PROPOSED Local Coverage Determination (LCD)

KidneyIntelX and KidneyIntelX.dkd Testing

DL39726

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DL39726
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Proposed LCD Title
KidneyIntelX and KidneyIntelX.dkd Testing
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Issue

Issue Description

Kidney disease is a public health epidemic affecting over 850 million people globally, and is one of the most common causes of premature death worldwide. The Centers for Disease Control and Prevention estimates that 15% of U.S. adults, or over 37 million people in 2018, suffered from chronic kidney disease (CKD). Nearly 95% of people with CKD suffer from early-stage CKD (i.e., CKD stages 1-3). Early­ stage CKD is underdiagnosed and undertreated, largely because it is asymptomatic at this time in the disease's progression.

Issue - Explanation of Change Between Proposed LCD and Final LCD

CMS National Coverage Policy

Internet Only Manual (IOM) Citations:

  • CMS IOM Publication 100-04, Medicare Claims Processing Manual,
    • Chapter 1, Section 60 Provider Billing of Non-covered Charges on Institutional Claims
    • Chapter 16, Laboratory Services
    • Chapter 23, Section 20.9 National Correct Coding Initiative (NCCI), Section 20.9.1.1 Instructions for Codes with Modifiers (A/B MACs (B) Only), and Section 40 Clinical Diagnostic Laboratory Fee Schedule
  • CMS IOM Publication 100-08, Medicare Program Integrity Manual,
    • Chapter 3, Sections 3.4.1.3 Diagnosis Code Requirements and 3.6.2.3 Limitations of Liability Determinations

National Correct Coding Initiative (NCCI) Citation:

  • NCCI Policy Manual for Medicare Services,
    • Chapter 10, Pathology/Laboratory Services, (A) Introduction and (F) Molecular Pathology

Social Security Act (Title XVIII) Standard References:

  • Title XVIII of the Social Security Act, Section 1833(e) states that no payment shall be made to any provider of services or other person under this part unless there has been furnished such information as may be necessary in order to determine the amounts due such provider or other person under this part for the period with respect to which the amounts are being paid or for any prior period.
  • Title XVIII of the Social Security Act, Section 1834A(d) This section addresses payment for new advanced diagnostic laboratory tests.

Code of Federal Register (CFR) References:

  • CFR, Title 42, Volume 2, Chapter IV, Part 410.32(d)(3) Diagnostic x-ray tests, diagnostic laboratory tests, and other diagnostic tests: Conditions.
  • CFR, Title 42, Volume 3, Chapter IV, Part 414, Subpart G Payment for Clinical Diagnostic Laboratory Tests.
  • CFR, Title 42, Volume 3, Chapter IV, Part 414.50 Physician or other supplier billing for diagnostic tests performed or interpreted by a physician who does not share a practice with the billing physician or other supplier.

 

Coverage Guidance

Coverage Indications, Limitations, and/or Medical Necessity

Indications of Coverage

Once in a lifetime KidneylntelX or KidneyIntelX.dkd test is considered reasonable and necessary when all the following criteria are met:

  • The results are used to facilitate therapeutic prognostic decision-making in the medical management of a selected patient population, and
  • The results are used to assess the risk of progressive decline in kidney function in patients over the age of 21 years of age with:
    • Type 2 diabetes (T2D) and existing early-stage chronic kidney disease (CKD) (stages 1-3b), and
    • The test is ordered by the treating physician or qualified non-physician practitioner, and
    • The test is performed in a CLIA certified laboratory qualified to perform high complexity testing, and
    • The specific reason for the test must be documented by the treating practitioner in the medical documentation and demonstrate that the test is medically reasonable and necessary.

Note: Kidney function decline is defined as:

  • a decline in eGFR slope of ≥: 5 ml/min/l.73m 2 /year; or
  • a sustained decrease in eGFR ≥40% confirmed at least 3 months apart; or
  • kidney failure, defined by sustained eGFR <15.

Limitations of Coverage

  • KidneylntelX or KidneyIntelX.dkd test is not medically reasonable and necessary for:
    • Patients with eGFR <30
    • Patients with eGFR ≥60 ml/min/l.73m 2 without albuminuria
    • Patients with ESRD or on renal recovery treatments
    • Patients who are pregnant
    • Patients who are currently hospitalized
    • Patients taking Etanercept
  • KidneylntelX or KidneyIntelX.dkd is not covered as a screening or standalone diagnostic.
Summary of Evidence

