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Optimal equation for estimation of glomerular filtration rate in autosomal dominant polycystic kidney disease: influence of tolvaptan

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Abstract

Background

The reliability of various equations for estimating the GFR in ADPKD patients and the influence of tolvaptan on the resulting estimates have not been examined when GFR is calculated on the basis of inulin clearance.

Methods

We obtained baseline and on-tolvaptan measured GFRs (mGFRs), calculated on the basis of inulin clearance, in 114 ADPKD, and these mGFRs were compared with eGFRs calculated according to four basic equations: the MDRD, CKD-EPI, and JSN-CKDI equations and the Cockcroft–Gault formula, as well as the influence of tolvaptan and of inclusion of cystatin C on accuracy of the results. Accuracy of each of the seven total equations was evaluated on the basis of the percentage of eGFR values within mGFR ± 30% (P30).

Results

mGFRs were distributed throughout CKD stages 1–5. Regardless of the CKD stage, P30s of the MDRD, CKD-EPI, and JSN-CKDI equations did not differ significantly between baseline values and on-tolvaptan values. In CKD 1–2 patients, P30 of the CKD-EPI equation was 100.0%, whether or not the patient was on-tolvaptan. In CKD 3–5 patients, P30s of the MDRD, CKD-EPI, and JSN-CKDI equations were similar. For all four equations, regression coefficients and intercepts did not differ significantly between baseline and on-tolvaptan values, but accuracy of the Cockcroft–Gault formula was inferior to that of the other three equations. Incorporation of serum cystatin C reduced accuracy.

Conclusions

The CKD-EPI equation is most reliable, regardless of the severity of CKD. Tolvaptain intake has minimal influence and cystatin C incorporation does not improve accuracy.

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References

  1. Milutinovic J, Fialkow PJ, Agodoa LY, et al. Autosomal dominant polycystic kidney disease: symptoms and clinical findings. Q J Med. 1984;53:511–22.

    CAS  Google Scholar 

  2. Churchill DN, Bear JC, Morgan J, et al. Prognosis of adult onset polycystic kidney disease re-evaluated. Kidney Int. 1984;26:190–3.

    Article  CAS  Google Scholar 

  3. Gabow PA, Johnson AM, Kaehny WD, et al. Factors affecting the progression of renal disease in autosomal-dominant polycystic kidney disease. Kidney Int. 1992;41:1311–9.

    Article  CAS  Google Scholar 

  4. Higashihara E, Nutahara K, Kojima M, et al. Prevalence and renal prognosis of diagnosed autosomal dominant polycystic kidney disease in Japan. Nephron. 1998;80:421–7.

    Article  CAS  Google Scholar 

  5. Kopple JD, Greene T, Chumlea WC, et al. Relationship between nutritional status and the glomerular filtration rate: results from the MDRD study. Kidney Int. 2000;57:1688–703.

    Article  CAS  Google Scholar 

  6. Carlsen JE, Møller ML, Lund JO, et al. Comparison of four commercial Tc-99m(Sn)DTPA preparations used for the measurement of glomerular filtration rate: concise communication. J Nucl Med. 1980;21:126–9.

    CAS  Google Scholar 

  7. Garnett ES, Parsons V, Veall N. Measurement of glomerular filtration-rate in man using a 51Cr-edetic-acid complex. Lancet. 1967;15:818–9.

    Article  Google Scholar 

  8. Yanai M. Measurement of glomerular filtration rate and its estimation equation. Mod Media. 2013;59:155–60.

    Google Scholar 

  9. Horio M, Imai E, Yasuda Y, et al. Japanese equation for estimating GFR: Simple sampling strategy for measuring inulin renal clearance. Clin Exp Nephrol. 2009;13:50–4.

    Article  CAS  Google Scholar 

  10. Orskov B, Borresen ML, Feldt-Rasmussen B, et al. Estimating glomerular filtration rate using the new CKD-EPI equation and other equations in patients with autosomal dominant polycystic kidney disease. Am J Nephrol. 2010;31:53–7.

    Article  Google Scholar 

  11. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40:221–6.

    Article  CAS  Google Scholar 

  12. Horio M, Imai E, Yasuda Y, et al. Performance of serum cystatin C versus serum creatinine as a marker of glomerular filtration rate as measured by inulin renal clearance. Clin Exp Nephrol. 2011;15:868 – 76.

    Article  CAS  Google Scholar 

  13. Stevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009;75:652–60.

    Article  CAS  Google Scholar 

  14. Torres VE, Chapman AB, Devuyst O, et al. TEMPO 3:4 trial investigators: tolvaptan in patients with autosomal dominant polycystic kidney disease. N Engl J Med. 2012;367:2407–18.

    Article  CAS  Google Scholar 

  15. Muto S, Kawano H, Higashihara E, et al. The effect of tolvaptan on autosomal dominant polycystic kidney disease patients: a subgroup analysis of the Japanese patient subset from TEMPO 3:4 trial. Clin Exp Nephrol. 2015;19:867–77.

    Article  CAS  Google Scholar 

  16. Kawada T. The effect of tolvaptan on kidney function in patients with autosomal dominant polycystic kidney disease. Clin Exp Nephrol. 2016;20:147–48.

    Article  Google Scholar 

  17. Levey AS, Stevens LA, Schmid CH, et al. CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration): a new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.

    Article  Google Scholar 

  18. Earley A, Miskulin D, Lamb EJ, et al. Estimating equations for glomerular filtration Rate in the era of creatinine standardization: a systematic review. Ann Intern Med. 2012;156:785–95.

