Selected Kidney Function Tests

Number: 0775

Policy

Aetna considers the RenalVysion test (Nephrocor, Glen Allen, VA) for diagnosing and monitoring kidney disease experimental and investigational because of insufficient evidence of its effectiveness.

Aetna considers the KidneyIntelX test for the prediction of renal decline in individuals with type 2 diabetes mellitus experimental and investigational because of insufficient evidence of its effectiveness.

Aetna considers the use of a transdermal system with pyrazine‐based fluorescent agents experimental and investigational for measurement of glomerular filtration rate because the effectiveness of this approach has not been established.

Aetna considers the GFRNMR test (a combination of multiple metabolites including myo-inositol, dimethyl sulfone, valine, and creatinine and analyzed by nuclear magnetic resonance spectroscopy) experimental and investigational for assessment of glomerular filtration rate/kidney function because its effectiveness has not been established.

Background

An estimated 7 % of adults aged 20 or older (15.5 million adults) have physiological evidence of chronic kidney disease (CKD) as defined by a moderately or severely reduced glomerular filtration rate (GFR) (Coresh et al, 2007).  Patients with kidney disease have a variety of different clinical presentations.  Some have symptoms that are directly referable to the kidney (e.g., gross hematuria, flank pain) or to extra-renal sites of involvement (e.g., edema, hypertensive, signs of uremia).  Many patients, however, lack specific symptoms and are noted on routine examination to have an elevated plasma creatinine concentration or an abnormal urinalysis.

According to the National Institute for Clinical Excellence (NICE, 2008), 30 % of people with advanced kidney disease are referred late to nephrology services causing increased mortality and morbidity.  Strategies aimed at earlier identification and, where possible, prevention of progression to established renal failure are needed.

The National Kidney Foundation practice guidelines on CKD (2002) recommended identifying persons at increased risk for CKD to include persons with diabetes, hypertension, those with a family history of CKD, age greater than 60 years, and those with United States racial or ethnic minority status.  The guidelines stated that kidney damage can be detected by measuring the albumin-creatinine ratio in un-timed ("spot") urine specimens, and estimating the GFR from serum creatinine measurements by using prediction equations.  The estimated GFR comes from a formula that combines the creatinine level with the patient's age, race and gender.  Chronic kidney disease is defined as the presence of kidney damage or level of GFR less than 60 ml/min per 1.73 m2 that is present for 3 or more months.

The indications for performing a renal biopsy varies among nephrologists, being determined in part by the presenting signs and symptoms.  A percutaneous renal biopsy is sometimes performed to obtain a diagnosis, help guide therapy, and ascertain the degree of active and chronic changes.  The routine evaluation of a percutaneous renal biopsy involves examination of the tissue under light, immunofluorescence, and electron microscopy. 

RenalVysion (Nephrocor, Glen Allen, VA), a urine-based test that integrates urine cytopathlogy with urine chemistries (i.e., creatinine, protein, beta-2 microglobulin, microalbumin), is being marketed as a 'liquid biopsy' for the early diagnosis and monitoring of kidney disease.  However, there are no published studies on the use of RenalVysion for kidney disease and no medical professional society recommends RenalVysion testing for diagnosing and monitoring kidney disease.  Chronic kidney disease can be readily detected with tests for proteinuria, hematuria, and estimated GFR.  Randomized controlled studies comparing RenalVysion to these standard methods for diagnosing and monitoring kidney disease are needed.

KidneyIntelX

RenalytixAI is a developer of artificial intelligence (AI) enabled clinical diagnostic solutions for kidney disease.  RenalytixAI’s solutions are being designed to make significant improvements in kidney disease risk assessment, clinical care and patient stratification for drug clinical trials.  RenalytixAI’s technology platform will draw from distinct sources of patient data, including large electronic health records, predictive blood-based biomarkers and other genomic information for analysis by learning computer algorithms.  RenalytixAI intends to build a deep, unique pool of kidney disease-related data for different AI-enabled applications designed to improve predictive capability and clinical utility over time.  In 2019, RenalytixAI expects to launch KidneyIntelX, an artificial intelligence in-vitro diagnostic product intended to support physician decision-making by improving identification, prediction, and risk stratification of patients with progressive kidney disease.

On May 2, 2019, the Food and Drug Administration (FDA) granted “Breakthrough Device Designation” to KidneyIntelX.  KidneyIntelX is designed to diagnose and improve clinical management of patients with type II diabetes with fast-progressing kidney disease.  The diagnostic will use machine learning algorithms to evaluate the combination of predictive blood-based biomarkers, including sTNFR1, sTNFR2 and KIM1, in combination with electronic health record information, to identify progressive kidney disease.