Tokita et al- conducted a prospective data collection study which enrolled 1686 patients in a large metro health system over 16 months to assess the impact of the KidneyIntelX test result on clinical decision-making and outcomes. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. Determination of a new referral to a specialty consult service (i.e., nephrology, endocrinology, nutrition), any new prescriptions, or modification to any existing prescription medication for ACEi/ARB, SLGT2i, or GLP-1 agonists was based on a 6-month pre-baseline to 6-month post-test assessment. Limitations included patient compliance with filling prescriptions were not available. 53% of all KidneyIntelX high risk patients had a follow-up within a month while standard of care for follow-up is every 12 months.1 The authors found that 53% of all KidneyIntelX high-risk patients had a follow-up visit within 1 month and 57% had action taken (medication change or referral) within 3 months compared to 13% and 35%, respectively, for low-risk individuals. Traditionally, the standard-of-care for follow-up visit frequency is every 12 months. Thus, these results reflect a needed change in management for high-risk patients regarding visit frequency and any action taken. When evaluating new or modified prescriptions for antihypertensive at 6-months, both ACEi and ARBs achieved a greater than 20% change in the high-risk group (ACEi, OR = 1.36; 95% CI: 0.77-2.30; ARBs, OR = 1.65; 95% CI: 1.01-2.63). Early evidence suggests that the introduction of the SGLT2i lowered HbA1c levels most notably in the high-risk category (median 8.2% HbA1c at 6 months pre KidneyIntelX vs 7.45% post-test. In conclusion, the authors found that patients with early-stage DKD who were identified as high-risk via the KidneyIntelX score received earlier follow-up visits, necessary change in medications or specialist referral compared to those who were identified as low- or intermediate-risk patients. Specifically, high-risk patients were more likely to be referred to a nephrologist and by 6 months, these patients had a significant increase in anti-hypertension medications compared to those of intermediate- and low-risk who were more likely to receive standard of care.

Nadkarni et al- The authors conducted a post hoc analysis, assessing the association of KidneyIntelX at baseline with the time-to-event composite end point of 57% decline in eGFR or adjudicated ESKD, HHF, or death. The authors studied 1278 participants in the CANagliflozin Cardiovascular Assessment Study (CANVAS) trial as they hypothesized that KidneyIntelX would also risk stratify patients with prevalent DKD for a clinically relevant kidney outcome, HHF, and all-cause mortality. KidneyIntelX was evaluated in the subgroup of the CANVAS population that met the criteria for prevalent DKD (eGFR ≥30–59.9 ml/min per 1.73 m2 [G3a and G3b] or those with an eGFR ≥60 ml/min per 1.73 m2 with a urine albumin-creatinine ratio [uACR] ≥30 mg/g) at the time of enrollment with existing bio banked blood samples. Measurements were obtained of soluble TNF receptors (sTNFR) 1 and 2, and kidney injury molecule-1 (KIM-1) via proprietary assays, and calculated KidneyIntelX scores using the existing validated algorithm. Among the 1278 CANVAS participants in this post hoc analysis, the mean age was 64 years, 32% were women, the mean baseline eGFR was 65 ml/min per 1.73 m2, the median uACR was 56 mg/g, 498 (40%) had an eGFR< 60 ml/min per 1.73 m2, and 209 (16%) had heart failure at baseline. During a mean of 5.6 years follow-up, 282 (22%) experienced the composite outcome, 41 (3%) developed a 57% decline in eGFR or ESKD, 78 (6%) were hospitalized for heart failure, and 209 (16%) died. The risk for the composite event was reduced by 22%–24% across all risk strata in participants randomized to canagliflozin versus placebo, with absolute risk reductions of 11% in the high-risk stratum, 6% in the intermediate-risk stratum, and 4% in the low-risk stratum (P<0.01 for high versus low risk). Although KidneyIntelX has been validated for an outcome of DKD progression, the results from this subsequent post hoc analysis from CANVAS demonstrated that KidneyIntelX robustly stratified patients for a composite end point consisting of clinically relevant outcomes. In conclusion, the authors found that KidneyIntelX, a composite risk score trained and validated for a kidney-specific outcome, provided risk stratification for a triple composite end point that included not only the kidney-specific outcome of progression, but also clinically relevant outcomes of hospitalizations for heart failure and all-cause mortality, even after adjusting for several other risk factors for these outcomes. 2

Lam et al- The authors measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from 1325 CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2 or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. The mean age of the full study population was 64 years, where 32% were female, the mean eGFR was 65 mL/min/1.73 m2, and the median UACR was 56 mg/g. Overall, stratified by the baseline KidneyIntelX score and adjusted for the treatment arm, each 10% reduction in KidneyIntelX risk was associated with a 20% lower risk of experiencing the composite kidney outcome (adjusted odds ratio per 10% reduction of 0.80 [95% CI: 0.77, 0.83]; p < 0.001). In conclusion, the authors found KidneyIntelX successfully risk-stratified a large multinational external cohort for risk of progression of DKD, with larger differences in the eGFR slope for canagliflozin versus placebo in those with higher versus lower baseline KidneyIntelX scores.3 The authors found the effects of the SGLT2i canagliflozin on the chronic eGFR slope were numerically greater in magnitude in participants who scored as high risk by KidneyIntelX at enrollment. Second, canagliflozin decreased KidneyIntelX risk scores over time compared to an increase in the placebo, and this improvement in prognosis was maintained over the follow-up period.

