JAIDS Journal of Acquired Immune Deficiency Syndromes:
Performance of Creatinine and Cystatin C GFR Estimating Equations in an HIV-Positive Population on Antiretrovirals
Inker, Lesley A. MD, MS*; Wyatt, Christina MD†; Creamer, Rebecca BSN, RN, CCRC‡; Hellinger, James MD*; Hotta, Matthew BS†; Leppo, Maia BS*; Levey, Andrew S. MD*; Okparavero, Aghogho MD, MPH*; Graham, Hiba PharmD§; Savage, Karen BSN, RN, CCRC‡; Schmid, Christopher H. PhD*; Tighiouart, Hocine MS*; Wallach, Fran MD†; Krishnasami, Zipporah MD‡
*Tufts Medical Center, Boston, MA
†Department of Medicine, Mount Sinai School of Medicine, New York, NY
‡Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
§HIV Medical Affairs, Gilead Sciences, Inc, Foster City, CA.
Correspondence to: Lesley A. Inker, MD, MS, Division of Nephrology, Tufts Medical Center, 800 Washington St, Box #391, Boston, MA (e-mail: email@example.com).
Supported by Gilead Sciences, Inc under an investigator-initiated protocol NCRR L1RR025752; the National Center for Research Resources; the National Center for Advancing Translational Sciences, National Institutes of Health Grant UL1 RR025752 (Tufts Medical Center) and UL1 RR029887 (Mount Sinai School of Medicine); the Center for Clinical and Translational Sciences Grant UL1 RR025777 (University of Alabama at Birmingham). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
H. Graham is an employee of Gilead Sciences Inc. C. Wyatt has received funding for investigator-initiated research from the Gilead Foundation.
Preliminary results of this research were presented in abstract form at the Annual Meeting of the American Society of Nephrology, November 18, 2010, Denver, CO.
The other authors have no funding or conflicts of interest to disclose.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).
Received April 27, 2012
Accepted July 18, 2012
Objective: To evaluate the performance of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine, cystatin C, and creatinine–cystatin C estimating equations in HIV-positive patients.
Methods: We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) Study and CKD-EPI creatinine 2009, CKD-EPI cystatin C 2012, and CKD-EPI creatinine–cystatin C 2012 glomerular filtration rate (GFR) estimating equations compared with GFR measured using plasma clearance of iohexol in 200 HIV-positive patients on stable antiretroviral therapy. Creatinine and cystatin C assays were standardized to certified reference materials.
Results: Of the 200 participants, median (IQR) CD4 count was 536 (421) and 61% had an undetectable HIV viral load. Mean (SD) measured GFR (mGFR) was 87 (26) mL/min per 1.73 m2. All CKD-EPI equations performed better than the MDRD Study equation. All 3 CKD-EPI equations had similar bias and precision. The cystatin C equation was not more accurate than the creatinine equation. The creatinine–cystatin C equation was significantly more accurate than the cystatin C equation, and there was a trend toward greater accuracy than the creatinine equation. Accuracy was equal or better in most subgroups with the combined equation compared to either alone.
Conclusions: The CKD-EPI cystatin C equation does not seem to be more accurate than the CKD-EPI creatinine equation in patients who are HIV-positive, supporting the use of the CKD-EPI creatinine equation for routine clinical care for use in North American populations with HIV. The use of both filtration markers together as a confirmatory test for decreased estimated GFR based on creatinine in individuals who are HIV-positive requires further study.
Since the introduction of antiretroviral therapy (ART) in the mid-1990s, AIDS-related death and illnesses, including kidney failure, have declined in the United States and Europe.1–4 However, with improved survival and long-term ART, chronic diseases such as diabetes and hypertension are becoming more prevalent in the HIV-infected population.2,5–7 These chronic diseases and antiretrovirals themselves may cause acute and chronic kidney disease, leading to concern for an increase in End Stage Renal Disease (ESRD) in the future.8–11 An additional increase in the incidence of End Stage Renal Disease due to HIV-associated nephropathy (HIVAN) is projected because of the expansion of AIDS among the black population.12 Detection of kidney disease at earlier stages will facilitate timely initiation of treatments to slow progression of kidney disease and prevent complications. In addition, medications including antiretrovirals must be appropriately dosed based on kidney function, and clinical studies evaluating new medications or treatment regimens must be able to detect nephrotoxicity. These goals require accurate assessment of glomerular filtration rate (GFR).
