• ISSN 16748301
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Volume 33 Issue 1
Jan.  2019
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Citation:

Accuracy of glomerular filtration rate estimation equations in patients with hematopathy

  • Renal dysfunction is a common side-effect of chemotherapeutic agents in patients with hematopathy. Although broadly used, glomerular filtration rate (GFR) estimation equations were not fully validated in this specific population. Thus, this study was designed to further assess the accuracy of various GFR equations, including the newly 2012 CKD-EPI equations. Referring to 99mTc-DTPA clearance method, three Scr-based (MDRD, Peking, and CKD-EPIScr), three Scys C-based (Steven 1, Steven 2, and CKD-EPIScys C), and three Scr-Scys C combination based (Ma, Steven 3, and CKD-EPIScr-Scys C) equations were included. Bias, P30, and misclassification rate were applied to compare the applicability of the selected equations. A total of 180 Chinese hematological patients were enrolled. Mean bias, absolute mean bias, P30, misclassification rate and Bland-Altman plots of the CKD-EPIScr-Scys C equation were 7.90 mL/minute/1.73 m2, 17.77 mL/minute/1.73 m2, 73.3%, 38% and 79.7 mL/minute/1.73 m2, respectively. CKD-EPIScr-Scys C predicted the most precise eGFR both in lymphoma and leukemia subgroups. Additionally, CKD-EPIScys C equation in the rGFR $\geqq $ 90 mL/minute/1.73 m2 subgroup and Steven 2 equation in the rGFR < 90 mL/ minute/1.73 m2 subgroup provided more accurate estimates in each subgroup. The CKD-EPIScr-Scys C equation could be recommended to monitor kidney function in hematopathy patients. The accuracy of GFR equations may be closely related with GFR level and kidney function markers, but not the primary cause of hematopathy.
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  • [1] Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure[J]. JAMA, 2011, 305(15): 1553–1559. doi: 10.1001/jama.2011.451
    [2] Zhang QL, Rothenbacher D. Prevalence of chronic kidney disease in population-based studies: systematic review[J]. BMC Public Health, 2008, 8: 117. doi: 10.1186/1471-2458-8-117
    [3] Zhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey[J]. Lancet, 2012, 379 (9818): 815–822. doi: 10.1016/S0140-6736(12)60033-6
    [4] Kolvek G, Podracka L, Rosenberger J, et al. Solitary functioning kidney in children–a follow-up study[J]. Kidney Blood Press Res, 2014, 39(4): 272–278. doi: 10.1159/000355804
    [5] Johansson M, Moonen M. Prediction of post-operative glomerular filtration rate after nephrectomy for renal malignancy[J]. Clin Physiol, 2001, 21(6): 688–692. doi: 10.1046/j.1365-2281.2001.00370.x
    [6] Tanaka N, Fujimoto K, Tani M, et al. Prediction of postoperative renal function by preoperative serum creatinine level and three-dimensional diagnostic image reconstruction in patients with renal cell carcinoma[J]. Urology, 2004, 64(5): 904–908. doi: 10.1016/j.urology.2004.07.006
    [7] Knight EL, Verhave JC, Spiegelman D, et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement[J]. Kidney Int, 2004, 65(4): 1416–1421. doi: 10.1111/j.1523-1755.2004.00517.x
    [8] Laterza OF, Price CP, Scott MG. Cystatin C: an improved estimator of glomerular filtration rate[J]? Clin Chem, 2002, 48 (5): 699–707.
    [9] Heikkinen JO, Kuikka JT, Ahonen AK, et al. Quality of dynamic radionuclide renal imaging: multicentre evaluation using a functional renal phantom[J]. Nucl Med Commun, 2001, 22(9): 987–995. doi: 10.1097/00006231-200109000-00008
    [10] Gates GF. Computation of glomerular filtration rate with Tc-99m DTPA: an in-house computer program[J]. J Nucl Med, 1984, 25(5): 613–618.
    [11] Levey AS, Bosch JP, Lewis JB, et al., and the Modification of Diet in Renal Disease Study Group. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation[J]. Ann Intern Med, 1999, 130(6): 461–470.
    [12] Brenner BM, Mackenzie HS. Nephron mass as a risk factor for progression of renal disease[J]. Kidney Int Suppl, 1997, 63: S124–S127.
    [13] Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease[J]. Kidney Int, Suppl 3: 1–150.
    [14] Hoy WE, Hughson MD, Singh GR, et al. Reduced nephron number and glomerulomegaly in Australian Aborigines: a group at high risk for renal disease and hypertension[J]. Kidney Int, 2006, 70(1): 104–110. doi: 10.1038/sj.ki.5000397
    [15] Keller G, Zimmer G, Mall G, et al. Nephron number in patients with primary hypertension[J]. N Engl J Med, 2003, 348(2): 101–108. doi: 10.1056/NEJMoa020549
    [16] Hughson MD, Douglas-Denton R, Bertram JF, et al. Hypertension, glomerular number, and birth weight in African Americans and white subjects in the southeastern United States[J]. Kidney Int, 2006, 69(4): 671–678. doi: 10.1038/sj.ki.5000041
    [17] Siomou E, Giapros V, Papadopoulou F, et al. Growth and function in childhood of a normal solitary kidney from birth or from early infancy[J]. Pediatr Nephrol, 2014, 29(2): 249–256. doi: 10.1007/s00467-013-2623-4
    [18] Bertram JF, Douglas-Denton RN, Diouf B, et al. Human nephron number: implications for health and disease[J]. Pediatr Nephrol, 2011, 26(9): 1529–1533. doi: 10.1007/s00467-011-1843-8
    [19] Heikkinen JO, Kuikka JT, Ahonen AK, et al. Quality of dynamic radionuclide renal imaging: multicentre evaluation using a functional renal phantom[J]. Nucl Med Commun, 2001, 22(9): 987–995. doi: 10.1097/00006231-200109000-00008
