Percentage of patients aged 18-85 years with a diagnosis of diabetes and/or hypertension who received a kidney health evaluation defined by an Estimated Glomerular Filtration Rate (eGFR) AND Urine Albumin-Creatinine Ratio (uACR) within the measurement period.
Measure Specs
General Information
Chronic Kidney Disease (CKD) is a major driver of morbidity, mortality and high healthcare costs in the United States. Currently, 35.5 million American adults have CKD and millions of others are at increased risk (CDC, 2023), with an estimated population prevalence growing to nearly 17% among Americans aged 30 years and older by the year 2030 (Hoerger, 2015; USRDS, 2025). Total Medicare spending in 2023 on both CKD and End-Stage Kidney Disease (ESKD) was over $196 billion, comprising 36% of total Medicare fee-for-service spending overall with costs increasing exponentially with advancing CKD (Nichols, 2020; USRDS, 2025). In the US from 2002-2016, the burden of CKD, defined as years of life lost, years living with disability, disability-adjusted life years, and deaths, outpaced changes in the burden of disease for other conditions (Bowe, 2018). Patients with CKD experience hospitalization and readmissions more frequently than those without diagnosed CKD (USRDS, 2025). CKD is the 9th leading cause of death in the US and is the fastest growing non-communicable disease in terms of in burden largely due to death (Hoerger, 2015; Bowe, 2018). This public health issue is driven largely by the impact of diabetes and hypertension - the most common comorbid risk factors for CKD (Bowe, 2018; USRDS, 2025).
The intent of this process measure is to improve rates of guideline-concordant kidney health evaluation in patients with diabetes and/or hypertension to more consistently identify and potentially treat or delay progression of CKD in this high-risk population. Annual kidney health evaluation in patients with diabetes and/or hypertension to determine risk of CKD using estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (uACR) is recommended by clinical practice guidelines (American Diabetes Association, 2026; American College of Cardiology, 2025; KDIGO, 2024; American Heart Association, 2023; de Boer, 2022; National Kidney Foundation, 2007; National Kidney Foundation, 2012) and has been a focus of various local and national health care quality improvement initiatives, including Healthy People 2030 (Healthy People 2030, 2023; American Heart Association, 2023). There is variability in the performance of these tests in patients with diabetes and/or hypertension. Rates of testing vary by payer type and by comorbidity. Medicare Advantage members with diabetes are tested most frequently (59.6%) whereas Medicare Fee-For-Service members with hypertension only are tested least (8.0%) (USRDS, 2024, Stempneiwicz, 2021). Low rates of detection of CKD in a population of patients with diabetes have been demonstrated to be associated with low patient awareness of their own kidney health status (Szczech, 2014). Indeed, 90% of individuals with CKD are unaware of their condition due to under-recognition and under-diagnosis (CDC, 2023). Currently, an individual’s lifetime probability of developing CKD is relatively high, reaching 54% for someone currently aged 30-49 years (Hoerger, 2015). As CKD is associated with an elevated risk of cardiovascular events and mortality even in its earliest stages, eGFR and uACR are strong predictors of adverse cardiovascular events (Khan, 2023). Regular kidney health evaluations, utilizing both eGFR and uACR, provide an opportunity to improve identification of CKD and reduce progression of CKD and its associated cardiovascular risk, particularly in high-risk populations, such as those with diabetes and/or hypertension.
References:
Bowe B, Xie Y, Li T, et al. Changes in the US Burden of Chronic Kidney Disease From 2002 to 2016: An Analysis of the Global Burden of Disease Study. JAMA Netw Open. Nov 2 2018;1(7):e184412. doi:10.1001/jamanetworkopen.2018.4412
Centers for Disease Control & Prevention (CDC). Chronic Kidney Disease in the United States, 2023. Accessed March 30, 2026 https://www.cdc.gov/kidney-disease/php/data-research/index.html
de Boer IH, Khunti K, Sadusky T, et al. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. 2022;45(12):3075-3090. doi:10.2337/dci22-0027
Diabetes* ADAPPCf. 11. Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes—2026. Diabetes Care. 2025;49(Supplement_1):S246-S260. doi:10.2337/dc26-S011
Healthy People 2020. Chronic Kidney Disease. U.S. Department of Health and Human Services,. Accessed March 30, 2026. https://odphp.health.gov/healthypeople/objectives-and-data/browse-objectives/chronic-kidney-disease
Hoerger TJ, Simpson SA, Yarnoff BO, et al. The future burden of CKD in the United States: a simulation model for the CDC CKD Initiative. Am J Kidney Dis. Mar 2015;65(3):403-11. doi:10.1053/j.ajkd.2014.09.023
KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Diabetes and Chronic Kidney Disease. Am J Kidney Dis. Feb 2007;49(2 Suppl 2):S12-154. doi:10.1053/j.ajkd.2006.12.005
KDOQI Clinical Practice Guideline for Diabetes and CKD: 2012 Update. American Journal of Kidney Diseases. 2012;60(5):850-886. doi:10.1053/j.ajkd.2012.07.005
Khan SS, Matsushita K, Sang Y, et al. Development and Validation of the American Heart Association’s PREVENT Equations. Circulation. 2024/02/06 2024;149(6):430-449. doi:10.1161/CIRCULATIONAHA.123.067626
Ndumele CE, Rangaswami J, Chow SL, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation. 2023;148(20):1606-1635. doi:doi:10.1161/CIR.0000000000001184
Nichols GA, Ustyugova A, Déruaz-Luyet A, O'Keeffe-Rosetti M, Brodovicz KG. Health Care Costs by Type of Expenditure across eGFR Stages among Patients with and without Diabetes, Cardiovascular Disease, and Heart Failure. J Am Soc Nephrol. Jul 2020;31(7):1594-1601. doi:10.1681/asn.2019121308
Stempniewicz N, Vassalotti JA, Cuddeback JK, et al. Chronic Kidney Disease Testing Among Primary Care Patients With Type 2 Diabetes Across 24 U.S. Health Care Organizations. Diabetes Care. Sep 2021;44(9):2000-2009. doi:10.2337/dc20-2715
Stevens PE, Ahmed SB, Carrero JJ, et al. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International. 2024;105(4):S117-S314. doi:10.1016/j.kint.2023.10.018
Szczech LA, Stewart RC, Su H-L, et al. Primary Care Detection of Chronic Kidney Disease in Adults with Type-2 Diabetes: The ADD-CKD Study (Awareness, Detection and Drug Therapy in Type 2 Diabetes and Chronic Kidney Disease). PLOS ONE. 2014;9(11):e110535. doi:10.1371/journal.pone.0110535
United States Renal Data System. 2024 USRDS Annual Data Report: Epidemiology of kidney disease in the United States, 2024. Accessed March 30, 2026. https://usrds-adr.niddk.nih.gov/2024
United States Renal Data System. 2025 USRDS Annual Data Report: Epidemiology of kidney disease in the United States, 2025. Accessed March 30, 2026 https://usrds-adr.niddk.nih.gov/2025
Writing Committee M, Jones DW, Ferdinand KC, et al. 2025 AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2025/09/16 2025;152(11):e114-e218. doi:10.1161/CIR.0000000000001356
Practices collect EHR data using certified electronic health record technology (CEHRT). The MAT output, which includes the human readable and XML artifacts of the clinical quality language (CQL) for the measure are contained in the eCQM specifications attached. No additional tools are used for data collection for eCQMs.