More than 80% of clinical laboratories now report an estimated GFR when serum creatinine is measured,13,14 using the Modification of Diet in Renal Disease (MDRD) Study equation15 and the more recently developed Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 creatinine equation.16 Despite standardization of serum creatinine assays, GFR estimates remain relatively imprecise, especially at the high range,16 because of variation in non-GFR determinants of serum creatinine, particularly muscle mass and diet, which may be affected in acute and chronic illness.17 Cystatin C is a newer filtration marker that has been proposed to be better than creatinine, as its generation is thought not to be dependent on muscle mass or diet, and CKD-EPI has now developed estimating equations based on cystatin C and on the combination of cystatin C and creatinine.18 However, the factors that determine the level of cystatin C other than GFR are not well understood. Some studies have linked differences in generation of cystatin C to adipose tissue and higher or lower cell turnover, as is found in states of inflammation.19,20 The performance of estimating equations using creatinine or cystatin C has not been extensively tested in people with HIV. Given possible changes in muscle mass, adipose tissue, and/or inflammation in people with HIV, it is possible that neither filtration marker performs well in this population. The purpose of this study is to evaluate the GFR estimating equations based on creatinine and cystatin C compared with GFR measured using iohexol clearance in the HIV population on stable ART.
Participants were recruited from nephrology and infectious disease clinics at Mt Sinai Hospital and St Vincent's Medical Center in New York, Tufts Medical Center in Boston, and University of Alabama at Birmingham, and from HIV community centers in Boston, MA. Eligible patients included those who were older than 18 years of age, on stable ART for at least 3 months, with confirmed HIV status, and an HIV viral load and a CD4 count within 6 months of recruitment. Patients were excluded if they were pregnant, had an allergy or other contraindication to use of iohexol or iodine, recent acute kidney injury, had cognitive or physical impairments or were on cimetidine. Patients were recruited who were and who were not on tenofovir and those with higher levels of serum creatinine.
GFR was measured using protocols previously described.21–23 Subjects were asked to fast overnight and to discontinue medications that influence creatinine secretion for at least 1 week before the study visit (eg, trimethoprim). Five milliliters of iohexol (Omnipaque 300, GE Healthcare, Princeton, NJ) was administered through an intravenous line over a period of 30 seconds followed by 10 mL normal saline flush. The dose was calculated from the difference in the syringe weights before and after administration of the iohexol multiplied by the concentration of iohexol divided by the density at room temperature (1.345 g/cm3). Blood samples for plasma clearance measurements were taken from a separate intravenous line at approximately 10, 30, 120, and 240 minutes from the second intravenous line, with the exact times recorded. For participants with serum creatinine >1.5 mg/dL, a sample at 360 minutes was drawn.
Concentration of iohexol was determined using high-performance liquid chromatography at the University of Minnesota (CV 2.7% at 10 mg/dL and 2.4% at 50 mg/dL). Cystatin C was measured on the Siemens Pro-spec instrument particle-enhanced immunonephelometric assay (Siemens Healthcare Diagnostics, Deerfield, IL) at the University of Minnesota, traceable to the Institute for Reference Materials and Measurements certified reference material.24–26 Creatinine was measured at the University of Minnesota on the Roche-Hitachi P-Module instrument with Roche Creatininase Plus assay (Hoffman-La Roche, Ltd, Basel, Switzerland), traceable to National Institute Standardized Technology creatinine standard reference material 967.27,28
CKD risk factors include diabetes, hypertension and known cardiovascular disease. HIV status was determined by CD4 count and viral load in the past 6 months, and a list of all current ART medications was obtained. Inflammation was evaluated by level of C-reactive protein and serum albumin. Hepatitis B and C status were obtained either from a review of the patient's medical records or self-reports.