    [20] Gates GF. Computation of glomerular filtration rate with Tc-99m DTPA: an in-house computer program[J]. J Nucl Med, 1984, 25(5): 613–618.
    [21] Levey AS, Bosch JP, Lewis JB, et al., and the Modification of Diet in Renal Disease Study Group. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation[J]. Ann Intern Med, 1999, 130(6): 461–470.
    [22] Ma YC, Zuo L, Chen JH, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease[J]. J Am Soc Nephrol, 2006, 17(10): 2937–2944. doi: 10.1681/ASN.2006040368
    [23] Stevens LA, Coresh J, Schmid CH, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3, 418 individuals with CKD [J]. Am J Kidney Dis, 2008, 51(3): 395–406. doi: 10.1053/j.ajkd.2007.11.018
    [24] Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate[J]. Ann Intern Med, 2009, 150(9): 604–612. doi: 10.7326/0003-4819-150-9-200905050-00006
    [25] Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C [J]. N Engl J Med, 2012, 367(1): 20–29. doi: 10.1056/NEJMoa1114248
    [26] Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement[J]. Lancet, 1986, 1(8476): 307–310.
    [27] Kitiyakara C, Atichartakarn V. Renal failure associated with a specific inhibitor of BCR-ABL tyrosine kinase, STI 571[J]. Nephrol Dial Transplant, 2002, 17(4): 685–687. doi: 10.1093/ndt/17.4.685
    [28] Foringer JR, Verani RR, Tjia VM, et al. Acute renal failure secondary to imatinib mesylate treatment in prostate cancer[J]. Ann Pharmacother, 2005, 39(12): 2136–2138. doi: 10.1345/aph.1G131
    [29] Pinder EM, Atwal GS, Ayantunde AA, et al. Tumour lysis syndrome occurring in a patient with metastatic gastrointestinal stromal tumour treated with glivec (imatinib mesylate, Gleevec, STI571)[J]. Sarcoma 2007; 2007: 82012.
    [30] Al-Kali A, Farooq S, Tfayli A. Tumor lysis syndrome after starting treatment with Gleevec in a patient with chronic myelogenous leukemia[J]. J Clin Pharm Ther, 2009, 34(5): 607–610. doi: 10.1111/jcp.2009.34.issue-5
    [31] Pou M, Saval N, Vera M, et al. Acute renal failure secondary to imatinib mesylate treatment in chronic myeloid leukemia[J]. Leuk Lymphoma, 2003, 44(7): 1239–1241. doi: 10.1080/1042819031000079140
    [32] Vora A, Bhutani M, Sharma A, et al. Severe tumor lysis syndrome during treatment with STI 571 in a patient with chronic myelogenous leukemia accelerated phase[J]. Ann Oncol, 2002, 13(11): 1833–1834. doi: 10.1093/annonc/mdf277
    [33] Naughton CA. Drug-induced nephrotoxicity[J]. Am Fam Physician, 2008, 78(6): 743–750.
    [34] Stevens LA, Coresh J, Greene T, et al. Assessing kidney function–measured and estimated glomerular filtration rate[J]. N Engl J Med, 2006, 354(23): 2473–2483. doi: 10.1056/NEJMra054415
    [35] Sarnak MJ, Levey AS, Schoolwerth AC, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention[J]. Hypertension, 2003, 42(5): 1050–1065. doi: 10.1161/01.HYP.0000102971.85504.7c
    [36] Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization[J]. N Engl J Med, 2004, 351(13): 1296–1305. doi: 10.1056/NEJMoa041031
    [37] Holweger K, Bokemeyer C, Lipp HP. Accurate measurement of individual glomerular filtration rate in cancer patients: an ongoing challenge[J]. J Cancer Res Clin Oncol, 2005, 131(9): 559–567. doi: 10.1007/s00432-005-0679-7
    [38] Levey AS, Coresh J. Chronic kidney disease[J]. Lancet, 2012, 379(9811): 165–180. doi: 10.1016/S0140-6736(11)60178-5
    [39] .National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification[J]. Am J Kidney Dis, 2012, 39(Suppl. 1): 81–100.
    [40] Matsushita K, Mahmoodi BK, Woodward M, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate[J]. JAMA, 2012, 307(18): 1941–1951.
    [41] Shaffi K, Uhlig K, Perrone RD, et al. Performance of creatininebased GFR estimating equations in solid-organ transplant recipients[J]. Am J Kidney Dis, 2014, 63(6): 1007–1018. doi: 10.1053/j.ajkd.2014.01.436
    [42] Xie P, Huang JM, Liu XM, et al. (99m)Tc-DTPA renal dynamic imaging method may be unsuitable to be used as the reference method in investigating the validity of CDK-EPI equation for determining glomerular filtration rate[J]. PLoS One, 2013, 8(5): e62328. doi: 10.1371/journal.pone.0062328
    [43] Pei XH, He J, Liu Q, et al. Evaluation of serum creatinine- and cystatin C-based equations for the estimation of glomerular filtration rate in a Chinese population[J]. Scand J Urol Nephrol, 2012, 46(3): 223–231. doi: 10.3109/00365599.2012.660985
    [44] Pei X, Liu Q, He J, et al. Are cystatin C-based equations superior to creatinine-based equations for estimating GFR in Chinese elderly population[J]? Int Urol Nephrol, 2012, 44(6): 1877–1884. doi: 10.1007/s11255-012-0278-x
    [45] Pei X, He J, Wu J, et al. Diagnostic accuracy of serum cystatin C evaluating kidney function in Chinese general population[J]. J Nephrol, 2012, 20 (6) :579
    [46] Ye X, Wei L, Pei X, et al. Application of creatinine- and/or cystatin C-based glomerular filtration rate estimation equations in elderly Chinese[J]. Clin Interv Aging, 2014, 9: 1539–1549.
    [47] Zhu Y, Ye X, Zhu B, et al. Comparisons between the 2012 new CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equations and other four approved equations[J]. PLoS One, 2014, 9(1): e84688. doi: 10.1371/journal.pone.0084688
    [48] Wei L, Ye X, Pei X, et al. Diagnostic accuracy of serum cystatin C in chronic kidney disease: a meta-analysis[J]. Clin nephrol, 2015; ID: 108525–1.
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Accuracy of glomerular filtration rate estimation equations in patients with hematopathy