Numerator
Patients who received a kidney health evaluation during the measurement period.
Kidney health evaluation is defined by an eGFR AND uACR within the measurement period OR an eGFR and a Urine Albumin and Urine Creatinine result documented less than or equal to four days apart.
Patients who received a kidney health evaluation during the measurement period.
Kidney health evaluation is defined by an:
- Estimated Glomerular Filtration Rate (eGFR) and
- Urine Albumin-Creatinine Ratio (uACR).
OR
- An eGFR and
- A Urine Albumin and Urine Creatinine result documented less than or equal to four days apart.
Specific codes required to calculate the numerator are outlined in the attached value set data dictionary and eCQM package (Quality Data Model - QDM output).
Denominator
All patients aged 18-85 years with a diagnosis of diabetes and/or hypertension at the start of the measurement period with a visit during the measurement period.
All patients:
- Aged 18-85 years and
- A diagnosis of diabetes and/or hypertension at the start of the measurement period and
- Has at least one visit during the measurement period
Specific codes required to calculate the denominator are outlined in the attached value set data dictionary and eCQM package (Quality Data Model - QDM output).
Exclusions
Patients with a diagnosis of ESRD active during the measurement period
Patients with a diagnosis of CKD Stage 5 active during the measurement period
Patients who have an order for or are receiving hospice or palliative care
Patients who meet the following criteria during the measurement period:
- Patients with a diagnosis of ESRD active during the measurement period
- Patients with a diagnosis of CKD Stage 5 active during the measurement period
- Patients who have an order for or are receiving hospice or palliative care
Specific codes required to calculate the denominator exclusions are outlined in the attached value set data dictionary and eCQM package (Quality Data Model - QDM output).
Measure Calculation
The measure calculation diagram is attached to this submission.
Performance Rate = (Numerator)/ (Denominator - Exclusions)
- Find the patient visits that qualify for the denominator (i.e., the patients who meet the age, diagnosis and encounter requirements).
- Remove those patients who meet the exclusion criteria from the denominator.
- Identify whether the patients received both the eGFR and uACR tests during the measurement period.
Not applicable - this measure is not stratified.
A minimum sample size of 20 patients at the individual clinician level was used to demonstrate that the performance scores were reliable. A minimum was not needed at the group level to ensure that the scores were reliable.
Supplemental Attachment
Point of Contact
Not applicable
Devante Dodgens
New York, NY
United States
Elizabeth Montgomery
National Kidney Foundation
New York, NY
United States
Importance
Evidence
As discussed in section 1.10, the intent of this process measure is to improve rates of guideline-concordant kidney health evaluation in patients with diabetes and/or hypertension to more consistently identify and potentially treat or delay progression of CKD in this high-risk population. Rates of CKD diagnoses continue to increase with one estimate indicating that it will be in the top five causes of years of life lost and contribute to increasing healthcare costs across the world within the next fifteen years (Li, 2020). In the United States, 14% of adults live with chronic kidney disease, with older, rural, and underserved communities bearing a higher burden of disease (CDC, 2026). Detecting patients at risk as early as possible allows clinicians the opportunity to offer lifestyle and treatment strategies to slow or prevent the progression to end-stage kidney disease (ESKD) and reduce cardiovascular events (e.g., myocardial infarction) (de Boer, 2022; Kahn, 2024).
This clinical quality measure is based on several evidence-based clinical guidelines ((American Diabetes Association, 2026; American College of Cardiology, 2025; KDIGO, 2024; National Kidney Foundation, 2007; National Kidney Foundation, 2012). These guidelines explicitly recommended eGFR and uACR laboratory testing in patients with a diagnosis of diabetes and/or hypertension. Kidney health evaluations, utilizing both eGFR and uACR tests, among patients with a diagnosis of diabetes and/or hypertension, provides an opportunity to improve identification of CKD and reduce cardiovascular morbidity and prevent worsening kidney function.
The following evidence statements are quoted verbatim from the referenced clinical guidelines and other sources, where applicable:American Diabetes Association, Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes—2026:
Assess kidney function with random urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) at least annually in people with type 1 diabetes with duration of ≥5 years and in all people with type 2 diabetes regardless of treatment. Level of Evidence: B
KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Diabetes and Chronic Kidney Disease, 2007 and 2012 Update:Patients with diabetes should be screened annually for Diabetic Kidney Disease (DKD). Initial screening should commence:· 5 years after the diagnosis of type 1 diabetes; (Quality of Evidence: A) or· From diagnosis of type 2 diabetes. (Quality of Evidence: B)Screening should include:· Measurements of urinary albumin-creatinine ratio (ACR) in a spot urine sample; (Quality of Evidence: B)· Measurement of serum creatinine and estimation of GFR. (Quality of Evidence: B)
KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease:
Practice Point 1.1.1.1: Test people at risk for and with chronic kidney disease (CKD) using both urine albumin measurement and assessment of glomerular filtration rate (GFR).
AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults, 2025
For adults who are diagnosed with hypertension, laboratory tests (ie, complete blood count, serum electrolytes, serum creatinine, lipid pro le, glucose or hemoglobin A1c [HbA1c], thyroid-stimulating hormone, urinalysis, and urine albumin-to-creatinine ratio) and diagnostic procedures (12-lead ECG) should be performed to optimize management. (COR: 1, LOE: C-EO)
References:
Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2026.
Diabetes* ADAPPCf. 11. Chronic Kidney Disease and Risk Management: Standards of Care in Diabetes—2026. Diabetes Care. 2025;49(Supplement_1):S246-S260. doi:10.2337/dc26-S011
KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Diabetes and Chronic Kidney Disease. Am J Kidney Dis. Feb 2007;49(2 Suppl 2):S12-154. doi:10.1053/j.ajkd.2006.12.005
KDOQI Clinical Practice Guideline for Diabetes and CKD: 2012 Update. American Journal of Kidney Diseases. 2012;60(5):850-886. doi:10.1053/j.ajkd.2012.07.005
Khan SS, Matsushita K, Sang Y, et al. Development and Validation of the American Heart Association’s PREVENT Equations. Circulation. 2024/02/06 2024;149(6):430-449. doi:10.1161/CIRCULATIONAHA.123.067626
Li PK, Garcia-Garcia G, Lui SF, et al. Kidney health for everyone everywhere-from prevention to detection and equitable access to care. Kidney Int. 2020;97:226–232.
Stevens PE, Ahmed SB, Carrero JJ, et al. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney International. 2024;105(4):S117-S314. doi:10.1016/j.kint.2023.10.018
Writing Committee M, Jones DW, Ferdinand KC, et al. 2025 AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2025/09/16 2025;152(11):e114-e218. doi:10.1161/CIR.0000000000001356
Measure Impact
CKD is asymptomatic at onset. Clinicians and patients can only learn about the presence of CKD through routine testing for CKD among people who are at risk.
Eighty percent of people living with chronic kidney disease remain undetected in primary care settings (Maciejewski, 2020; Shang, 2021). Overarching care for people with a CKD diagnosis is also suboptimal as most people do not receive guideline directed medical therapy (GDMT) (Nicholas, 2023). Only 54% of people with advanced CKD receive nephrology care (USRDS, 2025).