Plasma clearance of iohexol was calculated using the following equation: I/(exp A/α + exp B/β) × 1.73/BSA, where I is dose of the iohexol (mg), exp A is the intercept of the slow curve, α is its corresponding slope, exp B is the intercept of the fast curve, β is its corresponding slope, and BSA is the body surface area.21 The fast curve was determined from concentrations at 10 and 30 minutes. The slow curve was determined from concentrations at 120 and 300 minutes. GFR was corrected to 1.73 m2 BSA by the ratio of 1.73 m2/BSA.
GFR was estimated using the MDRD Study, CKD-EPI creatinine 2009, CKD-EPI cystatin C 2012, and CKD-EPI creatinine–cystatin C 2012 equations15,16,18 (herein referred to as creatinine, cystatin C, and creatinine–cystatin C equations) (for equation formulae, see Table 1, Supplemental Digital Content, http://links.lww.com/QAI/A346). Comparisons were performed in the overall data set and in subgroups defined by estimated GFR and clinical characteristics of age, sex, race, body mass index (BMI), presence of diabetes, tenofovir use, log transformed high sensitivity C-reactive protein, CD4 count (categorized by quartiles for initial regression analyses and by less than or greater than 350 cells/μL for graphic presentation), viral load (categorized as undetectable, less or greater than 1000 copies/mL for regression analyses, and by less than or greater than 1000 copies/mL for graphic presentation).
Performance of the estimating equations was evaluated using standard metrics used in the evaluation of estimating equations.29,30 Bias was assessed as the median difference between the measured and estimated GFR, with positive values indicating an underestimation of measured GFR. Precision was assessed as interquartile range (IQR) for the difference between measured and estimated GFR. Accuracy combines both bias and precision and was assessed as the percent of estimates within 30% of the measured GFR (P30), with large errors indicated by 1 − P30. In the overall data set, the Wilcoxon signed rank test was used to test statistical significance of the difference for the median of errors for each equation and McNemar test was used to determine statistical significance of differences of 1 − P30. Significance testing was not performed in subgroups because of small sample size in some groups and concern about multiple testing. For the purpose of reporting, we defined clinically significant differences as differences greater than 2 mL/min per 1.73 m2 for median difference and IQR, respectively, and differences greater than 4% of 1 − P30. Overall and in subgroups, confidence intervals were calculated by bootstrap methods31 (2000 bootstraps) for median difference and IQR of the differences and by the binomial method for P30. Analyses were performed using R Foundation for Statistical Computing (Version 2.9.2, Vienna, Austria)32 and SAS Institute Inc., (Version 9.2, Cary, NC).
Role of the Sponsor
The study was funded by Gilead Sciences Inc under an investigator-initiated protocol. Gilead Sciences was not required to approve publication of the finished manuscript. The project received institutional review board approval at all sites, and all participants provided written informed consent.
Clinical characteristics of the participants are shown in Table 1. Of the 200 participants, 145 (73%) were male, 104 (52 %) were blacks, 68 (34%) were older than 50 years. The mean (SD) measured GFR was 87 (26) mL/min per 1.73 m2 and ranged between 23 and 175 mL/min per 1.73 m2, with 27 (14%) having measured GFR less than 60 mL/min per 1.73 m2, and 90 (45%) greater than 90 mL/min per 1.73 m2. Median (IQR) CD4 count was 536 (421) cells/μL and 61% had undetectable viral loads. There were no significant differences in these descriptors between subjects taking tenofovir or not.