Chauhan et al- studied 1369 patients that were selected from a biobank at an institutional review board–approved biorepository that includes consented access to the patients’ EHR from NYC. The authors selected two cohorts from the biobank: (1) T2D, enrollment eGFR 45–90 ml/min, and ≥3 years of follow-up data (n=871); and (2) APOL1-HR with African ancestry, enrollment eGFR >30 ml/min and ≥3 years of follow-up data (n=498). The authors measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. Performance was compared to a validated clinical model and thresholds applied to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories. The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (P<0.01) in high-risk (top 15%) stratum for T2D and APOL1-HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1-HR versus 85% and 93% for the clinical model, respectively (P=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%). 4

Liu et al- completed a database literature search to capture studies evaluating the associations between single or multiple kidney biomarkers and any of the following CKD outcomes: incident CKD, CKD progression, or incident ESKD (e.g., initiation of chronic hemodialysis, peritoneal dialysis requirement, or transplant). 129 studies were included in the meta-analysis for the most frequently studied plasma biomarkers (TNFR1, FGF23, TNFR2, KIM-1, suPAR, and others) and urine biomarkers (KIM-1, NGAL, and others). The authors found that studies of preclinical biomarkers for CKD outcomes have considerable heterogeneity across study cohorts and designs, limiting comparisons of prognostic performance across studies. Plasma TNFR1, FGF23, TNFR2, KIM-1, and suPAR were among the most frequently investigated in the setting of CKD outcomes. 5

Connolly et al- this study was based on Clinical Laboratory Standards Institute (CLSI) guidelines, analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2). Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. The sensitivity, reproducibility, and linearity of the assay for the simultaneous measurements of KIM-1, sTNFR-1 and sTNFR-2 in human plasma are integral to assuring robust and consistent results for each analyte. Additionally, demonstrating reproducibility of the risk score and disease risk categorization is key to confirming that inherent variation does not impact reported clinical results of the test. The authors found that the assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. The authors found that the set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. 6

Chan et al- This study sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. The authors performed an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. The study has 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol. The authors found KidneyIntelX scores correctly classified more cases into the appropriate risk strata (NRI event = 55% in the derivation set and 41% in the validation set, p < 0.05; ESM Table 5) than the KDIGO risk strata did. NRI non-event was −8.2% in the derivation set and − 7.9% in the validation set.7

Datar et al- This study was a qualitative analysis based on 30–45-min interviews with 16 primary care physicians (PCP) treating Type 2 diabetic (T2D) patients. The interviews found testing for kidney disease was not consistently top of mind, with 56% reportedly performing kidney function testing in their T2D patients. PCPs most frequently reported using estimated glomerular filtration rate (eGFR) alone to monitor and stage DKD; only 25% PCPs reported testing for albuminuria. The authors felt this study showcased the important unmet needs in T2D DKD testing, staging, and stratification in the PCP setting that limit effective patient care. 8

Datar et al- This study was a prospective web-based survey administered among 401 PCPs in the United States to assess the decision-making impact of an artificial intelligence–enabled prognostic test, KidneyIntelX, in the management of DKD by primary care physicians (PCPs). The survey included hypothetical patient profiles with 6 attributes: albuminuria, eGFR, age, blood pressure (BP), hemoglobin A1c (HbA1c), and KidneyIntelX result. For each patient, PCPs were asked to indicate whether they would prescribe a sodium-glucose cotransporter-2 (SGLT2) inhibitor, increase angiotensin receptor blocker (ARB) dose, and/or refer to a nephrologist. The authors found the relative importance of the top 2 attributes for each decision were HbA1c (52%) and KidneyIntelX result (23%) for prescribing SGLT2 inhibitors, BP (62%) and KidneyIntelX result (13%) for increasing ARB dose, and eGFR (42%) and KidneyIntelX result (27%) for nephrologist referral. The authors concluded KidneyIntelX test had greater relative importance than albuminuria and eGFR to PCPs in making treatment decisions and was second only to eGFR for nephrologist referrals.9

Analysis of Evidence (Rationale for Determination)

The current evidence concerning KidneyIntelX or KidneyIntelX.dkd as a test to identify and stratify patients with T2D and early-stage CKD into low, intermediate, and high risk for near-term rapid progressive decline in kidney function, suggests that the early identification of high-risk patients by the test allows for more intensive patient management, selection of appropriate medications, and appropriate specialty referral or consultation. Also, the clinical principles, that more proactive care leads to better health outcomes and improved quality of life for patients, including slowed disease progression, avoidance or delay of kidney failure and need for hemodialysis, were supported by our CAC Subject Matter Experts.