Recent publications suggest that improvements in CKD testing, particularly the use of urine albumin-creatinine ratio, impacts the probability of people with CKD receiving GDMT in primary care settings (Chu, 2023). Studies regarding the impact of new interventions, such as sodium-glucose transporter 2 inhibitors (SGLT2i) (Heerspink, 2020; Herrington, 2023; Perkovic, 2019) or non-steroidal mineralocorticoid receptor agonists (nsMRA) (Bakris, 2020), have demonstrated significant reductions in CKD progression, associated cardiovascular events, and related utilization. A recent retrospective cohort study illustrated that good CKD disease management in CKD Stage 3 and Stage 4 (defined as CKD testing, diagnosis, risk factor management, and use of basic interventions to address proteinuria) could yield as much as a 40% reduction in inpatient hospitalization, a 30% reduction in emergency room visits, and an decrease in monthly healthcare costs by as much as 17% (Li, 2023).
While the opportunity to slow CKD progression, reduce the rising cardiovascular risk associated with it, and reduce utilization are opportunities that arise from improved CKD testing, there are few adverse events that are associated with the use of the two widely available, inexpensive tests associated with this measure.
References:
Bakris GL, Agarwal R, Anker SD, et al. Effect of Finerenone on Chronic Kidney Disease Outcomes in Type 2 Diabetes. New England Journal of Medicine 2020;383(23):2219-2229. DOI: doi:10.1056/NEJMoa2025845.
Chu CD, Xia F, Du Y, et al. Estimated Prevalence and Testing for Albuminuria in US Adults at Risk for Chronic Kidney Disease. JAMA Netw Open 2023;6(7):e2326230. (In eng). DOI: 10.1001/jamanetworkopen.2023.26230.
Heerspink HJL, Stefánsson BV, Correa-Rotter R, et al. Dapagliflozin in Patients with Chronic Kidney Disease. N Engl J Med 2020;383(15):1436-1446. (In eng). DOI: 10.1056/NEJMoa2024816.
Herrington WG, Staplin N, Wanner C, et al. Empagliflozin in Patients with Chronic Kidney Disease. N Engl J Med 2023;388(2):117-127. (In eng). DOI: 10.1056/NEJMoa2204233.
Li Y, Barve K, Cockrell M, et al. Managing comorbidities in chronic kidney disease reduces utilization and costs. BMC Health Serv Res 2023;23(1):1418. (In eng). DOI: 10.1186/s12913-023-10424-8.
Maciejewski, M, Onstad, K, and Tamayo, L. Chronic Kidney Disease Often Undiagnosed in Medicare Beneficiaries. CMS OMH Data Highlight No.20. Baltimore, MD: CMS Office of Minority Health 2020.
Nicholas SB, Daratha KB, Alicic RZ, et al. Prescription of guideline-directed medical therapies in patients with diabetes and chronic kidney disease from the CURE-CKD Registry, 2019-2020. Diabetes Obes Metab. 2023;25(10):2970-2979. doi:10.1111/dom.15194
Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med 2019;380(24):2295-2306. (In eng). DOI: 10.1056/NEJMoa1811744.
Shang, N, Khan, A, Polubriaginof, F, et al. Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies. npj Digit. Med. 4, 70 (2021). https://doi.org/10.1038/s41746-021-00428-1
United States Renal Data System. 2025 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2025.
This measure, which expands the population to include patients with a diagnosis of hypertension, will be submitted to the Centers for Medicare & Medicaid Services (CMS) to be considered to replace the existing Merit-Based Incentive Program (MIPS) Kidney Health Evaluation measure (Quality ID #488). The rationale provided in section 1.10 provides additional context on why this measure is needed.
This measure was developed with input from a technical expert panel (TEP), which included patient and caregiver representation. These individuals were also trained in measure development prior to participating on the TEP. Generally, patients express the wish to have been made aware of their kidney health or diagnosed with CKD earlier as it would have allowed them opportunities for better lifestyle choices and engage in decision-making.
Performance Gap
The data used for testing in this submission (described in Section 5.1) demonstrates that there is a gap in care for routine testing for CKD in patients with a diagnosis of diabetes and/or hypertension. Specifically, performance scores at the individual clinician and group levels were calculated for the period of January 1 to December 31, 2025, for 374 individual clinicians across six clinics within a large health system in the Northeast (Tables 1 and 2 in the supplemental attachment).
Table 1: Mean performance scores by decile, clinician-level, 2025, sample size >=20
Overall | Min | Decile 1 | Decile 2 | Decile 3 | Decile 4 | Decile 5 | Decile 6 | Decile 7 | Decile 8 | Decile 9 | Decile 10 | Max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean Performance Score | 24.59% | 0.00% | 8.69% | 15.53% | 16.81% | 19.70% | 22.86% | 26.29% | 27.96% | 33.57% | 37.91% | 44.43% | 52.63% |
| N of Entities | 82 | 1 | 9 | 8 | 7 | 9 | 8 | 9 | 8 | 7 | 9 | 8 | 1 |
| N of Persons / Encounters / Episodes | 4,725 | 20 | 482 | 1,119 | 257 | 258 | 315 | 557 | 377 | 307 | 432 | 621 | 403 |
Table 2: Distribution of performance scores, group-level, 2025
# of Clinics | # of patients | Min | P10 | P25 | Median | P75 | P90 | Max | Mean | Std Deviation |
6 | 7,885 | 14.28% | 14.28% | 14.37% | 24.56% | 33.65% | 34.84% | 34.84% | 24.38% | 0.0893 |
Care Gaps
Closing Care Gaps
As discussed in section 1.10, research demonstrates that the number of patients with diabetes and/or hypertension who receive routine testing for CKD remains less than optimal and testing varies by payer type and by comorbidity. Medicare Advantage members with diabetes are tested most frequently (59.6%) whereas Medicare Fee-For-Service members with hypertension only are tested least (8.0%) (USRDS, 2024, Stempneiwicz, 2021). Low rates of detection of CKD in a population of patients with diabetes have been demonstrated to be associated with low patient awareness of their own kidney health status (Szczech, 2014). In fact, 90% of individuals with CKD are unaware of their condition due to under-recognition and under-diagnosis (CDC, 2023).
Using the same data described in section 2.4 Performance Gap, we also calculated mean performance score overall (i.e., all patients with diabetes), and by patient’s characteristics (sex, race, and Hispanic), hypertension status, type of insurance, and clinics. Through these estimations, we seek to identify potential performance differences within subgroups (Table 3 in the supplemental attachment). The overall performance score across all patients from the six clinics is 23.8%. Performance differed between male (25.6%) and female (22.6%), different race categories (e.g., 22.9% for White and 26.8% for Asian), type of insurance (e.g., 25.7% for commercial insurance and 22.4% for managed Medicaid program), and across clinics (ranged from 14.3% to 34.8%). As shown in below Table 3, there are statistically significant differences in subgroups by sex (p=0.002), race (p=0.0302), and clinics (p<0.0001).
Based on published research and data from the six clinics, we believe that clinicians should be aware that potential differences in care decisions can exist, especially on those individuals matching those characteristics that may be less likely to receive these tests and develop quality improvement strategies at the point of care to drive further improvements.