    Corresponding author: Weihong Zhao, zhaoweihongny@njmu.edu.cn
  • 1. Division of Nephrology, Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
  • 2. Division of Respiratory Medicine, Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China

Abstract: Renal dysfunction is a common side-effect of chemotherapeutic agents in patients with hematopathy. Although broadly used, glomerular filtration rate (GFR) estimation equations were not fully validated in this specific population. Thus, this study was designed to further assess the accuracy of various GFR equations, including the newly 2012 CKD-EPI equations. Referring to 99mTc-DTPA clearance method, three Scr-based (MDRD, Peking, and CKD-EPIScr), three Scys C-based (Steven 1, Steven 2, and CKD-EPIScys C), and three Scr-Scys C combination based (Ma, Steven 3, and CKD-EPIScr-Scys C) equations were included. Bias, P30, and misclassification rate were applied to compare the applicability of the selected equations. A total of 180 Chinese hematological patients were enrolled. Mean bias, absolute mean bias, P30, misclassification rate and Bland-Altman plots of the CKD-EPIScr-Scys C equation were 7.90 mL/minute/1.73 m2, 17.77 mL/minute/1.73 m2, 73.3%, 38% and 79.7 mL/minute/1.73 m2, respectively. CKD-EPIScr-Scys C predicted the most precise eGFR both in lymphoma and leukemia subgroups. Additionally, CKD-EPIScys C equation in the rGFR $\geqq $ 90 mL/minute/1.73 m2 subgroup and Steven 2 equation in the rGFR < 90 mL/ minute/1.73 m2 subgroup provided more accurate estimates in each subgroup. The CKD-EPIScr-Scys C equation could be recommended to monitor kidney function in hematopathy patients. The accuracy of GFR equations may be closely related with GFR level and kidney function markers, but not the primary cause of hematopathy.

