Clinical and Experimental Nephrology

, Volume 23, Issue 2, pp 199–206 | Cite as

GFR estimation in lenalidomide treatment of multiple myeloma patients: a prospective cohort study

  • Andrea Schmidts
  • Julian Grünewald
  • Martina Kleber
  • Evangelos Terpos
  • Gabriele Ihorst
  • Heike Reinhardt
  • Gerd Walz
  • Ralph Wäsch
  • Monika EngelhardtEmail author
  • Stefan ZschiedrichEmail author
Original article



The estimated glomerular filtration rate (eGFR) is clinically used to approximate renal function and adapt drug dosage. Multiple myeloma is a hematological disease; its prognosis is largely influenced by renal function. We evaluated two commonly used GFR estimations, CKD-EPI and MDRD (CKD Epidemiology Collaboration; Modification of Diet in Renal Disease) in myeloma patients undergoing treatment with lenalidomide, a renally excreted immunomodulatory drug.


We prospectively studied 130 myeloma patients receiving lenalidomide treatment at our institution. At baseline and after 3 months, GFR estimations were performed based on the CKD-EPI and MDRD equations. We compared eGFR-dependent CKD staging and lenalidomide dosage assignments.


Initially, most patients were classified as CKD stage I/II, using both equations. Comparison of baseline renal function via CKD-EPI and MDRD induced concordance of CKD staging in 83% of patients, while CKD-EPI improved CKD staging in 16% of patients (p = 0.11). CKD-EPI assigned 3% of patients to higher lenalidomide dosing as opposed to MDRD. Both equations showed improved eGFR after 3 months of lenalidomide treatment.


In our multiple myeloma patient cohort, CKD-EPI and MDRD led to similar CKD staging with minor differences in lenalidomide dosage assignment. Consistent with previous studies, eGFR improved under lenalidomide treatment. To standardize GFR estimation in myeloma patients, we suggest using the CKD-EPI equation.


eGFR Multiple myeloma Lenalidomide Modification of diet in renal disease (MDRD) Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) 



The authors thank Prof. Dr. Hermann Einsele, University of Würzburg, Prof. Dr. Pieter Sonneveld, Erasmus MC Rotterdam, Prof. Dr. Christian Straka, Schön Clinic Starnberger See, Prof. Dr. Keith Stewart, Mayo Clinic Arizona & Rochester, Prof. Dr. Torben Plesner, Center Lillebaelt University of Southern Denmark and Prof. Dr. Justus Duyster, University of Freiburg, for their significant support, fruitful discussion and valuable suggestions. We are also highly obliged to the fruitful discussion with DSMM, GMMG, GIMEMA, EMN and IMWG myeloma experts. This work was supported by a restricted educational grant of Celgene and the Deutsche Krebshilfe (grants 1095969 and 111424 [to ME and RW]).

Author Contributions

AS, JG, MK, GI, ME, SZ performed the analysis; AS, JG, ET, GI, HR, RW, ME, SZ analyzed results; AS, JG, MK, ME, SZ prepared tables and figures; ME, RW, designed the research and AS, JG, ME, SZ wrote the paper. All authors approved the manuscript.

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee at which the studies were conducted (IRB approval number EK 27/1/14) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

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

Conflict of interest

The authors declare no competing financial interest. Our manuscript has not been published in any other scientific journal, nor has it been or will it be submitted or published elsewhere while being under consideration by Clinical and Experimental Nephrology.

Supplementary material

10157_2018_1626_MOESM1_ESM.pdf (845 kb)
Supplementary material 1 (PDF 845 KB)