The CKD-EPI creatinine equation was less biased and more accurate than the MDRD study equation overall (P < 0.0001 for median difference and P = 0.018 for 1 − P30) and across the range of GFR (Table 2). Overall, the CKD-EPI creatinine and cystatin C equations had similar bias [median difference (95% confidence intervals) of 5.4 (2.7–7.9) vs 4.3 (1.2–7.7) mL/min per 1.73 m2, P = 0.048], precision [IQR of the difference of 22.7 (18.3–26.3) vs 25.7 (20.6–28.8) mL/min per 1.73 m2, P = 0.37], and accuracy [1 − P30 of 15% (10.5, 20) vs 17.5% (12.5, 22.8), P = 0.48]. There seemed to be differences in performance across the range of GFR (Table 2). Compared to the creatinine equation, the cystatin C equation had greater bias at GFR less than 60 mL/min per 1.73 m2 [13.1 (7.4–16.6) vs 7.4 (5.0–14.6) mL/min per 1.73 m2] and at GFR greater than 90 mL/min per 1.73 m2 [median difference of −5.4 (−10.9 to −0.5) vs 0.0 (−5.0 to 4.5) mL/min per 1.73 m2]. The 2 equations had similar accuracy at GFR greater than 60 mL/min per 1.73 m2, but the cystatin C equation was less accurate at GFR less than 60 mL/min per 1.73 m2 [1 − P30 of 25.6% (12.8–38.7) vs 35.7% (21.4–50)].
The creatinine–cystatin C equation had a statistically, but not clinically, significant larger bias compared with the creatinine equation [6.4 (3.1–9.3) vs 5.4 (2.7–7.9), P = 0.013] but a trend toward better accuracy [1 − P30 of 10% (6.0–14) vs 15% (10.5–20.0), P = 0.059] (Table 2). In contrast, the creatinine–cystatin C equation had similar bias to the cystatin C equation [6.4 (3.1–9.3) vs 4.3 (1.2–7.7) mL/min per 1.73 m2, P = 0.24], but better accuracy [1 − P30 of 10% (6.0–14.0) vs 17.5% (12.5–22.8), P = 0.0017]. For both equations, results were consistent across the GFR range (Table 2). The performance of the average of the creatinine and cystatin C equations was approximately equivalent to the creatinine–cystatin C equation but differed minimally at the extremes of eGFR (data not shown).
Figures 1, 2 show the bias and accuracy across subgroups for the creatinine, cystatin C, and creatinine–cystatin C equations. In general, there seemed to be no substantial differences in bias among the 3 equations for all subgroups defined by age, sex, race, BMI, diabetes status, HIV factors (CD4 count or viral load), or inflammation (C-reactive protein or serum albumin) (Fig. 1). Accuracy seemed equal or better in most of these subgroups with the combined equation compared with equations with either marker alone (Fig. 2). Compared with the creatinine and creatinine–cystatin C equations, the cystatin C equation seemed to have lower accuracy in the subgroup of participants with BMI less than 22 kg/m2 [1 − P30 of 31.4 (16.5–47.1) vs 22.9 (9.4–37.1) or 22.9 (9.4–37.5), respectively], or who were on tenofovir [1 − P30 of 21.6 (14.5 to 28.9) vs 13.6 (7.8, 19.7) or 9.6 (4.8 to 15), respectively]. Conversely, the creatinine equation seemed to be less accurate compared to the cystatin C and the creatinine–cystatin C equation in people with BMI greater than 30 kg/m2 [1 − P30 of 13.9 vs 5.6 or 5.6, respectively].
Here, we report on the performance of GFR estimating equations using creatinine, cystatin C, and the combination of the 2 markers in a population of HIV-positive patients on stable ART. Our results showed that similar to other populations, the CKD-EPI creatinine equation performs better than the MDRD study equation.16 The creatinine–cystatin C–based equation was significantly more accurate than the cystatin C equation, and there was a trend toward greater accuracy than the creatinine equation.18 These results have implications for clinical use of GFR estimating equations in the HIV-positive population.