Pena and co-workers (2015) identified a novel panel of biomarkers predicting renal function decline in type 2 diabetes mellitus (T2DM), using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy.  These researchers carried out a systematic data integration to select biomarkers representing different disease pathways.  A total of 28 biomarkers were measured in 82 patients seen at an out-patient diabetes center in the Netherlands.  Median follow-up was 4.0 years.  These investigators compared the cross-validated explained variation (R2) of 2 models to predict estimated glomerular filtration rate (eGFR) decline, one including only established risk markers, the other adding a novel panel of biomarkers.  Least absolute shrinkage and selection operator (LASSO) was used for model estimation.  The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as less than -3.0 ml/min/1.73 m2/year.  Patients' average age was 63.5 years and baseline eGFR was 77.9 ml/min/1.73 m2.  The average rate of eGFR decline was -2.0 ± 4.7 ml/min/1.73 m2/year.  When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7 % to 54.6 %; p = 0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p = 0.008).  The authors concluded that a novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in patients with T2DM.  However, these researchers stated that these findings need to be confirmed in a large prospective cohort to validate and assess its applicability in a broad T2DM population.

The authors stated that the main drawback of this study was the measurement of multiple biomarkers in a small sample size.  However, as advancing laboratory techniques generate larger amounts of data, methods of data analysis to accommodate “big data” with smaller sample sizes are needed.  The rigorous statistical method of the LASSO regression allowed for modeling many biomarkers in the small sample size, and multiple imputation was used to avoid truncating observations due to missing data.  The true predictive capacity of the model could have been over-estimated due to the prediction model being developed and tested in the same sample, and these researchers agreed that external validation is necessary.  In the absence of external validation, these investigators carried out internal boot-strap validation in an attempt to minimize this limitation; GFR was estimated using a serum creatinine-based equation instead of by direct measurement, which may have contributed to mis-classification bias.  However, this could have only resulted in an under-estimation of the strength of the reported associations.  These researchers chose to omit 5 biomarkers from their analysis due to many missing or below level of detection (LOD) values.  While the exclusion of these biomarkers from their analysis may have resulted in an under-representation of pathways, the omission of biomarkers could have only under-estimated the predictive ability of the biomarker panel.  The authors stated that additional drawbacks included the lack of information concerning insulin use, diet, and renin-angiotensin-aldosterone system medication type and dose, which clearly represent unmeasured confounders in this study.

Heinzel and colleagues (2018) stated that the decline of eGFR in patients with T2DM is variable, and early interventions would likely be cost-effective.  These researchers examined the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors.  They studied participants in PROVALID (PROspective cohort study in patients with T2DM for VALIDation of biomarkers), a prospective multi-national cohort study of patients with T2DM and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 ml/min/1.73 m2; urine albumin-to-creatinine ratio, 8.1 mg/g).  The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA.  The prediction of eGFR decline was evaluated by linear mixed modeling.  In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients.  A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62 %.  A panel of 12 biomarkers was selected by the procedure and accounted for 34 % of the total explained variability, of which 32 % was due to 5 markers (KIM1, FGF23, NTproBNP, HGF, and MMP1).  The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low.  When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79 %), and the contribution of each biomarker dropped below 1 %.  The authors concluded that in this longitudinal study of patients with T2DM and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low.  These researchers stated that given the inferior performance of this highly selected set of biomarkers in early-stage chronic kidney disease patients to predict future eGFR loss, these markers are not likely to be useful for clinical decision-making.