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Introduction
  • Chronic kidney disease (CKD) has been a major health problem worldwide. Moreover, the incidence of CKD has been sharply expanding[1-2]. A cross-section survey in China demonstrated the prevalence of CKD reached 10.8%, equivalent to 119.5 million CKD subjects[3]. The incidence of renal impairment in patients of hematopathy has been increasing[4]. Acute renal impairment is commonly associated with early treatment-related toxicities that lead to severe hemodynamic disturbances, most notably hepatic veno-occlusive disease (VOD) and sepsis, and with the use of nephrotoxic medication[5-6]. Chronic renal impairment is commonly attributed to delayed effects of the infiltration of kidneys by leukemic cells, nephrotoxicity, and metabolic changes arising from chemotherapy, radiotherapy, infections, and intravascular coagulopathy[7-11]. A recent study has indicated that 20%-50% of multiple myeloma patients required dialysis after 15 years of illness[12]. Kidney Disease Improving Global Outcomes (KDIGO) in 2012 proposed that hematopathy-associated renal impairment should be regarded as a special kind of CKD[13], requiring regular monitoring of urine, blood pressure and GFR[14-16].

    As the best overall measurement of kidney function, the determination of GFR has three kinds. One is inulin clearance, which is regarded as the gold standard. Whereas, this impractical standard measurement of GFR is cumbersome, costly, and therefore not commonly available[17]. The second method is isotope plasma clearance, a substitution for inulin clearance, slightly simpler than the former in operation procedures, but also as accurate as the former. However, the isotope plasma clearance is also costly, and radioactive, just available in scientific research. The third kind is GFR evaluation equations, which now have been recommended to assess kidney function as a conventional method[18].

    The GFR evaluation equations were first constructed in 1976 by Cockcroft-Gault. After several generations were developed, the equations have experienced serum creatinine (Scr) based equations, serum cystatin C (Scys C) based equations and Scr-Scys C combination based equations. Several hundreds of equations were developed and validated in various ethnicities and CKD. However, few researchers focused on the subjects with hematopathy-associated renal impairment, who, more than ever, need accurate, noninvasive and repeatable methods to monitor kidney function. By far, no studies paid attention to this special population. Thus, this study was designed to validate whether the 2012 CKD-EPI equations were also accurate or not in hematological subjects, in comparison with other GFR equations (Table 1).

    Scr Scys C Gender Equation Years Subjects Disease Race
    Scr-based
    MDRD 186×Scr-1.154×Age-0.203×(0.742, if female) 1999 1, 628 CKD American
    Peking 175×Scr-1.234×Age-0.179×(0.79, if female) 2006 1, 570 CKD Chinese
    CKD-EPIScr ≤0.7 Female 144×(Scr/0.7)-0.329×(0.993)Age×(1.159) 2009 12, 150 CKD American
    > 0.7 144×(Scr/0.7)-1.209×(0.993)Age×(1.159)
    ≤0.9 Male 141×(Scr/0.9)-0.411×(0.993)Age×(1.159)
    > 0.9 141×(Scr/0.9)-1.209×(0.993)Age×(1.159)
    Scys C-based
    Steven 1 76.7×Scys C-1.19 2008 3, 418 CKD American
    Steven 2 127.7×Scys C-1.17×Age-0.13 ×(0.91 if female) 2008 3, 418 CKD American
    CKD-EPIScys C ≤0.8 133×(Scys C/0.8)-0.499×0.996Age×(0.932 if female) 2012 12, 150 CKD American
    > 0.8 133×(Scys C/0.8)-1.328×0.996Age×(0.932 if female)
    Scr and Scys C-based
    Ma 169×Scr-0.608×Scys C-0.63×Age-0.157×(0.83 if female) 2007 684 CKD Chinese
    Steven 3 177.6×Scr–0.65×Scys C–0.57×Age–0.20×(0.82 if female) 2008 3, 418 CKD American
    CKD-EPScr-Scys C ≤ 0.7 ≤0.8 Female 130×(Scr/0.7)-0.248×(ScysC/0.8)-0.375×0.995Age 2012 12, 150 CKD American
    > 0.8 130×(Scr/0.7)-0.248 ×(Scys C/0.8)-0.711×0.995Age
    > 0.7 ≤0.8 130 ×(Scr/0.7)-0.601×(Scys C/0.8)0.375×0.995Age
    > 0.8 130 ×(Scr/0.7)0.601 ×(Scys C/0.8)-0.711×0.995Age
    ≤ 0.9 ≤0.8 Male 135 ×(Scr/0.9)-0.207×(Scys C/0.8)0.375 ×0.995Age
    > 0.8 135×(Scr/0.9)-0.207×(Scys C/0.8)0.711 ×0.995Age
    > 0.9 ≤0.8 135×(Scr/0.9)-0.601×(Scys C/0.8)0.375×0.995Age
    > 0.8 135 ×(Scr/0.9)-0.601 ×(Scys C/0.8)0.711 ×0.995Age
    Scr: serum creatinine, shown as mg/dL; Scys C:serum cystatin C, shown as mg/L.