The improved performance of the CKD-EPI creatinine equation compared to the MDRD Study equation is consistent with a recent systematic review that showed the CKD-EPI creatinine equation seems to be more accurate and less biased in studies with higher mean measured GFR across multiple clinical populations and research studies.33 In the recent paper evaluating the CKD-EPI 2012 cystatin C and creatinine–cystatin C equations in clinical and research populations without HIV, the creatinine–cystatin C equation was more precise and accurate than both creatinine and cystatin C equations. In the current study, we see some improvement in accuracy with the creatinine–cystatin C equation, however, it is not as great as was observed in the non-HIV population.18 This difference may be related to relatively small sample size or to greater imprecision of cystatin C in patients who are HIV-positive. Few studies have evaluated GFR estimating equations in an HIV population.34–38 Most studies have been small, did not use gold standard reference methods, or did not consider differences among assays.18 Similar to the current study, in a recent study of 22 HIV-positive patients, another combined creatinine–cystatin equation performed better than either creatinine or cystatin based equations, whereas the cystatin C equation had the worst performance.36 In contrast, in a recent study of 196 Thai HIV-positive patients, a different cystatin C equation than was used here had lesser bias than the creatinine equation, although had worse precision, and therefore had similar accuracy. Difference between these studies may be related to the differences in race and geographical region.39
In the current study, the cystatin C equation seems to be less accurate in people with lower levels of BMI, and the creatinine equation was less accurate in people with higher levels of BMI. This observation is opposite to the performance of these equations in non-HIV study populations, where the cystatin C equation was more accurate than either creatinine or creatinine–cystatin C equations in people with lower levels of BMI, and the 3 equations had similar performance at higher levels of BMI.18 The results in the non-HIV population were hypothesized to be because of the fact that low BMI is an indication of low muscle mass, and the better performance of the cystatin C equation reflects the lesser association of cystatin C to muscle mass than creatinine. Our data suggests that this relationship may be more complex, and the relative contribution of adipose and muscle to BMI needs to be considered. In the Study of Fat Redistribution and Metabolic Change in HIV infection (FRAM); HIV-positive patients were shown to have lower levels of both adipose tissue and skeletal muscle compared with controls.40,41 In vitro studies have shown a relationship between preadipocyte cell cultures and cystatin C generation.42 Variation in both muscle and adipose mass in HIV-positive patients versus HIV-negative patients may lead to changes in both creatinine and cystatin C independent of GFR, which in turn affects performance of both creatinine and cystatin C equations. Inflammation is thought by some to be another determinant of cystatin C independent of GFR.19,43 It is possible that people with lower levels of BMI have higher levels of inflammation and if so, this could also be a cause of the lower accuracy of the cystatin C equation in patients with low BMI. Previous studies have demonstrated higher levels of high C-reactive protein and fibrinogen levels in HIV-positive males compared to controls.40,44 However, we did not observe any associations of performance of the equations with C-reactive protein or serum albumin. We cannot fully explain the observation of the worse accuracy of the cystatin C equation in HIV-positive patients with low BMI, but it does caution use of cystatin C–based equations in other population with low BMI on the assumption that creatinine-based estimates are expected to be inaccurate. The more consistent performance of the combined creatinine–cystatin C equation across BMI is likely because of smaller influence of non-GFR effects on each marker.