Norris and co-workers (2018) stated that albuminuria, elevated serum creatinine and low eGFR are pivotal indicators of kidney decline.  Yet, it is uncertain if these and emerging biomarkers such as uric acid represent independent predictors of kidney disease progression or subsequent outcomes among individuals with T2DM.  These researchers examined the available literature documenting the role of albuminuria, serum creatinine, eGFR, and uric acid in predicting kidney disease progression and cardio-renal outcomes in persons with T2DM.  Embase, Medline, and Cochrane Central Trials Register and Database of Systematic Reviews were searched for relevant studies from January 2000 through May 2016.  PubMed was searched from 2013 until May 2016 to retrieve studies not yet indexed in the other databases.  Observational cohort or non-randomized longitudinal studies relevant to albuminuria, serum creatinine, eGFR, uric acid and their association with kidney disease progression, non-fatal cardiovascular events, and all-cause mortality as outcomes in persons with T2DM, were eligible for inclusion.  Two reviewers screened citations to ensure studies met inclusion criteria.  From 2,249 citations screened, 81 studies were retained, of which 39 were omitted during the extraction phase (cross-sectional [n = 16]; no outcome/measure of interest [n = 13]; not T2DM specific [n = 7]; review article [n = 1]; editorial [n = 1]; not in English language [n = 1]).  Of the remaining 42 longitudinal study publications, biomarker measurements were diverse, with 7 different measures for eGFR and 5 different measures for albuminuria documented.  Kidney disease progression differed substantially across 31 publications, with GFR loss (n = 9 [29.0 %]) and doubling of serum creatinine (n = 5 [16.1 %]) the most frequently reported outcome measures.  Numerous publications presented risk estimates for albuminuria (n = 18), serum creatinine/eGFR (n = 13), or both combined (n = 6), with only 1 study reporting for uric acid.  Most often, these biomarkers were associated with a greater risk of experiencing clinical outcomes.  The authors concluded that despite the utility of albuminuria, serum creatinine, and eGFR as predictors of kidney disease progression, further efforts to harmonize biomarker measurements are needed given the disparate methodologies observed in this review.  Such efforts would help better establish the clinical significance of these and other biomarkers of renal function and cardio-renal outcomes in persons with T2DM.

Colombo and associates (2019) noted that as part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) program, these researchers previously reported that large panels of biomarkers derived from 3 analytical platforms maximized prediction of progression of renal decline in T2DM.  These investigators hypothesized that smaller (n less than or equal to 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in 3 additional T2DM cohorts.  These investigators used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS).  They analyzed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry.  The subset of 21 Luminex biomarkers was also measured on the CARDS samples.  They used the event definition of loss of greater than 20 % of baseline eGFR during follow-up from a baseline eGFR of 30 to 75 ml/min/1.73 m2.  A total of 403 individuals experienced an event during a median follow-up of 7 years.  These researchers used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function.  A total of 12 biomarkers showed significant association with eGFR decline adjusted for co-variates in 1 or more of the sample sets when evaluated singly.  Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardized odds ratios (ORs) for progression of at least 1.4 (p < 0.0003) in all cohorts.  A combination of B2M and KIM-1 added to clinical co-variates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the receiver operating characteristic curve (AUROC) in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively.  Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multi-platform panels previously identified, consistently improved prediction further across all validation sets.  The authors concluded that the combination of B2M and KIM-1, measured in serum, in addition to clinical co-variates, significantly improved prediction of renal function decline in T2DM on top of clinical data; use of a larger multi-platform biomarker panel did not consistently improve prediction further.

The authors stated that this study had several limitations.  Since there were differences in entry criteria and definition of caseness between their discovery cohort and the cohort sets studied here, they could not consider this strictly as a replication study.  The original biomarker panels were identified based on their power to predict a greater than or equal to 40 % decline in eGFR over a maximum follow-up of 3.5 years whereas in the current study these researchers looked at a decline of greater than or equal to 20 % over a longer follow-up period.  Thus, they were applying their biomarkers to a much less severe phenotype than previously.  Part of the rationale for this study was to examine the use of biomarkers for less extreme phenotypes.  These investigators expected that this might diminish associations between biomarkers and outcome.  However, they had confirmed that the biomarkers that predict more severe decline in renal function can also predict less severe decline and may be useful at earlier stages of kidney disease.  Since a 20 % drop in eGFR will be a noisier outcome measure than a 40 % drop, this meant that these researchers would have had less power to detect biomarker associations.  Nevertheless, it would not increase the level of false associations and their strict cross-validation techniques further protect against over-fitting.  In the GoDARTS and CARDS sample sets in this study the clinical co-variates were poor predictors compared with the original discovery case-control study and SDR cohort.  However, despite this, addition of the biomarkers increased the AUROC to a similar degree in the SDR and GoDARTS cohorts.  These investigators did not have the mass spectrometry biomarkers available in the CARDS samples.

Furthermore, an UpToDate review on “Diagnostic approach to the patient with newly identified chronic kidney disease” (Fatehi and Hsu, 2019) does not mention the measurements of biomarkers, KIM1, and sTNFR1, and sTNFR2 as a management tool.

