title: Agreement between equations estimating glomerular filtration rate in elderly nursing home residents and in hospitalised patients: implications for drug dosing.
abstract: BACKGROUND
  detecting chronic kidney disease (CKD) may have important implications for the management of older and frail people. We aimed at investigating whether clinical setting (nursing home: NH versus hospital: H) affects the agreement between glomerular filtration rate (GFR) values estimated by Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI), Cockcroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) equations.
  
  
  METHODS
  observational study.
  
  
  METHODS
  comparison between NH residents and H patients.
  
  
  METHODS
  we used data from 177 NH residents, and 439 H patients.
  
  
  METHODS
  the agreement between estimating equations and the odds of a discrepancy >25% between formulas in relation to setting (NH versus H) were investigated.
  
  
  RESULTS
  the agreement between MDRD and CKD-EPI formulas was good either in NH (k = 0.82) or H (k = 0.87) patients, while corresponding figures for CG indicate only a fair agreement with CKD-EPI (k = 0.50 for both populations). Setting (NH versus H) was associated with discordance between MDRD and CKD-EPI (OR = 3.97; 95% CI = 1.75-9.01), but not between CG and EPI (OR = 1.25; 95% CI = 0.87-1.81).
  
  
  CONCLUSIONS
  in NH residents, MDRD and CKD-EPI formulas yield highly concordant GFR values, but CG behaves differently in up to one-third of patients. Such findings have important implications in dosing drugs cleared by the kidney. Setting should be taken into consideration in studies for validation of GFR equations.
authors.affiliation: Unit of Geriatric Pharmacoepidemiology, Italian National Research Centres on Aging (INRCA), C.da Muoio Piccolo, Cosenza I-87100, Italy. andrea_corsonello@tin.it
authors.forename: Andrea
authors.initials: A
authors.lastname: Corsonello
authors.forename: Claudio
authors.initials: C
authors.lastname: Pedone
authors.forename: Fabrizia
authors.initials: F
authors.lastname: Lattanzio
authors.forename: Roberto
authors.initials: R
authors.lastname: Semeraro
authors.forename: Francesco
authors.initials: F
authors.lastname: D'Andria
authors.forename: Maurizio
authors.initials: M
authors.lastname: Gigante
authors.forename: Anna
authors.initials: A
authors.lastname: Coppola
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Cadeddu
authors.forename: Irma
authors.initials: I
authors.lastname: Laino
authors.forename: Raffaele Antonelli
authors.initials: RA
authors.lastname: Incalzi
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000097:blood; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D003404:Creatinine / Q000097:blood; D054796:Drug Dosage Calculations; D005260:Female; D016330:Frail Elderly; D005919:Glomerular Filtration Rate; D006707:Homes for the Aged; D006760:Hospitalization; D006801:Humans; D007558:Italy; D007668:Kidney / Q000503:physiopathology*; D007674:Kidney Diseases / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D008954:Models, Biological; D009735:Nursing Homes; D016017:Odds Ratio
chemical_list: D015415:Biomarkers; D003404:Creatinine