The performance of the creatinine and creatinine–cystatin C equations was similar for patients who were and were not on tenofovir, possibly supporting a lack of effect of tenofovir on creatinine handling by the kidney. The cystatin C equation seemed to be less accurate in patients on tenofovir. There were no apparent differences in clinical characteristics between patients on and not on tenofovir, which suggests that the greater inaccuracy may be because of a direct effect of tenofovir on the cystatin C assay, or on the generation, renal and/or extra-renal elimination of cystatin C. Given that both under and over estimation (ie, no systematic bias) were observed, it is likely that there are several contributing factors, or there is a variable effect of one factor. To our knowledge, no prior studies have suggested a possible mechanism for this effect, and as such, this requires confirmation and further study. Differences in drug effects on creatinine versus cystatin C may also offer a potential explanation for the differences in GFR decline when GFR was estimated using creatinine or cystatin C in a trial comparing GFR decline in patients treated with tenofovir/emtricitabine in association with atazanavir/ritonavir or efavirenz.45
There are several implications to these findings. First, this is one of the first large scale studies evaluating GFR estimating equations in an HIV-positive population, and our results validate the use of CKD-EPI creatinine equation in this population. The better performance of the CKD-EPI equation compared to the MDRD Study equation reinforces recommendations by others and decisions by large laboratory service providers to report the CKD-EPI equation in place of the MDRD Study equation.46–49 Accurate GFR estimates are particularly important in this population because of the high prevalence of CKD, need to dose adjust multiple medications, and as such knowledge that the CKD-EPI creatinine equation is unbiased is informative for common clinical decision making in HIV-positive patients. However, as in non-HIV populations, creatinine-based GFR estimates were imprecise.14,16 The small sample size does not allow for definitive recommendations as to which patients are most likely to have an inaccurate estimate, but it would be prudent to be cautious in people at the extremes of BMI.22 Clinicians should be cognizant of these limitations and should consider performing a confirmatory test, such as measured creatinine clearance from a 24-hour urine collection or measured GFR using an exogenous marker, especially when using medications with narrow therapeutic windows. Second, the lack of definitive improvement with the cystatin C–based equations is in contrast to prior hypotheses as to the performance of cystatin C in HIV-positive patients. Further studies to confirm these findings and to explore the non-GFR determinants of cystatin C in this population are required. Third, this is one of the first indications of a possible difference in accuracy of cystatin C–based equations by drug status. Because one of the major uses of GFR estimates in clinical practice is to adjust doses of medications, it is critical that the effect of drugs on the level of an endogenous marker is known, as is the case of the effect of cimetidine and trimethoprim on the level of serum creatinine.
Strengths of this study include its rigorous gold standard measure of GFR. Other studies have shown that accuracy of GFR estimating equations is not dependent on the GFR measurement method.33 Recruitment of patients from 3 clinical sites allowed us to capture the broad spectrum of prevalent HIV patients on ART in current clinical practice. We recruited specifically for patients on and not on tenofovir, the most commonly used antiretroviral drug, and at low levels of GFR, to ensure that we captured the heterogeneity in clinical practice. Serum creatinine and cystatin C measurements were traceable to high-level references materials for the respective assays, and all equations are expressed use of such values.
Our study has limitations. First, the small numbers within subgroups do not allow us to make definitive conclusions about performance within subgroups. Second, the study population had a narrow age range, and also did not include a substantial number of patients at very low levels of GFR, but this is reflective of current clinical practice. Third, the population may not be representative of patients with markedly reduced muscle mass or malnutrition, in whom both creatinine and cystatin C might be not be expected to perform well. Fourth, GFR is known to be measured with error, which may account for some of the observed imprecision.22,50
In conclusion, the CKD-EPI cystatin C equation does not seem to be more accurate than the CKD-EPI creatinine equation in patients who are HIV-positive, supporting the use of the CKD-EPI creatinine equation for routine clinical care. Clinicians should consider confirmatory tests for some patients where estimated GFR based on creatinine is expected to be inaccurate, especially those at the extremes of muscle mass.22 The use of both filtration markers together as a confirmatory test for decreased estimated GFR based on creatinine in individuals who are HIV-positive requires further study.
1. Mocroft A, Vella S, Benfield TL, et al.. Changing patterns of mortality across Europe in patients infected with HIV-1. EuroSIDA Study Group. Lancet. 1998;352:1725–1730.
2. Palella FJ Jr, Baker RK, Moorman AC, et al.. Mortality in the highly active antiretroviral therapy era: changing causes of death and disease in the HIV outpatient study. J Acquir Immune Defic Syndr. 2006;43:27–34.
3. Palella FJ Jr, Delaney KM, Moorman AC, et al.. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998;338:853–860.
4. Vittinghoff E, Scheer S, O'Malley P, et al.. Combination antiretroviral therapy and recent declines in AIDS incidence and mortality. J Infect Dis. 1999;179:717–720.