In an observational, cohort study, Chan and colleagues (2021) developed/validated a machine-learned, prognostic risk score (KidneyIntelX) combining electronic health records (EHR) and biomarkers.  This study included patients with prevalent diabetic kidney disease (DKD)/banked plasma from 2 EHR-linked biobanks.  A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net re-classification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of greater than or equal to 5 ml/min per year, greater than or equal to 40 % sustained decline, or kidney failure within 5 years.  In 1,146 patients, the median age was 63 years, 51 % were women, the baseline eGFR was 54 ml/min/1.73 m2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21 % had the composite endpoint.  On cross-validation in derivation (n = 686), KidneyIntelX had an AUC of 0.77 (95 % confidence interval [CI]: 0.74 to 0.79).  In validation (n = 460), the AUC was 0.77 (95 % CI: 0.76 to 0.79).  By comparison, the AUC for the clinical model was 0.62 (95 % CI: 0.61 to 0.63) in derivation and 0.61 (95 % CI: 0.60 to 0.63) in validation.  Using derivation cut-offs, KidneyIntelX stratified 46 %, 37 % and 17 % of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively.  The PPV for progressive decline in kidney function in the high-risk group was 61 % for KidneyIntelX versus 40 % for the highest risk strata by KDIGO categorization (p < 0.001).  Only 10 % of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90 %).  The NRIevent for the high-risk group was 41 % (p < 0.05).  The authors concluded that KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.

The authors stated that this study had several drawbacks.  First, uACR was missing in 38 % of the cohort; however, this was representative of current state of care.  Moreover, the objective was to develop a risk score using real world data from EHR to predict where uACR is missing in a significant number of patients.  More widespread availability of uACR values would enhance the performance of KidneyIntelX, as it was a contributing feature in this model.  Moreover, even with this drawback, KidneyIntelX had a more robust performance than the KDIGO very high-risk stratum in the sub-population with uACR measurements.  Second, there was no protocolized follow-up resulting in missing data and lack of kidney biopsies.  Missing data could lead to biased machine learning models and the data were prone to ascertainment bias.  However, the median number of eGFR values per subject was 16, and the median time of follow-up was 4.3 years.  Although the primary biobanked cohorts used in the study were broadly representative of individuals with DKD in type 2 diabetes in terms of race/ethnicity and gender, these researchers could not rule out an inherent bias since the recruitment was opt-in recruitment from out-patient clinics and individuals who chose to participate in the cohorts from which the study population was selected may be different from those who did not participate in the primary cohorts.  Third, these investigators did not have information on subjects’ socioeconomic status or the duration of the diabetes diagnosis.  In the absence of biopsy, these researchers could not exclude the possibility that CKD may be due to other causes.  The test performance of KidneyIntelX (random forest algorithm) was higher than a logistic regression model that used the final top biomarker and clinical features that were selected by the random forest approach.  However, these investigators chose to employ the machine learning approach because random forests could integrate feature selection and modelling as well as efficiently model potential non-linear interactions between features.  Fourth, both cohorts were from Northeast U.S. and an independent validation cohort is needed to ensure generalizability; however, only 1/3 of the subjects were white, so there was adequate representation of racial groups that experienced disparities for kidney disease.

Transdermal System with the Use of Pyrazine‐Based Fluorescent Agents for Measurement of Glomerular Filtration Rate

Transdermal glomerular filtration rate (GFR) measurement system entails placement of sensors and administration of a 1 or more dose(s) of a pyrazine-based fluorescent agent.  Sensors are usually placed at 2 locations on subjects’ skin, which will remain for 48 hours.  Subjects may undergo activities of daily living while measurements are being continuously collected.  However, there is currently insufficient evidence regarding the effectiveness of transdermal systems with the use of pyrazine‐based fluorescent agents for measurement of GFR.

Rajagopalan and colleagues (2011) examined various hydrophilic pyrazine-bis(carboxamides) derived from 3,5-diamino-pyrazine-2,5-dicarboxylic acid bearing neutral and anionic groups for use as fluorescent glomerular filtration rate (GFR) tracer agents.  Among these, the di-anionic d-serine pyrazine derivatives 2d and 2j, and the neutral dihydroxypropyl 2h, exhibited favorable physicochemical and clearance properties.  In-vitro studies show that 2d, 2h, and 2j have low plasma protein binding, a necessary condition for renal excretion.  In-vivo animal model results showed that these 3 compounds exhibited a plasma clearance equivalent to iothalamate (a commonly considered gold standard GFR agent).  In addition, these compounds had a higher urine recovery compared to iothalamate.  Finally, the plasma clearance of 2d, 2h, and 2j remained unchanged upon blockage of the tubular secretion pathway with probenecid, a necessary condition for establishment of clearance via glomerular filtration only.  Hence, 2d, 2h, and 2j are promising candidates for translation to the clinic as exogenous fluorescent tracer agents in real-time point-of-care (POC) monitoring of GFR.

