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Using Mathematical Algorithms to Modify Glomerular Filtration Rate Estimation Equations

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  • Xiaohua Pei
  • Wanyuan Yang
  • Shengnan Wang
  • Bei Zhu
  • Jianqing Wu
  • Jin Zhu
  • Weihong Zhao

Abstract

Background: The equations provide a rapid and low-cost method of evaluating glomerular filtration rate (GFR). Previous studies indicated that the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease-Epidemiology (CKD-EPI) and MacIsaac equations need further modification for application in Chinese population. Thus, this study was designed to modify the three equations, and compare the diagnostic accuracy of the equations modified before and after. Methodology: With the use of 99 mTc-DTPA renal dynamic imaging as the reference GFR (rGFR), the MDRD, CKD-EPI and MacIsaac equations were modified by two mathematical algorithms: the hill-climbing and the simulated-annealing algorithms. Results: A total of 703 Chinese subjects were recruited, with the average rGFR 77.14±25.93 ml/min. The entire modification process was based on a random sample of 80% of subjects in each GFR level as a training sample set, the rest of 20% of subjects as a validation sample set. After modification, the three equations performed significant improvement in slop, intercept, correlated coefficient, root mean square error (RMSE), total deviation index (TDI), and the proportion of estimated GFR (eGFR) within 10% and 30% deviation of rGFR (P10 and P30). Of the three modified equations, the modified CKD-EPI equation showed the best accuracy. Conclusions: Mathematical algorithms could be a considerable tool to modify the GFR equations. Accuracy of all the three modified equations was significantly improved in which the modified CKD-EPI equation could be the optimal one.

Suggested Citation

  • Xiaohua Pei & Wanyuan Yang & Shengnan Wang & Bei Zhu & Jianqing Wu & Jin Zhu & Weihong Zhao, 2013. "Using Mathematical Algorithms to Modify Glomerular Filtration Rate Estimation Equations," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-6, March.
    • Handle: RePEc:plo:pone00:0057852
    DOI: 10.1371/journal.pone.0057852
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    1. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
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