  1. 1.
    Heher EC, Rennke HG, Laubach JP, Richardson PG. Kidney disease and multiple myeloma. Clin J Am Soc Nephrol. 2013;8:2007–17.CrossRefGoogle Scholar
  2. 2.
    Dimopoulos M, Kastritis E, Rosinol L, Bladé J, Ludwig H. Pathogenesis and treatment of renal failure in multiple myeloma. Leukemia. 2008;22:1485–93.CrossRefGoogle Scholar
  3. 3.
    Bridoux F, Leung N, Hutchison CA, Touchard G, Sethi S, Fermand J-P, et al. Diagnosis of monoclonal gammopathy of renal significance. Kidney Int. 2015;87:698–711.CrossRefGoogle Scholar
  4. 4.
    Leung N, Bridoux F, Hutchison CA, Nasr SH, Cockwell P, Fermand JP, et al. Monoclonal gammopathy of renal significance: when MGUS is no longer undetermined or insignificant. Blood. 2012;120:4292–5.CrossRefGoogle Scholar
  5. 5.
    San Miguel JF, Mateos MV, Ocio E, Garcia-Sanz R. Multiple myeloma: treatment evolution. Hematology. 2012;17:3–7.CrossRefGoogle Scholar
  6. 6.
    Kleber M, Ihorst G, Deschler B, Jakob C, Liebisch P, Koch B, et al. Detection of renal impairment as one specific comorbidity factor in multiple myeloma: multicenter study in 198 consecutive patients. Eur J Haematol. 2009;83:519–27.CrossRefGoogle Scholar
  7. 7.
    Dimopoulos MA, Terpos E, Chanan-Khan A, Leung N, Ludwig H, Jagannath S, et al. Renal impairment in patients with multiple myeloma: a consensus statement on behalf of the International Myeloma Working Group. J Clin Oncol. 2010;28:4976–84.CrossRefGoogle Scholar
  8. 8.
    Kleber M, Ihorst G, Udi J, Koch B, Wäsch R, Engelhardt M. Prognostic risk factor evaluation in patients with relapsed or refractory multiple myeloma receiving lenalidomide treatment: analysis of renal function by eGFR and of additional comorbidities by comorbidity appraisal. Clin Lymphoma Myeloma Leuk. 2012;12:38–48.CrossRefGoogle Scholar
  9. 9.
    Benboubker L, Dimopoulos MA, Dispenzieri A, Catalano J, Belch AR, Cavo M, et al. Lenalidomide and dexamethasone in transplant-ineligible patients with myeloma. N Engl J Med. 2014;371:906–17.CrossRefGoogle Scholar
  10. 10.
    Dimopoulos MA, Cheung MC, Roussel M, Liu T, Gamberi B, Kolb B, et al. Impact of renal impairment on outcomes with lenalidomide and dexamethasone treatment in the FIRST trial, a randomized, open-label phase 3 trial in transplant-ineligible patients with multiple myeloma. Haematologica. 2016;101:363–70.CrossRefGoogle Scholar
  11. 11.
    FDA. U.S. Food and drug administration: revlimid highlights of prescribing information; Reference ID: 4059396. 2011;3–5.Google Scholar
  12. 12.
    EMA. European Medicines Agency. Revlimid product information EMEA/H/C/000717-II/0089/G, Annex 1. 2009.Google Scholar
  13. 13.
    Lawson J, Switchenko JM, McKibbin T, Harvey RD. Impact of isotope dilution mass spectrometry (IDMS) standardization on carboplatin dose and adverse events. Pharmacotherapy. 2016;36:617–22.CrossRefGoogle Scholar
  14. 14.
    Helou R. Should we continue to use the Cockcroft-Gault formula? Nephron Clin. Pract. 2010;116:172–86.CrossRefGoogle Scholar
  15. 15.
    Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. 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.CrossRefGoogle Scholar
  16. 16.
    Levey AS, Coresh J, Greene T, Marsh J, Stevens LA, Kusek JW, et al. Expressing the modification of diet in renal disease study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007;53:766–72.CrossRefGoogle Scholar
  17. 17.
    Levey AS, Stevens L, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.CrossRefGoogle Scholar
  18. 18.
    KDIGO. Chapter 1: Definition and classification of CKD. Kidney Int Suppl. 2013;3:19–62.CrossRefGoogle Scholar
  19. 19.
    Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.CrossRefGoogle Scholar
  20. 20.
    Landis J, Koch G. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.CrossRefGoogle Scholar
  21. 21.
    Engelhardt M, Ihorst G, Landgren O, Pantic M, Reinhardt H, Waldschmidt J, et al. Large registry analysis to accurately define second malignancy rates and risks in a well-characterized cohort of 744 consecutive multiple myeloma patients followed-up for 25 years. Haematologica. 2015;100:1340–9.CrossRefGoogle Scholar
  22. 22.
    Engelhardt M, Dold SM, Ihorst G, Zober A, Möller M, Reinhardt H, et al. Geriatric assessment in multiple myeloma patients: validation of the International Myeloma Working Group (IMWG) score and comparison with other common comorbidity scores. Haematologica. 2016;101:1110–9.CrossRefGoogle Scholar
  23. 23.
    Engelhardt M, Domm A-S, Dold SM, Ihorst G, Reinhardt H, Zober A, et al. A concise revised Myeloma Comorbidity Index as a valid prognostic instrument in a large cohort of 801 multiple myeloma patients. Haematologica. 2017;102:910–21.CrossRefGoogle Scholar
  24. 24.
    Engelhardt M, Terpos E, Kleber M, Gay F, Wäsch R, Morgan G, et al. European Myeloma Network recommendations on the evaluation and treatment of newly diagnosed patients with multiple myeloma. Haematologica. 2014;99:232–42.CrossRefGoogle Scholar
  25. 25.
    Dimopoulos MA, Christoulas D, Roussou M, Kastritis E, Migkou M, Gavriatopoulou M, et al. Lenalidomide and dexamethasone for the treatment of refractory/relapsed multiple myeloma: Dosing of lenalidomide according to renal function and effect on renal impairment. Eur J Haematol. 2010;85:1–5.CrossRefGoogle Scholar
  26. 26.
    Cockcroft D, Gault M. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;1:31–41.CrossRefGoogle Scholar
  27. 27.
    Kleber M, Cybulla M, Bauchmüller K, Ihorst G, Koch B, Engelhardt M. Monitoring of renal function in cancer patients: an ongoing challenge for clinical practice. Ann Oncol. 2007;18:950–8.CrossRefGoogle Scholar
  28. 28.
    Levey A, Stevens L. Estimating GFR Using the CKD epidemiology collaboration (CKD-EPI) creatinine equation: more accurate gfr estimates, lower CKD prevalence estimates, and better risk predictions. Am J Kidney Dis. 2010;55:622–7.CrossRefGoogle Scholar
  29. 29.
    Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis. 2014;63:820–34.CrossRefGoogle Scholar
  30. 30.
    Janowitz T, Williams EH, Marshall A, Ainsworth N, Thomas PB, Sammut SJ, et al. New model for estimating glomerular filtration rate in patients with cancer. J Clin Oncol. 2017;35:2798–805.CrossRefGoogle Scholar
  31. 31.
    Terpos E, Christoulas D, Kastritis E, Katodritou E, Pouli A, Michalis E, et al. The chronic kidney disease epidemiology collaboration cystatin C (CKD-EPI-CysC) equation has an independent prognostic value for overall survival in newly diagnosed patients with symptomatic multiple myeloma; is it time to change from MDRD to CKD-EPI-CysC. Eur J Haematol. 2013;91:347–55.Google Scholar
  32. 32.
    Matsushita K, Selvin E, Bash LD, Astor BC, Coresh J. Risk implications of the new CKD epidemiology collaboration (CKD-EPI) equation compared with the MDRD study equation for estimated GFR: the atherosclerosis risk in communities (ARIC) study. Am J Kidney Dis. 2010;55:648–59.CrossRefGoogle Scholar
  33. 33.
    White SL, Polkinghorne KR, Atkins RC, Chadban SJ. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD epidemiology collaboration (CKD-EPI) and modification of diet in renal disease (MDRD) study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle. Am J Kidney Dis. 2010;55:660–70.CrossRefGoogle Scholar

