Skip to main content

Advertisement

SpringerLink
Log in

How to estimate renal function in patients with liver disease: choosing the most suitable equation

  • Nephrology - Original Paper
  • Published:
International Urology and Nephrology Aims and scope Submit manuscript

Abstract

Background

Hepatitis B virus (HBV) infection is a public health challenge, especially in China. In clinical practice, HBV infection is associated with nephropathy. Impaired renal function is frequently observed in compensated Chronic Hepatitis B (CHB) and cirrhosis (LC). Thus, renal function must be monitored to avoid nephrotoxic effects before and during nucleoside analog treatment. Investigating the predictive markers of early renal dysfunction is essential. New GFR-predicting equations, based on Pcr and/or CystC, have been recently recommended in the general population, but their performance in liver disease patients has been rarely studied. In this study, we will discuss how to detect renal dysfunction in patients with HBV infection.

Methods

A total of 16 LC patients and 23 CHB patients were enrolled in this study, and we collected and compared the clinical data of the two groups. The estimated glomerular filtration rates (eGFRs) were also calculated by several equations. All patients received 99mTc-DTPA dynamic radionuclide imaging examinations to obtain mGFRs as the reference standard. To evaluate the performance of any equation in the CHB and LC groups, paired t test, Pearson’s correlation, Kappa analysis and Bland–Altman plots were utilized. Moreover, all 39 subjects were divided into two groups (according to GFR > 90 mL/min/1.73 m2). We compared the serum and urinary markers of kidney injury between the two groups and selected the indicators of renal injury by univariate analysis.

Results

The mGFR was 72.26 ± 20.69 mL/min/1.73 m2 in the LC group, and 87.49 ± 25.91 mL/min/1.73 m2 in the CHB group. The paired t test results of eGFR and mGFR showed no difference between eGFR (estimated by the CHINAcr-cys equation) and mGFR (p > 0.05) in the compensated LC and CHB groups. The difference between mGFR and eGFR estimated by other methods was obvious (p < 0.05). Comparing the eGFRs (estimated by 5 different equations) with mGFR in the compensated LC and CHB groups, Pearson’s correlation showed that only eGFR (estimated by the CHINAcr-cys equation) had a significant correlation coefficient in CHB (r = 0.678, p = 0.000) and had the highest R2 (R2 = 0.459) among all other measures. The kappa consistency test showed that eGFR from CHINAscr-cys had poor consistency with mGFR in the compensated LC group but moderate consistency in the CHB group. Bland–Altman consistency analysis showed that in the CHB group, the CHINAcr-cys and CKD-EPIcr equations presented narrower acceptable limits than did the aMDRD, c-aMDRD, and CKD-EPIcr-cys equations (62.8, 56.1 vs .85.7, 102.9, 93.6 mL/min per 1.73 m2). In the compensated LC group, the CHINAcr-cys and CKD-EPIcr equations presented narrower acceptable limits than did the aMDRD, c-aMDRD, and CKD-EPIcr-cys equations (83.6, 81.3 vs. 98, 113.5, 106.3 mL/min per 1.73 m2). Serum or urinary markers were compared with renal function (GFR > 90 mL/min/1.73 m2) and showed International normalized ratio (INR) (p = 0.009), creatinine (p = 0.006), urine N-acetyl-β-glucosaminidase (NAG) (p = 0.001) and serum cystatin C (CysC) (p = 0.044).

Conclusion

The CHINAcr-cys equation may be more suitable for the estimation of GFR in Chinese patients with CHB or compensated cirrhosis. INR, creatinine, NAG, and CysC are proper biomarkers for screening renal dysfunction in Chinese patients with CHB or compensated LC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Deterding K, Lampe N, Reijnders J, Manns MP, Janssen H, Zoulim F, Buti M, Santantonio T, Wedemeyer H (2011) 366 Prevalence and severity of kidney dysfunction in patients with chronic hepatitis B in Europe: data from the European Virgil Cohort. J Hepatol 54(Suppl 1):S147–S147

    Article  Google Scholar 

  2. Ha NB, Ha NBGarcia RT (2009) Renal dysfunction in chronic hepatitis B patients treated with adefovir dipivoxil. Hepatology 50(3):727–734