References:
Centers for Disease Control & Prevention (CDC). Chronic Kidney Disease in the United States, 2023. Accessed March 30, 2026 https://www.cdc.gov/kidney-disease/php/data-research/index.html
Stempniewicz N, Vassalotti JA, Cuddeback JK, et al. Chronic Kidney Disease Testing Among Primary Care Patients With Type 2 Diabetes Across 24 U.S. Health Care Organizations. Diabetes Care. Sep 2021;44(9):2000-2009. doi:10.2337/dc20-2715
Szczech LA, Stewart RC, Su H-L, et al. Primary Care Detection of Chronic Kidney Disease in Adults with Type-2 Diabetes: The ADD-CKD Study (Awareness, Detection and Drug Therapy in Type 2 Diabetes and Chronic Kidney Disease). PLOS ONE. 2014;9(11):e110535. doi:10.1371/journal.pone.0110535
United States Renal Data System. 2024 USRDS Annual Data Report: Epidemiology of kidney disease in the United States, 2024. Accessed March 30, 2026. https://usrds-adr.niddk.nih.gov/2024
Table 3. Mean performance score performance score overall (i.e., all patients with diabetes), and by patient’s characteristics (sex, race, and Hispanic), hypertension status, type of insurance, and clinics, clinician-level, 2025
Denominator | Numerator | Performance score | P-value | |
|---|---|---|---|---|
N | N | % |
| |
| Total | 7,885 | 1,875 | 23.8 |
|
| Stratification Variables |
|
|
|
|
| Hypertension |
|
|
| 0.4071 |
| Yes | 6,613 | 1,561 | 23.6 |
|
| No | 1,272 | 314 | 24.7 |
|
| Sex |
|
|
| 0.002 |
| Female | 4,736 | 1,069 | 22.6 |
|
| Male | 3,149 | 806 | 25.6 |
|
| Race |
|
|
| 0.0302 |
| White | 1,871 | 429 | 22.9 |
|
| Black | 3,033 | 679 | 22.4 |
|
| Asian | 575 | 154 | 26.8 |
|
| Multi Races | 1,555 | 395 | 25.4 |
|
| Others | 851 | 218 | 25.6 |
|
| Hispanic |
|
|
| 0.6281 |
| Hispanic | 1,720 | 420 | 24.4 |
|
| Non Hispanic | 5,061 | 1,186 | 23.4 |
|
| Others | 1,104 | 269 | 24.4 |
|
| Payer |
|
|
| 0.1331 |
| Commercial Insurance | 1,966 | 505 | 25.7 |
|
| Managed Medicaid | 1,111 | 249 | 22.4 |
|
| Managed Medicare | 905 | 210 | 23.2 |
|
| Medicaid | 2,409 | 555 | 23.0 |
|
| Medicare | 1,479 | 355 | 24.0 |
|
| Others | 15 | 1 | 6.7 |
|
| Clinics |
|
|
| <0.0001 |
| Clinic 1 | 835 | 120 | 14.4 |
|
| Clinic 2 | 1,386 | 198 | 14.3 |
|
| Clinic 3 | 3,240 | 771 | 23.8 |
|
| Clinic 4 | 537 | 136 | 25.3 |
|
| Clinic 5 | 1,260 | 439 | 34.8 |
|
| Clinic 6 | 627 | 211 | 33.7 |
|
Feasibility
Feasibility
This measure’s feasibility of data capture using electronic health record systems (EHRs) was assessed in one vendor system (Epic) used by two clinics within a large health system in the Northeast and the results evaluating the measure’s 30 data elements are captured in 5611e NKF KEH eCQM feasibility scorecard. Twenty-three data elements are documented in discrete fields using data standards and are routinely captured within the clinics. Of the seven data elements with a score of 0, these clinical concepts are captured using other data elements defined within the specifications. The measure is defined using multiple combinations of code systems to allow EHRs flexibility in capturing the required data elements and the continued inclusion of these data elements were considered important to facilitate ease of implementation regardless of the vendor system used and setting in which the measure is implemented.
While any implementation of an eCQM requires time and resources to map the clinical concepts as defined by the measure specifications within the EHRs, the feasibility assessment demonstrates that the data required for the measure can be captured within existing clinical workflows. No other costs to implement and report the measure are required. In addition, clinicians and practices can successfully capture and report the data needed for the existing Kidney Health Evaluation measure (Quality ID# 488), which uses the same data elements as this new measure.
This measure leverages structured data from EHRs, which supports secure and confidential data collection. No patient-identifiable data are needed to report the measure, and the measure does not rely on patient surveys.
Initial feasibility testing demonstrated that the measure can be readily captured using data from the EHRs. In addition, this measure leverages past implementation experience from the previous version of the measure that is currently in use in the Merit Incentive-based Payment System (MIPS) (Quality ID# 488).
Proprietary Information
Physician Performance Measures (Measures) and related data specifications developed by the National Kidney Foundation (NKF) are intended to facilitate quality improvement activities by health care professionals. These Measures are intended to assist health care professionals in enhancing quality of care.
These Measures are not clinical guidelines and do not establish a standard of medical care and have not been tested for all potential applications. NKF encourages testing and evaluation of its Measures.
Measures are subject to review and may be revised or rescinded at any time by NKF. The measures may not be altered without prior written approval from NKF. The measures, while copyrighted, can be reproduced and distributed, without modification, for noncommercial purposes. Commercial use is defined as the sale, license, or distribution of the measures for commercial gain, or incorporation of the measures into a product or service that is sold, licensed, or distributed for commercial gain. Commercial uses of the measures require a license agreement between the user and NKF. Neither NKF nor its members shall be responsible for any use of the measures.
THESE MEASURES AND SPECIFICATIONS ARE PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND.
Limited proprietary coding is contained in the Measure specifications for convenience. Users of the proprietary code sets should obtain all necessary licenses from the owners of these code sets.
CPT(R) contained in the Measure specifications is copyright 2004-2023 American Medical Association. LOINC(R) is copyright 2004-2025 Regenstrief Institute, Inc. This material contains SNOMED Clinical Terms(R) (SNOMED CT[R]) copyright 2004-2025 International Health Terminology Standards Development Organisation. ICD-10 is copyright 2025 World Health Organization. All Rights Reserved.
Due to technical limitations, registered trademarks are indicated by (R) or [R].
Scientific Acceptability
Testing Data
Data on 7,885 unique patients from six primary care clinics within a large health system in the Northeast that uses Epic for their electronic health record system from January 1 to December 31, 2025. The six clinics included 374 clinicians and the practice sizes ranged from six to 179 clinicians. The clinics were located across New York City, providing comprehensive outpatient adult care, preventive services and chronic disease management for a diverse set of individuals and included two resident/teaching clinics. These data were used for calculating performance gap, reliability, and validity.
From 01/01/2025 through 12/31/2025
Data element validity testing used the same data described in section 5.1.1 but only a sample 85 patients were randomly selected from Clinics 3 and 4.
There is a total of 374 entities (i.e., clinicians) from the six clinics. The number of entities who provided data in each of these six clinics are 6, 12, 13, 34, 130, and 179. The number of patients attributed to each clinician (measured by number of unique patients) varied with a minimum number of patients of 1, 25% quantile of 4, median of 10, 75% of quantile of 18, and maximum of 403.