Health disparities in patients with chronic kidney disease (CKD) and diabetes are significant and multifaceted. Studies consistently reveal disproportionate prevalence among minority populations, emphasizing the intersectionality of factors like race, socioeconomic status, and access to healthcare. These disparities manifest in higher rates of CKD and diabetes, delayed diagnosis, and increased complications. Studies have found that kidney disease disproportionately affects communities of color. Black or African Americans are almost four times more likely and Hispanics or Latinos are 1.3 times more likely to have kidney failure compared to White Americans. Although they make up only 13.5% of the population, Black or African Americans make up more than 35% of dialysis patients.10

Proposed Process Information

Synopsis of Changes
Changes Fields Changed
Not Applicable N/A
Associated Information
N/A
Sources of Information

The CAC Meeting transcript, August 24, 2023, is available below:

CAC Meeting -KidneyIntelX and KidneyIntelX.dkd

 

 

Bibliography

1. Tokita J, Vega A, Sinfield C, et al. Real World Evidence and Clinical Utility of KidneyIntelX on Patients With
Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes. J Prim Care
Community Health. Jan-Dec 2022;13:21501319221138196. doi:10.1177/21501319221138196
10.1177_21501319221138196.pdf (nih.gov)

2. Nadkarni GN, Takale D, Neal B, et al. A Post Hoc Analysis of KidneyIntelX and Cardiorenal Outcomes in
Diabetic Kidney Disease. Kidney360. Sep 29 2022;3(9):1599-1602. doi:10.34067/KID.0002172022

3. Lam D, Nadkarni GN, Mosoyan G, et al. Clinical Utility of KidneyIntelX in Early Stages of Diabetic Kidney
Disease in the CANVAS Trial. Am J Nephrol. 2022;53(1):21-31. doi:10.1159/000519920

4. Chauhan K, Nadkarni GN, Fleming F, et al. Initial Validation of a Machine Learning-Derived Prognostic Test
(KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney
Outcomes. Kidney360. Aug 27 2020;1(8):731-739. doi:10.34067/KID.0002252020
KIDK3602020000225 731..739 (nih.gov)

5. Liu C, Debnath N, Mosoyan G, et al. Systematic Review and Meta-Analysis of Plasma and Urine Biomarkers
for CKD Outcomes. J Am Soc Nephrol. Sep 2022;33(9):1657-1672. doi:10.1681/ASN.2022010098

6. Connolly P, Stapleton S, Mosoyan G, et al. Analytical validation of a multi-biomarker algorithmic test for
prediction of progressive kidney function decline in patients with early-stage kidney disease. Clin
Proteomics. Nov 17 2021;18(1):26. doi:10.1186/s12014-021-09332-y

7. Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using
biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. Jul
2021;64(7):1504-1515. doi:10.1007/s00125-021-05444-0  

8. Datar M, Ramakrishnan S, Montgomery E, Coca SG, Vassalotti JA, Goss T. A qualitative study documenting
unmet needs in the management of diabetic kidney disease (DKD) in the primary care setting. BMC Public
Health. May 17 2021;21(1):930. doi:10.1186/s12889-021-10959-7 

9. Datar M, Ramakrishnan S, Chong J, et al. A kidney diagnostic's impact on physician decision-making in diabetic kidney disease. Am J Manag Care. Dec 2022;28(12):654-661. doi:10.37765/ajmc.2022.89207

10. National Kidney Foundation Health Disparities: https://www.kidney.org/advocacy/legislative-priorities/health-disparities ( accessed 11/08/2023)

 

Open Meetings
Meeting Date Meeting States Meeting Information
02/29/2024 Connecticut
Illinois
Maine
Massachusetts
Minnesota
New Hampshire
New York - Downstate
New York - Entire State
New York - Queens
New York - Upstate
Rhode Island
Vermont
Wisconsin

Virtual Meeting:

12:00- 2:00 p.m. CT
1:00- 3:00 p.m. ET

 