    Table 1.  Equations to predict glomerular filtration rate

Subjects and methods

    Subjects

  • A total of 180 Chinese participants with hematopathy, who were outpatients or inpatients of the First Affiliated Hospital of Nanjing Medical University between December 2009 and December 2015, were enrolled in the study. All participants provided their written informed consent. The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University.

    Subjects with acute kidney injury, severe edema, skeletal musclepleural effusion or ascites, malnutrition, amputation, heart failure or ketoacidosis were excluded. Additionally, subjects who were taking glucocorticosteroids, renal replacement therapy were also excluded. The subjects were divided into two subgroups, the lymphoma group and the leukemia group. Therefore, the GFR equations were compared not noly in the reference GFR (rGFR) levels (rGFR≥90 and < 90 mL/ minute/1.73 m2), but also in this two subgroups.

  • Determination of Scr and Scys C

  • Scr concentration was assayed by isotope dilution mass spectrometry (IDMS) traceable standardized enzymatic method (Kehua Dongling Diagnostic Products Co., Ltd., Shanghai, China), with a reported coefficient of variation of 6%, reference range 44-136 mmol/L. Scys C was examined by the particle-enhanced immunoturbidimetry method (Leadman Biomedical Co., Ltd., Beijing, China), with a reported coefficient of variation of 8%, reference range 0.60-1.55 mg/L. Both fasting serum samples were assayed on an Olympus AU5400 autoanalyser (Olympus Co., Japan).

  • Measurement and estimation of GFR

  • rGFR was measured using 99mTc-diethylene triamine pentaacetic acid (99mTc-DTPA) kidney dynamic imaging[19] on a single photon emission computed tomography (Siemens Co., Germany). Participants received a bolus injection in the elbow vein of 185 MBq 99mTc-DTPA (Nanjing Senke Co., China, purity 95%–99%), after oral hydration with 300 mL water, and then emptying the bladder. rGFR was automatically calculated on the computer with the Gates method after image acquisition[20].

    eGFR was calculated separately from GFR estimation equations, including Modification of Diet in Renal Disease (MDRD)[21], Peking[22], Steven 1 based on Scys C[23], Steven 2 based on Scys C[23], Steven 3 based on Scr and Scys C[23], Ma based on Scr and Scys C[22], Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on Scr (CKD-EPIScr)[24], CKD-EPI equation based on Scys C (CKD-EPICys C)[25], and CKD-EPI equation based on Scr and Scys C (CKD-EPIScr-Scys C)[25].

  • Statistical analyses

  • Bias, precision, and accuracy were calculated to compare the performance of the equations. Bias was defined as the mean difference between eGFR and rGFR (eGFR-rGFR). Absolute bias was equal to the absolute mean difference |(eGFR-rGFR)|. Precision was expressed as inter-quartile range (IQR) (25%–75%). P30 was determined as the proportion of eGFR within± 30% of rGFR.

    Additionally, Bland-Altman analysis[26] was also calculated to compare the 95% limits of agreement (LOA, mean Bias±1.96 SD) of the equations. The smaller the LOA, the greater precision. Wilcoxon matched-pairs signed rank test was used to compare the bias, and the McNemar test was used to compare P30. P < 0.05 was considered as statistically significant. The calculation and statistical analysis above were performed with SPSS software (version 20.0; SPSS, Chicago, IL, USA) and Medcalc (ver. 15.2 for Windows; MedCalc Software, Mariekerke, Belgium).