    Article  CAS  PubMed  Google Scholar 

  3. Liu Y, Fan R, Chen J, Zheng Z, Liao B, Liang X, Yin J, Zhou Q, Sun J (2014) Assessment of renal function and risk factors for renal impairment in patients with hepatitis B virus-related liver cirrhosis. J South Med Univ 34(4):472–476

    Google Scholar 

  4. Mauss S, Berger F, Filmann N, Hueppe D, Henke J, Hegener P, Athmann C, Schmutz G, Herrmann E (2011) Effect of HBV polymerase inhibitors on renal function in patients with chronic hepatitis B. J Hepatol 55(6):1235–1240

    Article  CAS  PubMed  Google Scholar 

  5. Hai LI, Han T, Yan LI, Chen LY, Wang YL, Xiang XH, Yang SG, Zhu LM (2015) The early renal injury and its related factors in patients with chronic HBV infection. Chin J Blood Purif 0(11):644–648

    Google Scholar 

  6. Gane EJ, Deray G, Liaw YF, Lim SG, Lai CL, Rasenack J, Wang Y, Papatheodoridis G, Di BA, Buti M (2014) Telbivudine improves renal function in patients with chronic hepatitis B. Gastroenterology 146(1):138–146

    Article  CAS  PubMed  Google Scholar 

  7. Hou J, Wang G, Wang F, Cheng J, Ren H, Zhuang H, Sun J, Li L, Li J, Meng Q (2017) Guideline of prevention and treatment for chronic hepatitis B (2015 update). J Clin Transl Hepatol 5(4):297–318

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ofliver EAF (2017) EASL 2017 Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol 67(2):370–398

    Article  Google Scholar 

  9. Levey AS, Green T, Kuseket J, Beck G (2000) A simplified equitation to predict glomerular filtration rate from serum creatinine. J Am Soc Nephrol 11:A0828

    Google Scholar 

  10. Eknoyan G, Levin N (2001) NKF-K/DOQI clinical practice guidelines: update 2000. Foreword. Am J Kidney Dis 37(1):5–6

    Article  Google Scholar 

  11. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 130(6):461–470

    Article  CAS  Google Scholar 

  12. Zuo L, Ma YC, Zhou YH, Wang M, Xu GB, Wang HY (2005) Application of GFR-estimating equations in Chinese patients with chronic kidney disease. Am J Kidney Dis 45(3):463–472

    Article  PubMed  Google Scholar 

  13. Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, Xu JS, Huang SM, Wang LN, Huang W (2006) Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol Jasn 17(10):2937–2944

    Article  PubMed  Google Scholar 

  14. Levey AS, Stevens LA, Schmid CH, Zhang YL, Rd CA, Feldman HI, Kusek JW, Eggers P, Van LF, Greene T (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612

    Article  PubMed  PubMed Central  Google Scholar 

  15. Eriksen BO, Mathisen UD, Melsom T, Ingebretsen OC, Jenssen TG, Njølstad I, Solbu MD, Toft I (2010) Cystatin C is not a better estimator of GFR than plasma creatinine in the general population. Kidney Int 78(12):1305–1311

    Article  CAS  PubMed  Google Scholar 

  16. Stockman JA (2011) A new equation to estimate glomerular filtration rate. Yearb Pediatr 2011:193–194

    Article  Google Scholar 

  17. Tent H, Rook M, Stevens LA, Son WJV, Pelt LJV, Hofker HS, Ploeg RJ, Heide JJHVD, Navis G (2010) Renal function equations before and after living kidney donation: a within-individual comparison of performance at different levels of renal function. Clin J Am Soc Nephrol 5(11):1960–1968

    Article  PubMed  PubMed Central  Google Scholar 

  18. Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K (2012) Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med 156(11):785–795

    Article  PubMed  Google Scholar 

  19. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van LF, Zhang YL (2012) Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367(1):20–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Feng JF, Qiu L, Zhang L, Li XM, Yang YW, Zeng P, Guo XZ, Qin Y, Liu HC, Han XM (2013) Multicenter study of creatinine- and/or cystatin C-based equations for estimation of glomerular filtration rates in Chinese patients with chronic kidney disease. PLos ONE 8(3):e57240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Guo X, Qin Y, Zheng K, Gong M, Wu J, Shou W, Cheng X, Xia L, Xu E, Li X (2014) Improved glomerular filtration rate estimation using New equations combined with standardized cystatin C and creatinine in Chinese adult chronic kidney disease patients. Clin Biochem 47(13–14):1220–1226

