HHS Public Access Author manuscript Am J Kidney Dis. Author manuscript; available in PMC 2017 July 05.

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1 HHS Public Access Author manuscript Published in final edited form as: Am J Kidney Dis March ; 69(3): doi: /j.ajkd Performance of the Chronic Kidney Disease Epidemiology Collaboration equation to estimate glomerular filtration rate in a longitudinal study of Autosomal Dominant Polycystic Kidney Disease Chengli Shen, Ph.D. *, Douglas Landsittel, Ph.D. *, María V. Irazabal, M.D., Alan S.L. Yu, M.B., B.Chir., Arlene B. Chapman, M.D. #, Michal Mrug, M.D. &, Jared J. Grantham, M.D., Kyongtae T. Bae, M.D., Ph.D. *, William M. Bennett, M.D., Michael F. Flessner, M.D., Ph.D., and Vicente E. Torres, M.D., Ph.D. on behalf of the CRISP Investigators/Study Group * University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania Mayo Clinic College of Medicine, Rochester, Minnesota Kansas University Medical Center, Kansas City, Kansas # University of Chicago, Chicago, Illinois & University of Alabama, Birmingham, Alabama Legacy Good Samaritan Hospital, Portland, Oregon National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland Glomerular filtration rate (GFR) can only be measured indirectly 1. The gold standard is the renal clearance of inulin. Because it is cumbersome, alternative methods such as the renal clearance of iothalamate and plasma clearance of iohexol were developed. Other methods estimate GFR using equations based on endogenous filtration markers such as creatinine and cystatin C 2. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is recommended for GFR estimation. Whether egfr is sufficiently accurate in longitudinal studies of Autosomal Dominant Polycystic Kidney Disease (PKD, ADPKD) is controversial 3. A comparison with plasma iohexol clearance at two time points over one year questioned its reliability 4. Because of the small number of patients and the inability to estimate slopes, long-term adequately powered studies were recommended. Here we compare the performance of measured (mgfr) and egfr in the Consortium for Renal Imaging Studies of PKD (CRISP) 5 7, a prospective study of 241 patients, yearold, with an initial creatinine clearance >70 ml/min, and measurements of height adjusted total kidney volume (HtTKV), mgfr by the iothalamate clearance 5 and serum creatinine Corresponding author: Vicente E. Torres, M.D. Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN phone fax, torres.vicente@mayo.edu. CONTRIBUTIONS Research idea and study design: CS, DL, MVI, VET; data acquisition: ASLY, ABC, MM, JJG, VET; image analysis: KTB; statistical analysis: CS, DL; data analysis: CS, DL, MVI, WMB, MFF, VET. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. VET takes responsibility that this study has been reported honestly, accurately, and transparently; that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

2 Shen et al. Page 2 yearly during four years and biennially thereafter 8. To adjust for calibration bias, serum samples were exchanged between the four study sites and the Cleveland Clinic Laboratory and calibration equations were derived for each site. In the earlier CRISP publication by Rule et al analyses were conducted with and without calibration 9. As reported in this publication, there was good agreement between the egfr slopes with and without calibration and the predictor associations did not change with the calibrations. Bias was defined as mgfr minus egfr; precision as the standard deviation of bias; accuracy as the percentage of egfr values within 10% (P10) or 30% (P30) of mgfr values. Linear mixed coefficient models (random intercept and slope), accounting for within subject correlation and assuming random variation of annual GFR changes between subjects, were used to calculate egfr and mgfr slopes. Pearson correlation coefficients between GFR values and slopes were calculated. Relationships of HtTKV and change in HtTKV (adjusted for age, gender and race) with mgfr and egfr were analyzed by linear mixed models. Statistical significance was defined as two sided p<0.05. Baseline characteristics of the CRISP cohort are shown in Table 1. At all visits, mean mgfr was higher than egfr (Figure 1A and Supplemental Table 1). At baseline, mean bias and precision of egfr were 5.2 and 21.9 ml/min/1.73 m 2 (Table 1). The bias was greater at high levels of egfr (Supplemental Figure 1). The annual change of bias during the duration of the study was 0.21 ml/min/1.73 m 2 (p=0.126). P10 and P30 values at baseline were 33.5 and 83.5% (Table 1). egfr and mgfr were significantly correlated at all time-points, increasingly from baseline (r=0.62) to year 12 (r=0.92) (Supplemental Figure 2). During 12 years of follow-up mgfr and egfr decreased from 97.8±24.7 and 92.6±22.7 to 65.2±38.6 and 58.6±32.0 ( 32.6±33.9 and 32.9±30.8 ml/min/1.73 m 2, respectively; Figure 1A and Supplemental Table 1). Mean egfr and mgfr slopes were similar ( 2.6 and 2.5 ml/min/1.73m 2 /year, P = 0.12). Mean bias and precision of egfr slopes were 0.1 and 1.1 ml/min/1.73m2/year, respectively. P10 and P30 were 22.0% and 70.3%. egfr and mgfr slopes were significantly correlated (r = 0.77, P<0.001; Figure 1B). Multivariable analyses adjusting for age, gender and race showed that HtTKV at baseline and change in HtTKV were significantly associated with the rates of mgfr decline at 8 and 12 years and of egfr decline at 4, 8 and 12 years of follow-up (Supplemental Table 2). The strengths of the associations, reflected by the beta coefficients, were modestly higher for mgfr than for egfr, but this advantage was offset by larger standard errors and did not impact the statistical significance. Four studies have measured the bias, precision and accuracy of egfr compared to mgfr in ADPKD 4,8,10 (Supplemental Table 3). In general the bias of egfr is greater and the accuracy less when the mgfr values are high. The mean GFR of the patients in our study was higher than that of the patients in the other three studies, accounting for the larger bias and lower precision of egfr in our study. Indeed, we found that bias was high and precision low when mgfr was 70, whereas the reverse was true when mgfr was <70 ml/min/1.73 m 2 (Supplemental Table 3).