Results

    General clinical characteristics

  • A total of 180 Chinese participants with hematopathy in the First Affiliated Hospital of Nanjing Medical University between December 2009 and December 2015 were enrolled in this study. Their mean age was 40.56±13.95 years. The mean level of Scr, Scys C and rGFR were 0.78 mg/dL, 1.09 mg/L and 87.54 mL/ minute/1.73 m2, respectively. The mean values for the eGFRs varied from 80.42 mL/minute/1.73 m2 to 139.57 mL/minute/1.73 m2. The rGFR < 90 mL/minute/1.73 m2 group consisted of 96 subjects. The rGFR$\geqq $90 mL/ minute/1.73 m2 group was composed of 84 subjects. The detailed laboratory and anthropometric measurements are shown in Table 2.

    Subjects Total rGFR < 90 mL/minute/1.73 m2 rGFR≥90 mL/minute/1.73 m2
      Number (male/female) 180(103/77) 96(63/33) 84(40/44)*
      Age, years 40.56± 13.95 44.80± 13.28 35.71±13.17
      Height, cm 166.20± 6.66 167.58± 6.03 165.05 ± 6.89**
      Weight, kg 62.37±8.25 64.66±7.36 61.04 ± 8.91**
      BMI, kg/m2 22.53±2.15 22.74± 2.13 22.05±2.32**
    Renal variables
      Scys C, mg/l 1.09 ± 0.49 1.28 ± 0.59 1.01 ± 0.11**
      Scr, mg/dl 0.78 ± 0.48 1.08 ± 0.56 0.69±0.47**
      Albumin, g/l 41.10 ± 4.5 41.25 ± 4.88 41.06 ± 4.47
      rGFR, mL/minute/ 1.73m2 87.54± 21.05 71.98 ± 13.70 105.42±11.77*
    Types of hematopathy
      Lymphoma 88(48.9) 52(59.1) 36(40.1)
      Leukemia 63(35.0) 29(46.0) 34(54.0)
      Multiple myeloma 20(11.1) 10(50.0) 10(50.0)
      Anemia 6(3.3) 4(66.7) 2(33.3)
      Myelodysplastic syndrome 3(1.7) 1(33.3) 2(66.7)
    Cell values represent mean (SD) and N (%). Scr: serum creatinine; Scys C: serum cystatin C; rGFR: reference glomerular filtration rate; eGFR: estimated glomerular filtration rate. *P < 0.05, **P < 0.001, compared with the rGFR < 90 mL/minute/1.73 m2 group.

    Table 2.  Demographic and clinical characteristics

  • Accuracy of the equations in the whole population

  • Different equations performed with utterly different accuracies. All the three Scr-based equations overestimated rGFR more than 10 mL/minute/1.73 m2. The Peking equation unexpectedly deviated by 16.13 mL/ minute/1.73 m2. No Scr-based equations had a statisfactory performance, with low P30, high IQR and absolute mean bias. The other two kinds of GFR equations predicted relatively accurate estimates. The Scy C-based and Scr-Scy C combination based equations were similarly accurate. Among them, the CKD-EPIScr-Scys C equation performed the best according to the absolute mean bias and P30 (Table 3). Misclassification analysis of CKD stages and BlandAltman analysis also indicated that the CKD-EPIScr-Scys C equation performed well (Table 4 and Fig. 1).

    Equation Mean bias Absolute mean bias IQR P30 (%)
    Scr-based
    MDRD 31.91** 35.61 39.61 47.8
    Peking 52.03** 54.44** 53.22 28.3**
    CKD-EPIScr 22.52** 25.21** 27.67 52.8
    Scys C-based
    Steven 1 -6.83** 19.54** 30.61 71.1**
    Steven 2 -7.12** 19.36** 29.35 72.8**
    CKD-EPISCys c -2.86** 18.20** 30.73 73.3**
    Scr and Scys C -based
    Ma -2.86** 18.20** 30.73 73.3**
    Steven 3 18.87** 24.50** 33.37 62.8**
    CKD-EPIScr-Scys C 7.90** 17.77** 24.84 73.3**
    Mean Bias: eGFR-rGFR, mL/minute/1.73 m2; Absolute Mean Bias:| eGFR-rGFR|, mL/minute/1.73 m2; IQR: (75%-45%) limits of agreement of the equations, mL/minute/1.73 m2; P30: the percentage of eGFR within 30 % of rGFR; **P < 0.001, compared with the rGFR.

