; D007472:Iohexol; D007668:Kidney / Q000503:physiopathology*; D007674:Kidney Diseases / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D018482:Muscle, Skeletal / Q000473:pathology; D009752:Nutritional Status; D009929:Organ Size; D011237:Predictive Value of Tests; D044469:Racial Groups; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013030:Spain / Q000453:epidemiology
chemical_list: D015415:Biomarkers; C525030:CST3 protein, human; D055316:Cystatin C; D007472:Iohexol; D003404:Creatinine