Huang and Gretz (2017) noted that the non-invasive assessment of kidney function and diagnosis of kidney disease have long been challenges.  Traditional methods are not routinely available, because the existing protocols are cumbersome, time consuming, and invasive.  In the past several years, significant progress in the field of diagnosing kidney function and disease on the basis of light‐emitting agents has been made.  These researchers reviewed light‐emitting agents, including organic fluorescent agents and inorganic renal clearable luminescent nanoparticles for the non-invasive and real‐time monitoring of kidney function and disease.  They developed a non-invasive transcutaneous technique to measure glomerular filtration rate (GFR) on the basis of a miniaturized electronic device attached to the skin.  The smart transcutaneous device comprises light‐emitting diodes that excite a fluorescent agent and a photodiode that detects the emission signal of the injected fluorescent agent.  The device is attached to the skin of an animal before the fluorescent agent is injected so that a baseline can be recorded for a short time-period.  The device enables the fluorescent agent to be excited repeatedly within the interstitial space by blinking each second at the appropriate wavelength.  After each flash, the fluorescence emission of the fluorescent agent is detected and converted into a digital signal.  These digital data are stored in an internal memory within the device.  The authors stated that although GFR is considered the best indicator for overall renal function, further efforts should be made towards the development of novel light‐emitting agents for the detection of region‐specific injury in kidneys (e.g., tubular necrosis and function) so that kidney diseases can be differentiated and that kidney injury can be diagnosed at an early stage.

Debreczeny and Dorshow (2018) developed a prototype medical device for monitoring renal function by transdermal measurement of the clearance rate of the exogenous fluorescent tracer agent MB-102 (administered intravenously).  Verification of the device with an in-vitro protocol was described.  The expected renal clearance of the agent was mimicked by preparing a dilution series of MB-102 in the presence of a scattering agent.  The slope of a linear fit to the logarithm of fluorescence intensity as a function of dilution step agreed with predictions within 5 %, a level of accuracy that would be adequate in assessment of GFR to prevent mis-diagnosis of renal disease.  Transdermal measurement was validated using a rat model.  A 2-compartment pharmacokinetic dependence was observed, with equilibration of the fluorescent agent between the vascular space into which it was injected and the extracellular space into which it subsequently diffused.  The best observed signal-to-noise ratios were about 150, allowing determination of the renal clearance time with 5 % precision using a 10-min fitting window.  Based on the verification and validation methods for transdermal fluorescence detection described, the device performance was sufficient to proceed to human trials.  This device has subsequently been used in a FDA-approved pilot human clinical study on 16 healthy subjects.  The aim of these studies is to establish whether the measurement accuracy and precision is sufficient to warrant proceeding with further studies on patients with renal disease, in which transdermal measurements of GFR will be compared with GFR determined by standard plasma pharmacokinetic analysis.  The end goal of the commercial instrument is to provide bedside assessment in near real-time assessment of patient kidney function..

Shieh et al (2020) noted that MB-102 is a fluorescent tracer agent designed for measurement of POC GFR and is currently in clinical studies.  MB-102 possesses a strong UV absorbance at 266-nm and 435-nm, and broad fluorescent emission at approximately 560 nm when excited at approximately 440 nm.  The MB-102 formulation is stable at 2°C to 8°C for more  than 3 years.  The pKa's of the 2 acid groups are 2.71 and 3.40.  Both X-ray crystallography and HPLC confirmed the D, D chirality of MB-102 in solid, in solution, and in the drug formulation.  Initial safety and toxicity was published previously, which enabled the commencement of clinical studies.  In-vitro studies showed that 4.1 % of MB-102 is bound to human plasma proteins, compared to 6.0 % for the accepted standard GFR agent iohexol.  The blood-to-plasma ratio for MB-102 was 0.590, illustrating minimal distribution of MB-102 into red blood cells.  The manufacture of MB-102 under good manufacturing practice yields the designed molecular structure at high purity (greater than 95 % wt/wt).  The authors concluded that from the analytical results reported, MB-102 has the necessary properties for use as a tracer agent for GFR determination.  Its fluorescence property coupled with transdermal detection following bolus intravenous administration yielded the first true measurement of GFR in real-time and at the POC.

A clinical trial entitled “Tolerability and Background Fluorescence of the MediBeacon Transdermal GFR Measurement System” has been completed (last updated January 10, 2020).  However, the findings have not been published.  The MediBeacon Transdermal GFR Measurement System is intended to measure the GFR in patients with normal or impaired renal function by non-invasively monitoring fluorescent light emission from an exogenous tracer agent (MB-102) over time.  The device utilized in this study is the Brilliance device.