    Table 3.  Performance of GFR estimation equations in the overall sample

    Equation CKD stage Misclassification of CKD stage
    Stage 1 Stage 2 Stage 3-5
    rGFR 84 74 22
    Scr-based
      MDRD 140(24%) 27(41%) 13(7%) 74(41 %)
      Peking 152(47%) 16(50%) 12(0) 80(44%)
      CKD-EPIScr 153(40%) 16(63%) 11(0) 71(39%)
    Scys C-based
      Steven 1 54(21%) 91(44%) 35(51%) 71(39%)
      Steven 2 57(22%) 89(40%) 34(47%) 63(35%)
      CKD-EPIScys C 75(32%) 76(41%) 29(45%) 68(38%)
    Scr and Scys C -based
      Ma 137(45%) 26(50%) 17(12%) 77(43%)
      Steven 3 123(41%) 37(32%) 20(25%) 71(39%)
      CKD-EPIScr-Scys C 116(38) 42(28%) 22(31%) 69(38%)
    Note: Data are presented as number of each CKD stage patients(number of underestimation of CKD stage patients). Misclassification is defined as the proportion of patients with an unequal CKD stage between rGFR and the eGFR. Underestimation of CKD stage = CKDstagerGFR - CKDstageeGFR= 1.

    Table 4.  CKD misclassification in the additional external validation data set

    Figure 1.  Bland-Altman analysis of estimated GFR and reference GFR before and after modification

  • Accuracy of the equations in the subgroups

  • Consistent with the whole population, the Scr-based equations obviously overestimated GFR both in different subgroups of hematopathy and different CKD stages. Additionally, CKD-EPIScys C equation in the rGFR $\geqq $ 90 mL/minute/1.73 m2 subgroup and Steven 2 equation in the rGFR < 90 mL/minute/1.73 m2 subgroup provided relatively more accurate estimates in each subgroup. CKD-EPIScr-Scys C predicted the most precise eGFR both in the lymphoma and leukemia subgroups (Table 56).

    Equation Lymphoma Leukemia
    Mean bias Absolute mean bias IQR P30 (%) Mean bias Absolute mean bias IQR P30 (%)
    Scr-based
      MDRD 24.37 28.24 35.08 50.6 47.09 48.12 44.47 38.1**
      Peking 41.00** 43.11** 45.38 32.2 72.42 72.81** 52.84 49.21**
      CKD-EPIScr 22.43** 25.00** 25.09 49.4** -3.72** 28.47** 30.0 71.4**
    Scys C-based
      Steven 1 -9.29** 19.59** 29.12 71.3** -2.82** 19.76** 32.46 73.0**
      Steven 2 -9.30** 19.44** 29.73 71.3** -3.47** 19.47** 32.81 79.4**
      CKD-EPIScys C -4.11** 19.25** 30.9 73.6** 16.91** 19.85** 26.89 71.4**
    Scr and Scys C -based
      Ma 22.19** 27.30 30.94 51.7** 40.58** 41.26** 35.53 68.5**
      Steven 3 12.47** 20.36** 4.52 70.1** 28.87** 30.72** 33.24 79.2**
      CKD-EPIScr-Scys C 6.06** 17.89** 24.94 77** 13.91** 17.85** 24.79 85.7**
    Mean bias: eGFR–rGFR, mL/minute/1.73 m2; Absolute mean bias:|eGFR–rGFR|, mL/minute/1.73 m2; IQR: (75%–45%) limits of agreement of the equations, mL/minute/1.73 m2; P30: the percentage of eGFR within 30 % of rGFR; **P < 0.001, compared with the rGFR.

    Table 5.  Performance of the nine equations in different types of hematopathy

    Equation rGFR < 90 mL/minute/1.73 m2 rGFR ≥ 90 mL/minute/1.73 m2
    Mean bias Absolute mean bias IQR P30 (%) Mean bias Absolute mean bias IQR P30 (%)
    Scr-based
      MDRD 29.52** 33.00 40.00 38.5 34.64** 38.59 77.84 58.3
      Peking 60.27** 47.53** 55.77 26.0 16.51** 62.34** 39.64 31.0
      CKD-EPIScr 17.13** 29.74** 25.94 32.3** 14.25** 20.03** 19.93 76.2**
    Scys C-based
      Steven 1 -9.71** 14.79** 24.71 72.9** 2.73** 24.97** 39.1 69.0
      Steven 2 -9.73** 15.21** 22.77 75.0** 0.84** 24.10 35.45 70.2
      CKD-EPIScys C -6.38** 16.62** 28.81 69.8** 0.88** 20.00 31.53 77.4
    Scr and Scys C -based
      Ma 32.59** 28.93** 30.42 39.6 8.26** 37.01** 44.93 52.4**
      Steven 3 20.27** 21.59** 28.37 60.4** 6.59 27.81** 38.29 65.5**
      CKD-EPIScr-Scys C 5.17** 16.55** 25.41 70.8** 4.61** 19.17** 24.28 76.2**
    Mean bias: eGFR–rGFR, mL/minute/1.73 m2; Absolute mean bias:|eGFR–rGFR|, mL/minute/1.73 m2; IQR: (75% - 45%) limits of agreement of the equations, mL/minute/1.73 m2; P30: the percentage of eGFR within 30 % of rGFR; **P < 0.001, compared with the rGFR.

