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.

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.

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

The above policy is based on the following references:

  1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-2047.
  2. Post TW, Rose BD. Urinalysis in the diagnosis of renal disease. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
  3. Stevens L, Perrone RD. Assessment of kidney function: Serum creatinine; BUN; and GFR. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
  4. Whittier WL, Korbet SM. Indications for and complications of renal biopsy. UpToDate [online serial]. Waltham, MA: UpToDate; 2008.
  5. Nephrocor. RenalVysion [website]. Glen Allen, VA; Nephrocor; 2008. Available at: http://www.nephrocor.com/Global/services/laboratory-services/renalvysion.aspx. Accessed October 31, 2008.
  6. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. National Kidney Foundation. New York, NY; 2002.
  7. 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. 
  8. 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.
  9. Scottish Intercollegiate Guidelines Network (SIGN). Diagnosis and management of chronic kidney disease. A national clinical guideline. Edinburgh, Scotland: Scottish Intercollegiate Guidelines Network (SIGN); 2008.
  10. Joint Specialty Committee on Renal Medicine of the Royal College of Physicians and The Renal Association, and the Royal College of General Practitioners. Identification,management and referral of adults with chronic kidney disease. Guidelines for general physicians and general practitioners. Concise guidance to good practice, No 5. London: RCP, 2006.
  11. Kidney Health Australia. Chronic kidney disease (CKD) management in general practice. Kidney Health Australia, Melbourne, VIC. 2007.
  12. Eknoyan G, Hostetter T, Bakris GL, et al. Proteinuria and other markers of chronic kidney disease: A position statement of the National Kidney Foundation (NKF) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Am J Kidney Dis. 2003;42(4):617-622.
  13. Levey AS, Coresh J, Balk E, et al; National Kidney Foundation. National Kidney Foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann Intern Med. 2003;139(2):137-147.
  14. Canadian Agency for Drugs and Technologies in Health (CADTH). Liquid biopsy for the detection of renal disease. Emerging Issues in Diagnostic Technology. Health Technology Update. Issue 12. Ottawa, ON: CADTH; November 2009.
  15. 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.
  16. Heinzel A, Kammer M, Mayer G, et al. Validation of plasma biomarker candidates for the prediction of eGFR decline in patients with type 2 diabetes. Diabetes Care. 2018;41(9):1947-1954.
  17. 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.
  18. 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.
  19. RenalytixAI. FDA grants Breakthrough Device Designation to KidneyIntelX. Press Release. New York, NY: RenalytixAI; May 2, 2019. 
  20. Fatehi P, Hsu C-Y. Diagnostic approach to the patient with newly identified chronic kidney disease. UpToDate [online serial]. Waltham, MA: UpToDate; reviewed August 2019.