    Article  CAS  PubMed  Google Scholar 

  22. Wu X, Cai S, Li Z, Zheng C, Peng J (2016) Potential effects of telbivudine and entecavir on renal function: a systematic review and meta-analysis. Virol J 13(1):64

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mindikoglu AL, Dowling TC, Weir MR, Seliger SL, Christenson RH, Magder LS (2014) Performance of chronic kidney disease epidemiology collaboration creatinine-cystatin C equation for estimating kidney function in cirrhosis. Hepatology 59(4):1532–1542

    Article  CAS  PubMed  Google Scholar 

  24. Pirlich M, Schütz T, Spachos T, Ertl S, Weiss ML, Lochs H, Plauth M (2000) Bioelectrical impedance analysis is a useful bedside technique to assess malnutrition in cirrhotic patients with and without ascites. Hepatology 32(6):1208–1215

    Article  CAS  PubMed  Google Scholar 

  25. Gonwa TA, Jennings L, Mai ML, Stark PC, Levey AS, Klintmalm GB (2004) Estimation of glomerular filtration rates before and after orthotopic liver transplantation: evaluation of current equations. Liver Transplant 10(2):301–309

    Article  Google Scholar 

  26. Badiou S, Dupuy AM, Descomps B, Cristolead JP (2003) Comparison between the enzymatic vitros assay for creatinine determination and three other methods adapted on the Olympus analyzer. J Clin Lab Anal 17(6):235–240

    Article  CAS  PubMed  Google Scholar 

  27. Donadio C (2010) Serum and urinary markers of early impairment of GFR in chronic kidney disease patients: diagnostic accuracy of urinary β-trace protein. Am J Physiol Ren Physiol 299(6):1407–1423

    Article  CAS  Google Scholar 

  28. Uslu S, Efe B, Alataş O, Kebapçi N, Colak O, Demirüstü C, Yörük A (2005) Serum cystatin C and urinary enzymes as screening markers of renal dysfunction in diabetic patients. J Nephrol 18(5):559–567

    CAS  PubMed  Google Scholar 

  29. Adachi M, Tanaka A, Aiso M, Takamori Y, Takikawa H (2016) The benefit of cystatin c in evaluation of renal function and prediction of survival in patients with cirrhosis. Hepatol Res 45(13):1299–1306

    Article  CAS  Google Scholar 

  30. Omar M, Abdelrazek W, Aboraia G, Assem M, Elazab G (2015) Evaluation of serum cystatin C as a marker of early renal impairment in patients with liver cirrhosis. Int J Hepatol 2015:Article ID 309042

Download references

Acknowledgements

Supported by the National Science and Technology Major Project (2016ZX10002011).

Author information

Authors and Affiliations

  1. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, People’s Republic of China

    Song Ren

  2. Department of Hepatopancreatobiliary and Splenic Medicine, Affiliated Hospital, Logistics University of People’s Armed Police Force, 220 Chenglin Road, Hedong District, Tianjin, 300162, People’s Republic of China

    Yue Chang, Qing Zhang, Haiyan Niu, Linyan Chen, Chengjiao Lv, Zhengyun Zhang, Xiaohui Xiang, Limin Zhu & Hai Li

  3. Department of Ultrasonography, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 Yanta West Road, Xi’an, Shaanxi, People’s Republic of China

    Xiang Wang

Authors
  1. Song Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haiyan Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linyan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chengjiao Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhengyun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaohui Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Limin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SR, LZ and HL provided the ideas, and SR, HL and YC drafted the report. YC, QZ, XW, HN, LC, CL, ZZ and XX delivered experimental data. All authors have read and contributed to editing this manuscript and agree with the content and presentation of the paper.

Corresponding author

Correspondence to Hai Li.

Ethics declarations

Conflict of interest

No conflicts of interest, financial or otherwise, are declared by the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, S., Chang, Y., Zhang, Q. et al. How to estimate renal function in patients with liver disease: choosing the most suitable equation. Int Urol Nephrol 51, 677–690 (2019). https://doi.org/10.1007/s11255-019-02110-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11255-019-02110-8

Keywords

Search

Navigation