5. Seaberg EC, Munoz A, Lu M, et al.. Association between highly active antiretroviral therapy and hypertension in a large cohort of men followed from 1984 to 2003. AIDS. 2005;19:953–960.
6. Brown TT, Cole SR, Li X, et al.. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med. 2005;165:1179–1184.
7. Roling J, Schmid H, Fischereder M, et al.. HIV-associated renal diseases and highly active antiretroviral therapy-induced nephropathy. Clin Infect Dis. 2006;42:1488–1495.
8. Scherzer R, Estrella M, Li Y, et al.. Association of tenofovir exposure with kidney disease risk in HIV infection. AIDS. 2012;26:867–875.
9. Daugas E, Rougier JP, Hill G. HAART-related nephropathies in HIV-infected patients. Kidney Int. 2005;67:393–403.
10. Izzedine H, Launay-Vacher V, Deray G. Antiviral drug-induced nephrotoxicity. Am J Kidney Dis. 2005;45:804–817.
11. Berns JS, Kasbekar N. Highly active antiretroviral therapy and the kidney: an update on antiretroviral medications for nephrologists. Clin J Am Soc Nephrol. 2006;1:117–129.
12. Schwartz EJ, Szczech LA, Ross MJ, et al.. Highly active antiretroviral therapy and the epidemic of HIV+ end-stage renal disease. J Am Soc Nephrol. 2005;16:2412–2420.
13. Miller WG. Reporting estimated GFR: a laboratory perspective. Am J Kidney Dis. 2008;52:645–648.
14. Stevens LA, Schmid CH, Greene T, et al.. Comparative performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) Study equations for estimating GFR levels above 60 mL/min/1.73 m2
. Am J Kidney Dis. 2010;56:486–495.
15. Levey AS, Coresh J, Greene T, et al.. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–254.
16. Levey AS, Stevens LA, Schmid CH, et al.. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–612.
17. Stevens LA, Coresh J, Greene T, et al.. Assessing kidney function–measured and estimated glomerular filtration rate. N Engl J Med. 2006;354:2473–2483.
18. Inker L, Schmid CH, Tighiouart H, et al.. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:10–19.
19. 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. Kidney Int. 2004;65:1416–1421.
20. 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. Am J Kidney Dis. 2008;51:395–406.
21. Schwartz GJ, Furth S, Cole SR, et al.. Glomerular filtration rate via plasma iohexol disappearance: pilot study for chronic kidney disease in children. Kidney Int. 2006;69:2070–2077.
22. Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20:2305–2313.
23. Brochner-Mortensen J. A simple method for the determination of glomerular filtration rate. Scand J Clin Lab Invest. 1972;30:271–274.
24. Grubb A, Blirup-Jensen S, Lindstrom V, et al.. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem Lab Med. 2010;48:1619–1621.
25. Blirup-Jensen S, Grubb A, Lindstrom V, et al.. Standardization of Cystatin C: development of primary and secondary reference preparations. Scand J Clin Lab Invest Suppl. 2008;241:67–70.
26. Inker LA, Eckfeldt J, Levey AS, et al.. Expressing the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) cystatin C equations for estimating GFR with standardized serum cystatin C values. Am J Kidney Dis. 2011;58:682–684.
27. Levey AS, Coresh J, Greene T, 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;53:766–772.
28. Myers GL, Miller WG, Coresh J, et al.. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem. 2006;52:5–18.
29. Stevens LA, Zhang Y, Schmid CH. Evaluating the performance of equations for estimating glomerular filtration rate. J Nephrol. 2008;21:797–807.
30. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 suppl 1):S1–S266.
31. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York, NY: Chapman and Hall; 1993.
32. R Development Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing. Available at: http://www.R-project.org
. Accessed June 12, 2011.
33. 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;156:785–795.
34. Barraclough K, Er L, Ng F, et al.. A comparison of the predictive performance of different methods of kidney function estimation in a well-characterized HIV-infected population. Nephron Clin Pract. 2009;111:c39–c48.