Initially, there were 11,431 unique patients and 2.4% of these patients were removed when exclusions (i.e., patients with ESRD or CKD stage 5 or have an order or are receiving hospice or palliative care) were applied. We then excluded the duplicate patients who were defined as individuals who had one or more visits with two or more different clinicians across the clinics. The final data set used for testing included 7,885 unique patients. The distribution of patient characteristics by hypertension status, demographic variables (sex, race, and ethnicity), type of insurance, and clinics are presented in Table 4 in the supplemental attachment.
Table 4. Characteristics of denominators
Denominator | Numerator | P-value | |
|---|---|---|---|
N | N |
| |
| Total | 7,885 | 1,875 |
|
| Stratification Variables |
|
|
|
| Hypertension |
|
| 0.4071 |
| Yes | 6,613 | 1,561 |
|
| No | 1,272 | 314 |
|
| Sex |
|
| 0.002 |
| Female | 4,736 | 1,069 |
|
| Male | 3,149 | 806 |
|
| Race |
|
| 0.0302 |
| White | 1,871 | 429 |
|
| Black | 3,033 | 679 |
|
| Asian | 575 | 154 |
|
| Multi Races | 1,555 | 395 |
|
| Others | 851 | 218 |
|
| Hispanic |
|
| 0.6281 |
| Hispanic | 1,720 | 420 |
|
| Non Hispanic | 5,061 | 1,186 |
|
| Others | 1,104 | 269 |
|
| Payer |
|
| 0.1331 |
| Commercial Insurance | 1,966 | 505 |
|
| Managed Medicaid | 1,111 | 249 |
|
| Managed Medicare | 905 | 210 |
|
| Medicaid | 2,409 | 555 |
|
| Medicare | 1,479 | 355 |
|
| Others | 15 | 1 |
|
| Clinics |
|
| <0.0001 |
| Clinic 1 | 835 | 120 |
|
| Clinic 2 | 1,386 | 198 |
|
| Clinic 3 | 3,240 | 771 |
|
| Clinic 4 | 537 | 136 |
|
| Clinic 5 | 1,260 | 439 |
|
| Clinic 6 | 627 | 211 |
|
Reliability
To assess signal-to-noise, we employed the beta-binomial model as described by JL Adams in “The Reliability of Provider Profiling” (Adams, JL. The reliability of provider profiling: A tutorial. RAND Health, 2009). Using the techniques detailed in that document, we estimated the Clinician-to-Clinician variance (the signal) and the within-Clinician variance (the noise). The ratio of these estimates then produced an estimate of the reliability at each clinician, where a reliability of 0 implies that all variability is due to measurement error, while a reliability of 1 indicates that all variability is due to real differences in performance. The distribution of reliability estimates across all clinicians was examined. The equation of reliability is as below.
Reliability = [σ 2provider-to-provider/(σ 2provider-to-provider + σ 2error)]
σ 2provider-to-provider = αβ/[(α+β+1)(α+β)2]
σ 2error = p(1-p)/n
Table 5 provides the clinician-level reliability by denominator decile and Table 6 includes the clinician-level reliability by reliability score decile. Both used a minimum sample size of >= 20. Table 7 provides the distribution of reliability at the group level with no minimum samples applied. All tables can be found in the supplemental attachment.
Reliability is the measure of whether you can distinguish one provider from another. A reliability of 1 indicates that all variability is due to real differences in performance. Our result of mean reliability is 0.69 for patients with a diagnosis of diabetes and/or hypertension among individual clinicians with 20 or more patients in the denominator in Table 5. Results in Table 6 show the distribution of reliabilities by reliability decile at the individual clinician level. When we limited the sample size to 20 patients and greater, 96% (79 out of 82) of entities have a reliability of ≥ 0.4; 88% (72 out of 82) of entities have a reliability of ≥ 0.5; and 63% (52 out of 82) of entities have a reliability of ≥ 0.6. Lastly, the mean reliability at the group level across all six clinics was 0.971 and the interquartile range is narrow, demonstrating that variability is due to real differences in group performance rather than noise (Table 7).
To understand what may have contributed to the lower reliability scores at the individual clinician level (i.e., at least 70% of clinicians did not achieve reliability of >0.6), we compared the factors affecting reliability between clinician groups with reliabilities < and >0.6: sample size, performance score, and within variance (i.e., noise).
- The median sample size is 25 in those clinicians with reliability <0.6 VS. 76 in those clinicians with reliability >0.6. When holding other factors unchanged, reliability decreases with decreasing sample size.
- The performance score is 27% in those clinicians with reliability <0.6 VS. 24% in those clinicians with reliability >0.6. When holding other factors unchanged, reliability decreases with probability (i.e., performance score) increasing before reaching 50%.
- The mean within variance is 0.0074 in those clinicians with reliability <0.6 VS. 0.0025 in those clinicians with reliability >0.6. Based on the formula, when within variance (i.e., noise) is larger, the reliability is smaller.
Table 5: Reliability by denominator decile, clinician-level, 2025, sample size >= 20
Overall | Min | Decile 1 | Decile 2 | Decile 3 | Decile 4 | Decile 5 | Decile 6 | Decile 7 | Decile 8 | Decile 9 | Decile 10 | Max | |
| Reliability | 0.6920 | 0.4829 | 0.5615 | 0.6135 | 0.5608 | 0.6170 | 0.6272 | 0.7239 | 0.7506 | 0.7739 | 0.8125 | 0.9177 | 0.9547 |
| Mean Performance Score | 24.59% | 3.85% | 18.38% | 16.30% | 26.92% | 25.04% | 29.19% | 22.57% | 26.27% | 30.53% | 31.48% | 18.48% | 45.88% |
| N of Entities | 68 | 1 | 7 | 7 | 7 | 8 | 6 | 6 | 7 | 7 | 7 | 6 | 1 |
| N of Persons / Encounters / Episodes | 4,480 | 21 | 153 | 173 | 206 | 283 | 243 | 297 | 486 | 591 | 767 | 1,281 | 342 |
Note on Table 5: The sample size (i.e., N of persons per entity) is calculated based on number of persons with both diabetes and hypertension. As a result, total 68 entities are included for this table.
Table 6: Reliability by reliability score decile, clinician-level, 2025, sample size >= 20
Overall | Min | Decile 1 | Decile 2 | Decile 3 | Decile 4 | Decile 5 | Decile 6 | Decile 7 | Decile 8 | Decile 9 | Decile 10 | Max | |
| Reliability | 0.6920 | 0.4829 | 0.5022 | 0.5397 | 0.5802 | 0.6213 | 0.6722 | 0.7195 | 0.7561 | 0.7928 | 0.8392 | 0.9279 | 0.9547 |
Table 7: Distribution of reliability, group-level, 2025
# of Clinics | # of patients | Min | P10 | P25 | Median | P75 | P90 | Max | Mean | Std Deviation |
6 | 7,885 | 0.948 | 0.948 | 0.949 | 0.976 | 0.987 | 0.991 | 0.992 | 0.971 | 0.0186 |
Validity
Data element validity testing
We performed data element validity test for two clinics with Epic as their EHRs. An electronic report of the data elements as defined by the eCQM specification was produced and the medical record was then reviewed by a clinician for the presence or absence of the same data elements on a randomly selected set of patients. The results of the electronic report were then compared against the medical record (gold standard). The sample size for this analysis was 85 patients randomly selected.