Furthermore, an UpToDate review on “Assessment of kidney function” (Inker and Perrone, 2020) does not mention transdermal system or fluorescent agent as management tools.

The GFRNMR Test for Assessment of Glomerular Filtration Rate/Kidney Function

The GFRNMR is a serum-based test that uses multiple metabolites including myo-inositol, dimethyl sulfone, valine, and creatinine and analyzed by nuclear magnetic resonance spectroscopy; it is used to for evaluation of glomerular filtration rate (GFR)/kidney function.

Coresh and associates (2019) noted that estimation of GFR using estimated GFR creatinine (eGFRcr) is central to clinical practice but has limitations.  These researchers tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates.  They carried out a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR).  Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites.  On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors of greater than 30 % of mGFR (1-P30), before and after bias correction.  Of untargeted metabolites in the AASK and MESA, 283 of 780 (36% ) and 387 of 1,447 (27 %), respectively, were significantly correlated (p ≤ 0.001) with mGFR.  A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9 %, respectively) than eGFRcr [11.2 % and 18.5 %, respectively (p < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6 % (p = 0.02) and 9.1 % (p < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7 % (p > 0.05) and 9.1 % (p < 0.05), respectively].  A panel excluding creatinine and demographics still performed well [1-P30 6.4 % (p = 0.11) and 3.4 % (p < 0.001) in the AASK and MESA] versus eGFRcr.  The authors concluded that the algorithms presented in this study provided a proof-of-concept (POC) in realizing the potential of translating untargeted metabolomic screening to algorithms.  Given the known limitations of serum creatinine and widespread use of GFR estimation, the clinical implications that a panel of metabolites could provide an accurate estimate of GFR with or without serum creatinine or demographics could be substantial if these initial results can be taken through the full diagnostic test development process.  These researchers stated that testing in multiple populations should be carried out to confirm the external generalizability of a metabolite panel.  More importantly, the final robust algorithm that could be used to estimate GFR would ideally be developed in a more diverse dataset.

The authors stated that this study had several drawbacks.  The AASK was a study of African Americans with hypertensive kidney disease and as such is a rather homogeneous population with respect to race, geographical location and diet (U.S. based) and cause of kidney disease.  These investigators were able to replicate the findings in U.S. whites and blacks with and without kidney disease and thus knew that these findings were not due to black ethnicity; however, the relative homogeneity of the samples did not allow these researchers to test the generalizability of the findings.  Sample handling in the AASK did not follow a standardized protocol and the storage period was many years.  As a result, some metabolites may have been missed, but those that were identified were likely to be robust to a range of handling techniques and long-term storage.  The identity of some of the most strongly correlated metabolites with mGFR was unknown, limiting the current panel but providing an opportunity for further improvement on this POC.  GFR measurement is known to be imprecise, but this inflated the reported GFR estimation errors.  Furthermore, iothalamate and iohexol GFR measurement methods differ systematically and standardization of GFR for body surface area may not optimally deal with variation in body composition.  More importantly, the performance and practicality of combining metabolites with low molecular weight proteins such as cystatin C was not tested.  However, the focus on metabolites measured in a multiplex panel has the potential for economies of scale.  These investigators noted that future steps should include evaluation of panels including cystatin C and potentially other low molecular weight proteins.  A better understanding of the metabolism of all components of the panel used to estimate GFR will be useful to better predict when they are influenced by non-GFR determinants.  The magnitude of such influences on the overall GFR estimate across a range of clinical settings needs to be quantified, especially in clinical settings where creatinine and cystatin are known to be unreliable.

Ehrich and colleagues (2021) stated that evaluation of renal dysfunction includes eGFR as the initial step and subsequent laboratory testing.  In a POC study, these researchers hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation.  Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR).  The metabolites were combined into a metabolite constellation (GFRNMR) using n = 95 training samples and tested in n = 189 independent samples.  Tracer-mGFR served as a reference.  GFRNMR was compared to eGFR based on serum creatinine (eGFRCrea and eGFREKFC), cystatin C (eGFRCys-C), and their combination (eGFRCrea-Cys-C) when available.  The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology.  GFRNMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 versus 0.79 and 0.80).  Overall percentages of eGFR values within 30 % of mGFR for GFRNMR matched or exceeded those for eGFRCrea and eGFREKFC (81 % versus 64 % and 74 %), eGFRCys-C (81 % versus 72 %), and eGFRCrea-Cys-C (81 % versus 81 %).  The authors developed and tested a metabolite-based serum test for accurate estimation of GFR in pediatric, adult, and geriatric patients, obviating the need for invasive tracer application and bearing the potential of metabolic phenotyping of patients with chronic kidney disease (CKD).