N/A
Contractor Advisory Committee (CAC) Meetings
Meeting Date Meeting States Meeting Information
08/24/2023 Connecticut
Illinois
Maine
Massachusetts
Minnesota
New Hampshire
New York - Downstate
New York - Entire State
New York - Queens
New York - Upstate
Rhode Island
Vermont
Wisconsin

Virtual Meeting:

1:00- 4:00 p.m. CT
2:00- 5:00 p.m. ET

 

N/A
MAC Meeting Information URLs
CAC Meeting- KidneyIntelX and KidneyIntelX.dkd Testing
Description: KidneyIntelX and KidneyIntelX.dkd Testing
N/A
Proposed LCD Posting Date
02/08/2024
Comment Period Start Date
02/08/2024
Comment Period End Date
03/23/2024
Reason for Proposed LCD
  • Provider Education/Guidance
  • Request for Coverage by a Practitioner (Part B)
Requestor Information
This request was MAC initiated.
Requestor Name Requestor Letter
Michael J. Donovan, PhD, M.D. View Letter
N/A
Contact for Comments on Proposed LCD
National Government Services Medical Policy Unit
P.O. Box 7108
Indianapolis, IN 46207-7108
NGSDraftLCDComments@anthem.com

Coding Information

Bill Type Codes

Code Description
N/A

Revenue Codes

Code Description
N/A

CPT/HCPCS Codes

Group 1

Group 1 Paragraph

N/A

Group 1 Codes

N/A

N/A

ICD-10-CM Codes that Support Medical Necessity

Group 1

Group 1 Paragraph:

N/A

Group 1 Codes:

N/A

N/A

ICD-10-CM Codes that DO NOT Support Medical Necessity

Group 1

Group 1 Paragraph:

N/A

Group 1 Codes:

N/A

N/A

Additional ICD-10 Information

General Information

Associated Information
N/A
Sources of Information

The CAC Meeting transcript, August 24, 2023, is available below:

CAC Meeting -KidneyIntelX and KidneyIntelX.dkd

 

 

Bibliography

1. Tokita J, Vega A, Sinfield C, et al. Real World Evidence and Clinical Utility of KidneyIntelX on Patients With
Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes. J Prim Care
Community Health. Jan-Dec 2022;13:21501319221138196. doi:10.1177/21501319221138196
10.1177_21501319221138196.pdf (nih.gov)

2. Nadkarni GN, Takale D, Neal B, et al. A Post Hoc Analysis of KidneyIntelX and Cardiorenal Outcomes in
Diabetic Kidney Disease. Kidney360. Sep 29 2022;3(9):1599-1602. doi:10.34067/KID.0002172022

3. Lam D, Nadkarni GN, Mosoyan G, et al. Clinical Utility of KidneyIntelX in Early Stages of Diabetic Kidney
Disease in the CANVAS Trial. Am J Nephrol. 2022;53(1):21-31. doi:10.1159/000519920

4. Chauhan K, Nadkarni GN, Fleming F, et al. Initial Validation of a Machine Learning-Derived Prognostic Test
(KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney
Outcomes. Kidney360. Aug 27 2020;1(8):731-739. doi:10.34067/KID.0002252020
KIDK3602020000225 731..739 (nih.gov)

5. Liu C, Debnath N, Mosoyan G, et al. Systematic Review and Meta-Analysis of Plasma and Urine Biomarkers
for CKD Outcomes. J Am Soc Nephrol. Sep 2022;33(9):1657-1672. doi:10.1681/ASN.2022010098

6. Connolly P, Stapleton S, Mosoyan G, et al. Analytical validation of a multi-biomarker algorithmic test for
prediction of progressive kidney function decline in patients with early-stage kidney disease. Clin
Proteomics. Nov 17 2021;18(1):26. doi:10.1186/s12014-021-09332-y

7. Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using
biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. Jul
2021;64(7):1504-1515. doi:10.1007/s00125-021-05444-0  

8. Datar M, Ramakrishnan S, Montgomery E, Coca SG, Vassalotti JA, Goss T. A qualitative study documenting
unmet needs in the management of diabetic kidney disease (DKD) in the primary care setting. BMC Public
Health. May 17 2021;21(1):930. doi:10.1186/s12889-021-10959-7 

9. Datar M, Ramakrishnan S, Chong J, et al. A kidney diagnostic's impact on physician decision-making in diabetic kidney disease. Am J Manag Care. Dec 2022;28(12):654-661. doi:10.37765/ajmc.2022.89207

10. National Kidney Foundation Health Disparities: https://www.kidney.org/advocacy/legislative-priorities/health-disparities ( accessed 11/08/2023)

 

Revision History Information

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Updated On Effective Dates Status
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