First, we calculated percentage of agreement of data used in the analysis with data from the gold standard. We defined “agreement” if both are reported same. Second, we calculated a Kappa coefficient, which is a measure of interrater agreement. When there is perfect agreement between the two ratings, the kappa coefficient equals +1. When the observed agreement exceeds chance agreement, the value of kappa is positive, and its magnitude reflects the strength of agreement. The minimum value of kappa is between –1 and 0, depending on the marginal proportions. A value of kappa higher than 0.75 can be considered (arbitrarily) as "excellent" agreement, while lower than 0.4 will indicate "poor" agreement.
In addition, we also calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the individual data elements. Using the diabetes diagnosis data element as an example, these analyses tell us the following:
- Sensitivity is the probability that a person is reported as having a diagnosis of diabetes among those who truly have that diagnosis documented in the “gold standard.”
- Specificity is fraction of those reporting that they do not have the diabetes diagnosis who actually do not have the diagnosis documented in the “gold standard.”
- PPV is the probability of true patients with a diagnosis of diabetes among those who reported diabetes.
- NPV is the probability of true patients without a diagnosis of diabetes among those who reported that they did not have that diagnosis.
Face validity testing
As we considered expanding the denominator for the existing measure in MIPS (Quality ID# 488) to include patients with a diagnosis of hypertension in addition to diabetes, we distributed a survey to 23 individuals (19 clinicians and 4 patients) to formally assess the face validity of the updated measure. Information on the updated measure was provided, and we systematically assessed the face validity of the measure score as an indicator of quality based on the responses received to the following questions:
- The scores obtained from the measure as specified can be used to distinguish good and poor quality.
- The measure specifications are appropriate and align with current evidence.
Respondents were asked to indicate their agreement based on a five-point scale, where 1= Strongly Disagree; 2 = Agree; 3=Neither Agree nor Disagree; 4 – Agree; 5=Strongly Agree. They were also asked to provide further information on why they may disagree or strongly disagree with a statement.
Data element validity testing
Results by each data element are presented in Table 8 in the supplemental attachment. Analysis of the individual data elements demonstrated that the range of percentage of agreement is from 88.1% to 100%. The range of Kappa is from 0.729 to 1.00.
Table 8. Validity testing on data elements
Data element | Manual abstraction (gold standard) | EHR automated report | Percentage of agreement^ | Kappa | Sensitivity | Specificity | PPV | NPV | |
|---|---|---|---|---|---|---|---|---|---|
Elements for Supplemental Data | Ethnicity | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. |
| Payer | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
| Race | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
| Sex/Gender | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
| Age | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
Elements for Initial Population | Diabetes diagnosis | 53.57 | 52.38 | 98.81 | 0.976 | 97.78 | 100.00 | 100.00 | 97.50 |
| Hypertension diagnosis | 90.48 | 90.48 | 97.62 | 0.862 | 98.68 | 87.50 | 98.68 | 87.50 | |
| At least 1 outpatient visit | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
Elements for Denominator Exclusions | CKD Stage 5 | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. |
| ESRD | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
| Hospice | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
| Palliative Care | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. | |
Elements for Numerator | eGFR | 100.00 | 100.00 | 100.00 |
| 100.00 | n.a. | 100.00 | n.a. |
| UACR | 35.71 | 28.57 | 88.10 | 0.729 | 73.33 | 96.30 | 91.67 | 86.67 | |
| ^Agreement: "Yes" for manual abstraction and "1" for the electronic report OR "No" for manual abstraction and "0" for the electronic report. | |||||||||
Face validity testing
Twelve out of 23 individuals responded to the survey (52%) and at least one respondent was a patient. All agreed (25%) or strongly agreed (75%) that the scores obtained from this measure can distinguish between high and low-quality performance. Thirty-three percent agreed and 67% strongly agreed that the measure specifications were appropriate and aligned with current evidence.
Data element validity testing
Analysis of the individual data elements demonstrated that the range of percentage of agreement is from 88.1% to 100%. The range of Kappa is from 0.729 to 1.00. These results show very good agreement.
Face validity testing
There was 100% agreement that the scores obtained from this measure can distinguish between high and low-quality performance and the measure as specified is aligned with current evidence. These responses support the use of this measure to evaluate the quality of care provided on routine testing for CKD.
Risk Adjustment
Use & Usability
Use
The existing measures Quality ID #488: Kidney Health Evaluation and the National Committee for Quality Assurance (NCQA) measure Kidney Health Evaluation for Patients With Diabetes (KED) are used in numerous government and quasi-governmental accountability programs. Both measures have the same quality target: closing the gap in the percentage of persons 18–85 years of age with diabetes (type 1 or type 2) who received a kidney health evaluation, defined by an estimated glomerular filtration rate (eGFR) and a urine albumin-creatinine ratio (uACR), during the measurement period. The measures were developed and specified for use in accountability programs at the clinician level (i.e., the Merit-Based Incentive Payment System (MIPS)) and the health plan level (i.e., the Healthcare Effectiveness Data and Information Set (HEDIS)). As a result of this strategy, we can align health programs around the measures by advocating for their inclusion in accountability programs of various designs. Our advocacy strategy has been successful, demonstrated by the adoption of Kidney Health Evaluation in the Quality Payment Program (QPP) including both traditional MIPS and MIPS Value Pathways, interest in the measure for the APM Performance Pathway (APP) Plus quality measure set, and accountability for measure performance in both HEDIS and the Medicare Advantage Star Ratings.
The strategy for the expanded measure with the inclusion of hypertension in the clinician and group levels is similar. We are already in discussions with the Centers for Medicare and Medicaid Services (CMS) about the use of the expanded measure in MIPS and how the addition of hypertension would enhance other accountability programs like MIPS Value Pathways. The addition of hypertension to the denominator will support development of a health plan measure, which will be suitable for the Medicaid and Children’s Health Insurance Program (CHIP) Core Set and for the quality measure set for Federally Qualified Health Centers (FQHCs), the Uniform Data System.
The expanded measure is a process measure for which we do not anticipate any differential performance by either clinicians or health plans based on medical or social risk factors. The clinical practice guidelines upon which the measure is based also does not suggest that performance should vary by unmodifiable structural factors. Therefore, the measure specification does not include risk adjustment.
Usability
Two actions entities must take to ensure successful performance on this measure include education of cross-functional clinical care teams and engagement with laboratory leadership to ensure accurate calculation and reporting of kidney testing results.
Previous testing of the KEH measure (Quality ID# 488) for measured entities was approximately under 40% for the population with diabetes who should be receiving annual eGFR and uACR testing according to clinical practice guideline recommendations. The inclusion of patients with a diagnosis of hypertension strengthens the usefulness of the measure by addressing an equally important and high-risk population. To increase compliance, clinical care team education on the importance of screening at risk populations and targeted albuminuria testing is important. Once clinical care teams are engaged, collaboration with laboratory leadership is essential to clarify measured values are being captured accurately as issues may arise in the calculation of the uACR if urine albumin levels are below a detectable range.