    Article  Google Scholar 

  19. Pei Y, Obaji J, Dupuis A, Paterson AD, et al. Unified criteria for ultrasonographic diagnosis of ADPKD. J Am Soc Nephrol. 2009;20:205–12.

    Article  CAS  Google Scholar 

  20. http://www.kidney.org/professionals/kdoqi/guidelines_ckd/p4_class_gl.htm.2009. Retrieved on Sept 2015.

  21. Higashihara E, Nutahara K, Okegawa T, et al. Kidney volume and function in autosomal dominant polycystic kidney disease. Clin Exp Nephrol. 2014;18:157–65.

    Article  CAS  Google Scholar 

  22. Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med. 1999;130:461–70.

    Article  CAS  Google Scholar 

  23. Levey AS, Greene T, Kusek J, et al. A simplified equation to predict glomerular filtration rate from serum creatinine. J Am Soc Nephrol. 2000;11:155A.

    Google Scholar 

  24. Matsuo S, Imai E, Horio M, et al. Collaborators developing the Japanese equation for estimated GFR. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53:982–92.

    Article  CAS  Google Scholar 

  25. Horio M, Imai E, Yasuda Y, et al. Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: accuracy and use for population estimates. Am J Kidney Dis 2010;56:32–8.

    Article  Google Scholar 

  26. Inker LA, Schmid CH, Tighiouart H, CKD-EPI Investigators, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20–9.

    Article  CAS  Google Scholar 

  27. Horio M, Imai E, Yasuda Y, et al. Collaborators developing the Japanese equation for estimated GFR: GFR estimation using standardized serum cystatin C in Japan. Am J Kidney Dis. 2013;61:197–203.

    Article  CAS  Google Scholar 

  28. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16:31–41.

    Article  CAS  Google Scholar 

  29. Klahr S, Levey AS, Beck GJ, et al. The effects of dietary protein restriction and blood-pressure control on the progression of chronic renal disease. Modification of Diet in Renal Disease Study Group. N Engl J Med. 1994;330:877–84.

    Article  CAS  Google Scholar 

  30. Stevens LA, Coresh J, Greene T, et al. Assessing kidney function-measured and estimated glomerular filtration rate. N Engl J Med. 2006;354:2473–83.

    Article  CAS  Google Scholar 

  31. Shen C, Landsittel D, Irazabal MV, et al. CRISP Investigators: Performance of the CKD-EPI equation to estimate GFR in a longitudinal study of autosomal dominant polycystic kidney disease. Am J Kidney Dis 2017;69:482–4.

    Article  Google Scholar 

  32. McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 1947;12:153–7.

    Article  CAS  Google Scholar 

  33. Michels WM, Grootendorst DC, Verduijn M, et al. Performance of the Cockcroft–Gault, MDRD, and new CKD-EPI formulas in relation to GFR, age, and body size. Clin J am Soc Nephrol. 2010;5:1003–9.

    Article  Google Scholar 

  34. Ma YC, Zuo L, Chen JH, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol. 2006;17:2937–44.

    Article  Google Scholar 

  35. Donadio C. Serum and urinary markers of early impairment of GFR in chronic kidney disease patients: diagnostic accuracy of urinary β-trace protein. Am J Physiol Renal Physiol. 2010;299:407–23.

    Article  Google Scholar 

  36. Spithoven EM, Meijer E, Boertien WE, et al. Tubular secretion of creatinine in autosomal dominant polycystic kidney disease: consequences for cross-sectional and longitudinal performance of kidney function estimating equations. Am J Kid Dis. 2013;62:531–40.

    Article  CAS  Google Scholar 

  37. Chapman AB, Guay-Woodford LM, Grantham JJ, et al. Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease cohort. Renal structure in early autosomal-dominant polycystic kidney disease (ADPKD): the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort. Kidney Int. 2003;64:1035–45.

    Article  Google Scholar 

  38. Cimerman N, Brguljan PM, Krasovec M, et al. Serum cystatin C, a potent inhibitor of cysteine proteinases, is elevated in asthmatic patients. Clin Chim Acta. 2000;300:83–95.

    Article  CAS  Google Scholar 

  39. Bjarnadóttir M, Grubb A, Olafsson I. Promoter-mediated, dexamethasone-induced increase in cystatin C production by HeLa cells. Scand J Clin Lab Invest. 1995;55:617–23.

    Article  Google Scholar 

  40. Walser M. Assessing renal function from creatinine measurements in adults with chronic renal failure. Am J Kidney Dis 1998;32:23–31.

    Article  CAS  Google Scholar 

  41. Kazama JJ, Kutsuwada K, Ataka K, et al. Serum cystatin C reliably detects renal dysfunction in patients with various renal diseases. Nephron. 2002;91:13–20.

    Article  CAS  Google Scholar 

  42. Sans L, Radosevic A, Quintian C, et al. Cystatin C estimated glomerular filtration rate to assess renal function in early stages of autosomal dominant polycystic kidney disease. PLOS One. 2017;12:e0174583.

    Article  Google Scholar 

Download references

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Correspondence to Tsuyoshi Yamaguchi.

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The authors have declared that no conflict of interest exists.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee at which this studies were conducted and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethics Committee approval has been obtained (Approval number 482).

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Informed consent was obtained from all individual participants included in the study.

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Yamaguchi, T., Higashihara, E., Okegawa, T. et al. Optimal equation for estimation of glomerular filtration rate in autosomal dominant polycystic kidney disease: influence of tolvaptan. Clin Exp Nephrol 22, 1213–1223 (2018). https://doi.org/10.1007/s10157-018-1574-2

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  • DOI: https://doi.org/10.1007/s10157-018-1574-2

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