Literature DB >> 15593034

Does cystatin C improve the precision of Cockcroft and Gault's creatinine clearance estimation?

Luca Gabutti1, Nicola Ferrari, Giorgio Mombelli, Claudio Marone.   

Abstract

BACKGROUND: Cystatin C is increasingly used to estimate renal function, but its large intraindividual variability limits its practical value. This study aimed at verifying whether the clinical practice of associating cystatin C determination with serum creatinine (Cr) improved the ability of the Cockcroft and Gault formula to estimate creatinine clearance (CrCl).
METHODS: It was an observational cross-sectional study of 134 in-patients with mildly impaired renal function. Using the Hoek et al formula (glomerular filtration rate (GFR)/1.73m2 = - 4.32 + 80.35/cystatin C mg/L), multivariate linear regressions (LREG) and artificial neural networks (ANN), we integrated cystatin C in the Cockcroft and Gault formula and analyzed the potential superiority of this procedure by comparing its performance with that of the two algorithms taken separately.
RESULTS: The inclusion of cystatin C in the Cockcroft and Gault formula using the data of an LREG (CrCl = 0.371 x (Hoek et al) + 0.589 x Cockcroft and Gault), a simple mean between the two algorithms or ANN ameliorated the CrCl estimation precision allowing an absolute error reduction of approximately 4, 4 and 6%, respectively (relative values 12, 12 and 17%).
CONCLUSIONS: Although the combination of the Hoek et al and Cockcroft and Gault formulae using both linear and non-linear mathematical methods allowed a statistically significant reduction in the estimation error generated by Cockcroft and Gault, considering the small impact on the estimation precision and the large intraindividual variation of both cystatin C and Cr, this procedure probably has no clinical relevance.

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Year:  2004        PMID: 15593034

Source DB:  PubMed          Journal:  J Nephrol        ISSN: 1121-8428            Impact factor:   3.902


  2 in total

1.  Combining GFR estimates from cystatin C and creatinine-what is the optimal mix?

Authors:  Emil den Bakker; Reinoud Gemke; Joanna A E van Wijk; Isabelle Hubeek; Birgit Stoffel-Wagner; Arend Bökenkamp
Journal:  Pediatr Nephrol       Date:  2018-05-17       Impact factor: 3.714

2.  Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression.

Authors:  Navdeep Tangri; David Ansell; David Naimark
Journal:  Nephrol Dial Transplant       Date:  2008-04-25       Impact factor: 5.992

  2 in total

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