If measured entities have created electronic health record-based best practice alerts or similar tools to improve performance on the Kidney Health Evaluation measure (Quality ID# 488), working with the institutional informatics teams to add the additional ICD-10 codes to the current algorithms, order sets, standing orders, etc., will facilitate improved performance on this new measure.
Reference:
Ferrè S, Storfer-Isser A, Kinderknecht K, et al. Fulfillment and Validity of the Kidney Health Evaluation Measure for People with Diabetes. Mayo Clin Proc Innov Qual Outcomes. 2023;7(5):382-391. Published 2023 Aug 29. doi:10.1016/j.mayocpiqo.2023.07.002
No unintended consequences with the use of the previous version of the measure in MIPS (Quality ID# 488) has been identified and we do not anticipate that the expansion of the denominator will create any potential new concerns. NKF will continue to monitor for unintended consequences.
Comments
Staff Preliminary Assessment
CBE #5611e Staff Assessment
Importance
Strengths:
- This process measure intends to improve rates of guideline-concordant kidney health evaluation in patients with diabetes and/or hypertension to more consistently identify and potentially treat or delay progression of chronic kidney disease (CKD) in this high-risk population.
- The developer provides recent citations on the growing rate of CKD diagnoses. They also cite several graded, evidence-based clinical guidelines supporting the measure's numerator action - that is, obtaining estimated Glomerular Filtration Rate (eGFR) and Urine Albumin-Creatinine Ratio (uACR) for patients with diabetes and/or hypertension.
- Performance gaps exist at both the clinician and practice level. Clinician-level performance ranged from 0% to 52.6%, with a median of 22.8%. At the group level, performance ranged from 14.3% to 34.8%, with a median of 24.6%.
- In terms of meaningfulness, the developer convened a Technical Expert Panel (TEP) that included patient and caregiver representation. Patients indicated that they had wished they knew about the their kidney health or diagnosed with CDK earlier to allow themselves to engage in decision-making and make appropriate lifestyle choices.
Limitations:
- Logic model is lacking assumptions and external factors. Logic model activities include patient education materials and "Implementation of quality improvements activities and tools and technology integration." The logic model could be improved by breaking out activities into specific actions that providers and group practices can plausibly implement in the clinical setting.
- The composition of the TEP was not provided (# of patients, # of caregivers, etc.). In addition, the training received by the TEP on quality measurement was not described.
Rationale:
- This new measure is rated as “Not Met, but Addressable" for importance. The developer provides evidence for the growing rate of CKD diagnoses and cites several graded evidence--based reviews supporting the measure's numerator actions (eGFR, uACR). Also, a performance gaps exists with clinician rates ranging from 0% to 52% and group practice rates ranging from 14.3% to 34.8%.
- The submission could be strengthened by expanding upon activities in the logic model and adding assumptions and external factors. Also, additional details about the TEP that was established would be useful including numeric data describing TEP composition (including number of patients and caregivers), and the process for collecting meaningfulness data.
Closing Care Gaps
Strengths:
- The developers provided performance scores based on presence of hypertension, sex, race, Hispanic ethnicity, Payer, and group practice. They found statistically significant differences between subgroups by sex (p=.002), race (p=.03), and group practice sites (p<.0001). The developers also cited empirical literature to demonstrate gaps in care based on Medicare status (Medicare Advantage vs. Fee-for-service) and comorbidities.
Limitations:
- None identified.
Rationale:
- This new measure is rates as "Met" because the developer identified variations in measure scores among subgroups of patients based on sex, race, and location of care. Additional gaps in care based on Medicare status (Medicare Advantage vs. Fee-for-service) and comorbidities were identified through a review of empirical literature.
Feasibility Assessment
Strengths:
- Most data elements required for the measure calculation were deemed accurate, captured in structured fields within the EHR, code to standard terminologies, and collected during routine care.
Limitations:
- Seven data elements were identified as not currently feasible, based on the feasibility scorecard. These were all exclusion criteria and most were associated with hospice and palliative care. The developers provided a means to capture this information using diagnostic codes rather than assessment, encounter, and intervention codes.
Rationale:
- This new measure is rated as "Met" for feasibility because most data elements are readily available in structured fields within the EHR. The 7 variables that were not available were related to hospice and palliative care. The developers were able to use diagnostic code for palliative care and hospice to address this gap in data availability.
Scientific Acceptability
Strengths:
- The developer performed the required reliability testing for this new measure, namely, in the Feasibility Plan they stated how data elements from unstructured fields would be addressed. Data elements from unstructured fields are either captured in other data elements from structured fields, for which reliability is assumed, or the data are not captured in clinics where testing was completed.
- The developer stated that an existing measure (Kidney Health Evaluation - Quality ID #488, CMIT ID 00989-01-E-MIPS) uses the same data element as this new measure.
Limitations:
- Note that accountable entity-level reliability testing is not required for initial endorsement, and is not considered in the rating.
Rationale:
- This new measure is rated as 'Met' for reliability because the developer provided the required evidence for this measure to demonstrate sufficient reliability at the data element-level.
Strengths:
- The developer performed the required testing for this new measure; specifically, they performed data element validity testing for all data elements in the numerator, denominator, and exclusions. The data used for this testing were well described, and included a random sample of 85 patients from two of the six participating clinics that use Epic as their EHR vendor, and the data used for testing were collected January through December 2025.
The developer reported percent agreement and sensitivity between the EHR and the medical record (the "gold standard") for all data elements, and Cohen's kappa and specificity for selected data elements. They reported percent agreement and sensitivity above 95% for all data elements but uACR (88.1% and 73.3%, respectively), indicating generally high agreement, and "excellent" kappa for two denominator data elements, diabetes diagnosis (0.976) and hypertension diagnosis (0.862). - The developer performed face validity testing of the measure using a survey of 23 individuals (4 patients and 19 clinicians) to inquire whether the measure could distinguish good from poor quality and if the specifications are aligned with current evidence. Of the 12 responses (including "at least one" patient), 100% agreed or strongly agreed with both statements.
Limitations:
- A brief explanation for why agreement statistics are lower for uACR than for other data elements would be helpful.
- Table 8 shows a hypertension diagnosis rate of 90.48% in both chart and EHR, but agreement statistics reported seem lower than they should be (e.g., percent agreement is < 100%); indicating there may be an error in the table.
- Finally, specificity could not be calculated for most data elements, which may be because the sample of cases was drawn from patients who all met the numerator and exclusion requirements. This apparent lack of "negative cases" limits the ability of data element testing to demonstrate that the measure as specified can exclude cases that do not belong in the measure.
Rationale:
- This new measure is rated as ‘Not Met But Addressable’ for validity because the validity testing results partially support an inference of validity for the measure, suggesting that the measure somewhat accurately reflects performance on quality and can distinguish good from poor performance to a limited extent.
- The measure's data element validity could be strengthened by explaining the lower uACR agreement statistics relative to other data elements, confirming the accuracy of agreement statistics for the hypertension diagnosis rate (appears to be 100% match but percent agreement is <100%), and providing future testing of specificity.
Use and Usability
Strengths:
- The measure developer describes the target population, accountable entities, and care settings. The measure developer cites multiple CKD measures using in the Merit-based Incentive Payment System (MIPS) and the Healthcare Effectiveness Data and Information Set (HEDIS). The Kidney Health Evaluation for People with Diabetes and/or Hypertension measure complements existing measures and, with the inclusion of hypertension, expands the target population and increases the applicability of the measure across more programs.