35. Bonjoch A, Bayes B, Riba J, et al.. Validation of estimated renal function measurements compared with the isotopic glomerular filtration rate in an HIV-infected cohort. Antiviral Res. 2010;88:347–354.
36. Beringer PM, Owens H, Nguyen A, et al.. Estimation of glomerular filtration rate by using serum cystatin C and serum creatinine concentrations in patients with human immunodeficiency virus. Pharmacotherapy. 2010;30:1004–1010.
37. Vrouenraets SM, Fux CA, Wit FW, et al.. A comparison of measured and estimated glomerular filtration rate in successfully treated HIV-patients with preserved renal function. Clin Nephrol. 2012;77:311–320.
38. van Deventer HE, Paiker JE, Katz IJ, et al.. A comparison of cystatin C- and creatinine-based prediction equations for the estimation of glomerular filtration rate in black South Africans. Nephrol Dial Transplant. 2011;26:1553–1558.
39. Praditpornsilpa K, Avihingsanon A, Chaiwatanaratd T, et al.. Comparisons between validated estimated glomerular filtration rate (GFR) equations and isotopic GFR in HIV patients. AIDS. 2012;26:1781–1788.
40. Reingold J, Wanke C, Kotler D, et al.. Association of HIV infection and HIV/HCV coinfection with C-reactive protein levels: the fat redistribution and metabolic change in HIV infection (FRAM) study. J Acquir Immune Defic Syndr. 2008;48:142–148.
41. Study of Fat Redistribution and Metabolic Change in HIV Infection (FRAM). Fat distribution in women with HIV infection. J Acquir Immune Defic Syndr. 2006;42:562–571.
42. Taleb S, Cancello R, Clément K, et al.. Cathepsin s promotes human preadipocyte differentiation: possible involvement of fibronectin degradation. Endocrinology. 2006;147:4940–4945.
43. Stevens LA, Schmid CH, Greene T, et al.. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009;75:652–660.
44. Tien PC, Choi AI, Zolopa AR, et al.. Inflammation and mortality in HIV-infected adults: analysis of the FRAM study cohort. J Acquir Immune Defic Syndr. 2010;55:316–322.
45. Albini L, Cesana BM, Motta D, et al.. A randomized, pilot trial to evaluate glomerular filtration rate by creatinine or cystatin C in naive HIV-infected patients after tenofovir/emtricitabine in combination with atazanavir/ritonavir or efavirenz. J Acquir Immune Defic Syndr. 2012;59:18–30.
46. Becker BN, Vassalotti JA. A software upgrade: CKD testing in 2010. Am J Kidney Dis. 2010;55:8–10.
49. Ibrahim F, Hamzah L, Jones R, et al.. Comparison of CKD-EPI and MDRD to estimate baseline renal function in HIV-positive patients. Nephrol Dial Transplant. 2012;27:2291–2297.
50. Kwong YT, Stevens LA, Selvin E, et al.. Imprecision of urinary iothalamate clearance as a gold-standard measure of GFR decreases the diagnostic accuracy of kidney function estimating equations. Am J Kidney Dis. 2010;56:39–49.
This article has been cited 3 time(s).
Bmc NephrologyCreatinine-or cystatin C-based equations to estimate glomerular filtration in the general population: impact on the epidemiology of chronic kidney diseaseBmc Nephrology
Plos OneEstimating Kidney Function in HIV-Infected Adults in Kenya: Comparison to a Direct Measure of Glomerular Filtration Rate by Iohexol ClearancePlos One
Journal of Infectious DiseasesNephrotoxicity of Antiretroviral Agents: Is the List Getting Longer?Journal of Infectious Diseases
measured glomerular filtration rate; estimated glomerular filtration rate; Chronic Kidney Disease Epidemiology collaboration equation (CKD-EPI); Modification of Diet in Renal Disease Study equation (MDRD); creatinine; cystatin C
Supplemental Digital Content
© 2012 Lippincott Williams & Wilkins, Inc.
Highlight selected keywords in the article text.