Copyright information

© Japanese Society of Nephrology 2018

Authors and Affiliations

  • Andrea Schmidts
    • 1
  • Julian Grünewald
    • 2
  • Martina Kleber
    • 3
    • 4
  • Evangelos Terpos
    • 5
  • Gabriele Ihorst
    • 6
  • Heike Reinhardt
    • 1
  • Gerd Walz
    • 2
  • Ralph Wäsch
    • 1
  • Monika Engelhardt
    • 1
    Email author
  • Stefan Zschiedrich
    • 2
    Email author
  1. 1.Department of Hematology, Oncology and Stem Cell Transplantation, Medical Center - University of Freiburg, Faculty of MedicineUniversity of FreiburgFreiburgGermany
  2. 2.Department of Nephrology and Primary Care, Medical Center - University of Freiburg, Faculty of MedicineUniversity of FreiburgFreiburgGermany
  3. 3.Divisions of HematologyUniversity Hospital BaselBaselSwitzerland
  4. 4.Department of Internal MedicineUniversity Hospital BaselBaselSwitzerland
  5. 5.Department of Clinical Therapeutics, School of MedicineUniversity of AthensAthensGreece
  6. 6.Clinical Trials Unit, Medical Center - University of Freiburg, Faculty of MedicineUniversity of FreiburgFreiburgGermany

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