3 Shen et al. Page 3 Four studies including the present 4,8,9 inform on the performance of egfr as compared to mgfr to detect changes in renal function in longitudinal studies. Rule et al found that baseline predictors of disease progression in CRISP correlated slightly better with mgfr compared to egfr slopes 9. Ruggenenti et al reported a greater decline over one year using mgfr compared to egfr 4. Spithoven et al showed a strong correlation between egfr and mgfr and similar rates of GFR decline over three years with both methods 8. By comparison to the previous analysis of CRISP with only three years a follow-up and no significant change in mean egfr or mgfr 9, the current analysis includes 12 years of follow-up during which mean egfr and mgfr decreased substantially. This updated analysis shows that both methods detect similar and strongly correlated changes in GFR and are equally capable to detect the associations of baseline HtTKV and of change in HtTKV with the rate of decline in GFR. Supplementary Material Acknowledgments References Refer to Web version on PubMed Central for supplementary material. SUPPORT AND FINANCIAL DISCLOSURE The CRISP study is supported by cooperative agreements from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (DK056943, DK056956, DK056957, DK056961). This study was also supported in part by the NIDDK through P30 grants to the Kansas PKD Research and Translation Core Center (DK106912) and the Mayo Translational PKD Center (DK090728), by the National Center for Research Resources General Clinical Research Centers at each institution (RR000039, Emory University; RR00585, Mayo College of Medicine; RR23940, Kansas University Medical Center; RR000032, University of Alabama at Birmingham), and the National Center for Advancing Translational Sciences Clinical and Translational Science Awards at each institution (RR and TR000454, Emory; RR and TR000135, Mayo College of Medicine; RR and TR000001, Kansas University Medical Center; RR025777, TR and TR001417, University of Alabama at Birmingham; RR and TR000005, University of Pittsburgh School of Medicine). The investigators are indebted to the study coordinators in CRISP. 1. Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis. 2014; 63: DOI: /j.ajkd [PubMed: ] 2. Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med. 2012; 156: W-270, W-271, W-272, W-273, W-274, W-275, W-276, W-277, W-278. DOI: / [PubMed: ] 3. Chapman AB, Devuyst O, Eckardt KU, et al. Autosomal dominant polycystic kidney disease (ADPKD): executive summary from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2015; 88: [PubMed: ] 4. Ruggenenti P, Gaspari F, Cannata A, et al. Measuring and estimating GFR and treatment effect in ADPKD patients: results and implications of a longitudinal cohort study. PLoS One. 2012; 7:e32533.doi: /journal.pone [PubMed: ] 5. Chapman A, Guay-Woodford L, JJG, et al. 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: [PubMed: ] 6. Grantham JJ, Torres VE, Chapman AB, et al. Volume progression in polycystic kidney disease. N Engl J Med. 2006; 354: [PubMed: ]

4 Shen et al. Page 4 7. Chapman AB, Bost JE, Torres VE, et al. Kidney Volume and Functional Outcomes in Autosomal Dominant Polycystic Kidney Disease. Clin J Am Soc Nephrol. 2012; 7: CJN [pii]. DOI: /CJN [PubMed: ] 8. 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 Kidney Dis. 2013; 62: DOI: /j.ajkd [PubMed: ] 9. Rule AD, Torres VE, Chapman AB, et al. Comparison of methods for determining renal function decline in early autosomal dominant polycystic kidney disease: the consortium of radiologic imaging studies of polycystic kidney disease cohort. J Am Soc Nephrol. 2006; 17: [PubMed: ] 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: DOI: / [PubMed: ]

5 Shen et al. Page 5 Figure 1. A. Dynamic changes of egfr and mgfr over time. B. Correlations of egfr slopes and mgfr slopes; the regression line (solid black) and line of identity (black dash) are shown; intercept (CI 1.00, 0.844); slope (CI 0.584, 0.634).

6 Shen et al. Page 6 Table 1 Patient Characteristics and Measures of Performance of the CKD-EPI Equation at Baseline Using mgfr as Reference Variable N (%) Mean SD Gender Race Male 96 (40) Female 145 (60) White 209 (86.7) Nonwhite 32 (13.3) Age (years) Height (cm) BSA (m 2 ) BMI Diastolic BP (mmhg) Systolic BP (mmhg) TKV (ml) HtTKV (ml/m) Serum creatinine (mg/dl) Urine albumin (mg/24h) * 12.0, 51.0 * mgfr (ml/min/1.73 m 2 ) egfr (ml/min/1.73 m 2 ) Bias (ml/min/1.73 m2) Precision (ml/min/1.73 m2) P30 (%) P10 (%) Body Mass Index (BMI); Height Adjusted TKV (HtTKV); Total Kidney Volume(TKV); Body Surface Area (BSA) * Median and interquartile range Bias= mgfr minus egfr; Precision=SD of bias. P30 and P10= percentage of egfr estimates within 30% or 10% of mgfr