These researchers stated that their concept is associated with several weaknesses.  First, the total number of patient samples of both the training and test cohort would certainly benefit from additional samples.  Besides increasing statistical power, validation of the concept in further cohorts, including African-American and Asian ethnic groups, as well as patients with, e.g., type 2 diabetes mellitus under metformin treatment, nephrotic syndrome, or various tubulopathies, would allow a comprehensive evaluation of the potential clinical utility of the method.  Second, the training cohort consisted of a sample set with a heterogeneous reference standard with a mixture of inulin, 51Cr-EDTA, or iohexol renal clearances.  As even inulin clearance is associated with a coefficient variation of 7 % for repeated measurements, imprecision might increase even more when renal clearances of 51Cr-EDTA or iothalamate and plasma clearances of 51Cr-EDTA or iohexolare applied for measuring GFR.  Thus, the errors of inulin and other exogenous clearance markers are often under-estimated when they are used as referenced standards for establishing new eGFR equations.  Although these investigators could not determine any dependency of the GFRNMR results from the applied reference method in post-hoc analysis, they could not exclude the possibility of a reference or selection bias.  Third, the results obtained for eGFR equations considering cystatin C might have been influenced by both the prolonged storage times of the bio-banked samples and the use of different ELISA assays for cystatin C quantification.  Although sample storage was at −80 °C and the applied assays were calibrated to standard reference material, future work should consider an optimized design.  Finally, these researchers established the method on serum samples of at least a 630-µl volume, and its transferability to lower volumes or blood plasma could not be considered as simply given.  However, this may be less a limitation on its ability to perform in clinical routine than its application in clinical research with bio-banked serum samples.

Schultheiss and co-workers (2021) noted that kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high co-morbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care.  Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research.  Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge regarding the key work-flow steps: study planning, sample collection, metabolomics data acquisition and pre-processing, statistical/bioinformatics data analysis, as well as results interpretation within a biomedical context.  The authors concluded that the field of metabolomics already has been of unmeasurable value for nephrology research; however, many questions remain and need to be addressed in the future.  A first issue will be to understand the differing metabolite patterns across the diverse spectrum of kidney diseases, such as metabolic syndrome/diabetes mellitus, glomerular diseases, and many others; however, within similar phenotypic CKD etiologies, metabolomics also will aid in unraveling the mechanisms that differentiate, e.g., slow from fast CKD progressors.  Translation of metabolomics research into routine CKD patient care will pave the way for novel metabolic biomarkers to examine and monitor the safety and efficacy of treatments.  Therefore, metabolomics studies will support clinical decision-making.  Finally, metabolomics will become an integrated part of CKD diagnostics and will be able to inform the treating physicians on the rate of CKD progression, adverse risk evaluation, and other CKD-related co-morbidities, such as the stage of metabolic syndrome versus diabetes mellitus or others; thus, metabolomics will be a pioneering field for individualized patient treatment.

An UpToDate review on “IgA vasculitis (Henoch-Schonlein purpura): Kidney manifestations” (Niaudet et al, 2021) states that “Metabolomic profiling has identified putative biomarkers that may predict the development of kidney disease among patients with IgAV who do not present with kidney involvement; however, additional studies validating these findings in larger populations are needed”.

Furthermore, an UpToDate review on “Investigational biomarkers and the evaluation of acute kidney injury” (Erdbruegger and Okusa, 2021) states that “Metabolomics is the study of small-molecule metabolites that are produced by the body and provide insight into physiological and pathophysiological conditions.  Metabolomic analysis can be readily performed in biofluids such as blood and urine, and, because there are fewer metabolites than there are genes, mRNA, and proteins, analyses are simpler.  This method may allow for identification of new markers in AKI”.