    Table 6.  Performance of the nine equations in different CKD stages

Discussion
  • Renal dysfunction is a common side effect of chemotherapeutic agents, and a number of case reports suggested that it may be associated with acute renal failure[27-32]. Some reports also suggested that this adverse effect may be caused by two possible mechanisms: tumor lysis syndrome, with precipitation and deposition of uric acid in the renal tubules, and toxic tubular damage. Tubular cells are susceptible to the toxic effects of drugs, as they have a role in concentrating and reabsorbing the glomerular filtrate, what exposes them to high levels of circulating toxins[33]. However, the early period of CKD is asymptomatic, which means people do not get identified or treated until the disease has progressed to near endstage kidney failure. Therefore, a precise, non-invasive and repeatable method is eager for periodically assessing kidney function for hematological patients. According to these facts, both K/DOQI and KDIGO practice guidelines for evaluation and management of CKD[13] recommended that use of GFR estimation equations for assessing kidney function. Furthermore, the lower the GFR level is, the higher the monitor frequencies are[34-36].

    Factors affecting the accuracy of GFR evaluation equations have been controversial[37]. Up to now, the recognized main influences on the accuracy of equations include design of the study, ethnicity, kidney function parameter, sample size and GFR level[37]. Whether the primary disease of CKD affects the accuracy of equations or not is uncertain. Or rather, whether one or a few "representative" equations could predict similar accuracy for different CKD patients is not able to draw an absolute conclusion. Thus, studies worldwide successively validated equations in various patients population to learn their accuracy for various target populations[34, 38-39].

    A meta-analysis indicated that the CKD-EPIScr-Scys C equation was more accurate than the MDRD equation in categorizing the risk of mortality and CKD progression to ESRD[40]. Another recent systematic review in hematological recipients study demonstrated that CKD-EPIScr-Scys C equation was superior to other included equations[41]. The results of this study found that the Scr-based equation obviously overestimated GFR both in different subgroups of hematopathy and different CKD stages. On the other hand, Scy C-based equations provided relatively more accurate estimates, CKD-EPIScr-Scys C predicting the most precise eGFR. These results were similar to those of the previous two meta-analyses, showing a hypothesis that the accuracy of the equations might be irrelevant with the primary disease of CKD, but closely with the design of the study, kidney function parameter and GFR level. The CKD-EPIScr-Scys C equation would be generally suitable for hematological patients, regardless of the type of diseases.

    Some study used the inulin single-injection method as the GFR reference standard. This study set the 99mTcDTPA kidney dynamic imaging as the GFR reference standard, which has been proved inferior to inulin clearance[42]. The principal limitations of the kidney dynamic imaging consist in clinical experiences and region of interest sketching by operators, which is slightly subjective. However, once the operators are experienced, the kidney dynamic imaging could also obtain an ideal performance, such as this study. Additionally, we consistently applied Gates method as the reference standard, not only in our modification studies but in new equation development studies[43-48]. Consequently, we always put the quality of Gates method at the first step. We examined the accuracy of GFR from the Gates method time and again. Of course, to dismiss the puzzle, our group have gradually developed dynamic dual plasma method and worked harder to get more accurate data.

    In conclusion, the accuracy of the GFR equations in this study did not achieve a satisfactory accuracy in hematological patients. Therefore, it is imminent to modify some equations or develop a new GFR equation for this sample. In this study, CKD-EPIScr-Scys C equation was suitable for renal function screening in whole patients of hematopathy. CKD-EPIScys C equation in the rGFR $\geqq $ 90 mL/minute/1.73 m2 subgroup and the Steven 2 equation in rGFR < 90 mL/minute/1.73 m2 subgroup could be recommended for monitoring kidney function in each subgroup.

Acknowledgments
  • This work was supported by the grants from the Major State Basic Research Development Program of China 2013CB530803, the National Natural Science Foundation of China H0511-81370843 and H0511- 81670677, Chinese Society of Nephrology (1502002- 0590), the Innovation of Science and Technology Achievement Transformation Fund of Jiangsu Province BL2012066, the Chinese Medical Association of Clinical Medicine Research Special Funds 15020020- 590, a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions JX10231801.

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