Table: CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

There are no specific codes for RenalVysion:

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

I10 - I16.2 Hypertensive diseases
N18.1 - N18.9 Chronic kidney disease (CKD)
N19 Unspecified kidney failure
N26.2 Page kidney
R10.0 - R10.13
R10.30 - R10.33
R10.84
Abdominal pain
R31.0 Gross hematuria
R60.0 - R60.9 Edema, not elsewhere classified

KidneyIntelX:

CPT codes not covered for indications listed in the CPB:

0105U Nephrology (chronic kidney disease), multiplex electrochemiluminescent immunoassay (ECLIA) of tumor necrosis factor receptor 1A, receptor superfamily 2 (TNFR1, TNFR2), and kidney injury molecule-1 (KIM-1) combined with longitudinal clinical data, including APOL1 genotype if available, and plasma (isolated fresh or frozen), algorithm reported as probability score for rapid kidney function decline (RKFD)

ICD-10 codes not covered for indications listed in the CPB:

E11.00 - E11.9 Type II diabetes

Transdermal Fluorescent Pyrazine:

CPT codes not covered for indications listed in the CPB:

0602T Glomerular filtration rate (GFR) measurement(s), transdermal, including sensor placement and administration of a single dose of fluorescent pyrazine agent
0603T Glomerular filtration rate (GFR) monitoring, transdermal, including sensor placement and administration of more than one dose of fluorescent pyrazine agent, each 24 hours

ICD-10 codes not covered for indications listed in the CPB:

N17.0 Acute kidney failure with tubular necrosis
N18.1 - N18.9 Chronic kidney disease (CKD)
N19 Unspecified kidney failure
R94.4 Abnormal results of kidney function studies

GFRNMR Test:

CPT codes not covered for indications listed in the CPB:

0259U Nephrology (chronic kidney disease), nuclear magnetic resonance spectroscopy measurement of myo-inositol, valine, and creatinine, algorithmically combined with cystatin C (by immunoassay) and demographic data to determine estimated glomerular filtration rate (GFR), serum, quantitative

ICD-10 codes not covered for indications listed in the CPB:

E11.21-E11.29 Type 2 diabetes mellitus with kidney complications
N18.1- N18.9 Chronic kidney disease (CKD)

The above policy is based on the following references:

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  3. Colombo M, Looker HC, Farran B, et al; SUMMIT Investigators. Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes. Diabetologia. 2019;62(1):156-168.
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  18. National Institute for Clinical Excellence (NICE). Chronic kidney disease: Early identification and management of chronic kidney disease in adults in primary and secondary care. NICE clinical guideline 73. London, UK: NICE; September 2008.
  19. National Institutes of Health (NIH), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Kidney Disease Education Program. Chronic kidney disease (CKD). Information for health professionals. Bethesda, MD: NIH; December 28, 2005. 
  20. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. New York, NY: National Kidney Foundation; 2002.
  21. Nephrocor. RenalVysion [website]. Glen Allen, VA; Nephrocor; 2008. Available at: http://www.nephrocor.com/Global/services/laboratory-services/renalvysion.aspx. Accessed October 31, 2008.
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  23. Norris KC, Smoyer KE, Rolland C, et al. Albuminuria, serum creatinine, and estimated glomerular filtration rate as predictors of cardio-renal outcomes in patients with type 2 diabetes mellitus and kidney disease: A systematic literature review. BMC Nephrol. 2018;19(1):36.
  24. Pena MJ, Heinzel A, Heinze G, et al. A panel of novel biomarkers representing different disease pathways improves prediction of renal function decline in type 2 diabetes. PLoS One. 2015;10(5):e0120995.
  25. Post TW, Rose BD. Urinalysis in the diagnosis of renal disease. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
  26. Rajagopalan R, Neumann WL, Poreddy AR, et al. Hydrophilic pyrazine dyes as exogenous fluorescent tracer agents for real-time point-of-care measurement of glomerular filtration rate. J Med Chem. 2011;54(14):5048-5058.
  27. RenalytixAI. FDA grants Breakthrough Device Designation to KidneyIntelX. Press Release. New York, NY: RenalytixAI; May 2, 2019. 
  28. Schultheiss UT , Kosch R, Kotsis F, et al. Chronic kidney disease cohort studies: A guide to metabolome analyses. Metabolites. 2021;11(7):460.
  29. Scottish Intercollegiate Guidelines Network (SIGN). Diagnosis and management of chronic kidney disease. A national clinical guideline. Edinburgh, Scotland: Scottish Intercollegiate Guidelines Network (SIGN); 2008.
  30. Shieh J-J, Riley IR, Rogers TE, et al. Characterization of MB-102, a new fluorescent tracer agent for point-of-care renal function monitoring. J Pharm Sci. 2020;109(2):1191-1198.
  31. Stevens L, Perrone RD. Assessment of kidney function: Serum creatinine; BUN; and GFR. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
  32. Whittier WL, Korbet SM. Indications for and complications of renal biopsy. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.