The developer noted that the previous version of the measure, "Kidney Health Evaluation" (Quality ID #488, CMIT ID 00989-01-E-MIPS) has had no unexpected events reported and they do not anticipate any unintended consequences as a result of expanding the denominator to include patients with hypertension.
Limitations:
- None identified.
Rationale:
- This new measure is rated as “Met” for use and usability because the developer cited the target population, accountable entities, and care settings. They also described how the measure compliments existing measures and how, with the addition of hypertension, allows for the measure to be used in settings beyond those focused on chronic kidney disease.
Committee Independent Review
support
Importance
Developers present compelling arguments as to why this measure is important. Identification is the first step in appropriate management. This measure will help identify practices and providers who are providing good identification of appropriate patients. Additionally, at the local level, identification of patients who should have been screened, but were not, would be attainable helping to improve care
Closing Care Gaps
Good presentation of data showing gaps in care by categories of patient
Feasibility Assessment
Developers present data showing that data was able to be extracted from EPIC. Data from other EHR systems would be important to show before a national roll out of measure
Scientific Acceptability
It would be good to see if the patients categorized as negative for the conditions and testing were accurate and not just that the ones identified as DM or HTN actually had the disease.
It is unclear which types of clinics are rated by this measure. Is this primary care clinics only? How about surgical specialties. It seems that the assumption is that this measure would be used in primary care, but at a system level, to attain high reliability, all types of providers might need to participate. (In which case, individual providers would not be evaluated due to possible low numbers, but reporting could be at system level) Being explicit about the assumptions on which clinics and what level to report would be helpful.
Use and Usability
Summary
Measure is important and actionable. Reliability is good, but negative reliability should also be tested (ie how often did people with DM or HTN get categorized in the negative denominator). Please be explicit about the types of clinics that should be evaluated by this measure and the level of reporting that is acceptable (system, clinic, provider) What is the minimum number of patients evaluated is needed to have reliable reporting at the individual provider level.
Summary
Importance
The measure addresses a high-prevalence, high-burden condition and is firmly supported by current, high-level evidence from the ADA, KDIGO, and KDOQI. The anticipated impact is well-supported, with strong evidence linking earlier detection to GDMT initiation and meaningful reductions in CKD progression, cardiovascular events, hospitalizations, and cost. A clear performance gap is demonstrated and patient/caregiver input through the TEP confirms meaningfulness to the target population.
Closing Care Gaps
The submission demonstrates meaningful and addressable care gaps. The developer performed appropriate subgroup analyses on their own dataset and identified statistically significant disparities, indicating substantial system-level variation that a process measure of this kind can directly influence. The case for closing care gaps is well-supported.
Feasibility Assessment
Feasibility is partially demonstrated but not fully established. Strengths include strong alignment with the existing MIPS Quality ID #488 (minimizing transition burden), structured capture of 23 of 30 data elements in discrete fields, use of multiple code system combinations for cross-vendor flexibility, no patient-identifiable data requirements, and no reliance on patient surveys. However, feasibility testing was limited to a single EHR vendor (Epic) at only two clinics within one Northeast health system, which does not establish feasibility across the broader range of EHR platforms or in smaller, rural, or safety-net settings — particularly relevant given the equity rationale for the measure. The seven data elements scoring 0 on the feasibility scorecard are not transparently identified or discussed in the narrative, and implementation burden claims are asserted without quantitative support (build hours, workflow time, completeness rates). These gaps are correctable: expanded multi-vendor and small-practice testing, transparent reporting of the low-scoring elements with their alternative capture approach, and quantitative burden/completeness data would move this to fully met. Recommend Not Met but Addressable.
Scientific Acceptability
Scientific acceptability is partially demonstrated. Strengths include appropriate use of the Adams beta-binomial reliability model, excellent group-level reliability (mean 0.971), good data element agreement (88.1%–100%; Kappa 0.729–1.00), and use of both data element and face validity testing. However, individual clinician reliability is marginal (mean 0.69; only 63% of clinicians ≥0.6), with no proposed minimum case threshold for accountability use. The validity sample is small (n=85, 2 of 6 clinics), and the uACR element — the measure's most novel component — showed the lowest agreement (88.1%), Kappa (0.729), and sensitivity (73.3%), suggesting systematic underestimation of true performance. Face validity had only 12 respondents and one patient. The decision to use no risk adjustment or stratification is asserted but not justified despite statistically significant subgroup differences. All testing is from a single Epic-based NYC academic system, limiting generalizability. These gaps are addressable through a proposed minimum case threshold, expanded uACR-focused validity testing, broader face validity, empirical accountable-entity validity, justification for no risk adjustment, and multi-site replication. Recommend Not Met but Addressable.
Use and Usability
The use and usability case is reasonable but incomplete. Strengths include a clear multi-program use strategy, a strong track record with the predecessor measure (#488 in MIPS, MVP, HEDIS, MA Star Ratings), active CMS engagement, and practical guidance for entities (team education, lab leadership engagement, leveraging existing BPAs by adding hypertension ICD-10 codes). However, the expanded measure is "not in use" with no pilot of the hypertension-inclusive version, leaving real-world usability unproven. The unintended consequences analysis is thin and dismissive, despite plausible concerns including over-testing, workflow burden, lab-related uACR artifacts the developer themselves acknowledge, and equity impacts on safety-net providers whose clinics showed the lowest baseline performance (14.3%). No monitoring plan, formal user feedback loop, implementation toolkit, public reporting display strategy, performance benchmarks, or explicit equity-in-reporting approach (e.g., stratified reporting) is described. These gaps are correctable through a pilot, a rigorous unintended consequences analysis with monitoring indicators, an implementation toolkit, a stated feedback mechanism, and equity-in-reporting guidance. Recommend Not Met but Addressable.
Summary
This proposed eCQM, Rate of Annual Kidney Health Evaluation Among Adults with Diabetes and/or Hypertension, expands the existing MIPS Quality ID #488 by adding hypertension to the denominator and is well-aligned with current ADA, KDIGO, KDOQI, and 2025 AHA/ACC guidelines recommending annual eGFR and uACR testing in this high-risk population. The measure addresses a clinically important, high-prevalence condition with a clear performance gap (mean ~24.6%) and demonstrated subgroup disparities by sex, race, and clinic site, supporting strong cases for both Importance (Met) and Closing Care Gaps (Met). However, Feasibility, Scientific Acceptability, and Use and Usability are each rated Not Met but Addressable. Testing was limited to a single Epic-based, NYC academic health system, restricting generalizability across EHR vendors, geographies, and practice types. Individual clinician reliability is marginal (mean 0.69) with no proposed minimum case threshold, and the uACR data element — the measure's most novel component — showed the weakest validity (Kappa 0.729; sensitivity 73.3%), raising concern about systematic underestimation of performance. The expanded measure is not yet in use, with no pilot data, a thin unintended consequences analysis, and no described equity-in-reporting strategy. Overall, the measure has strong clinical foundation and clear improvement potential, but requires expanded testing, methodological strengthening, and a more rigorous implementation plan before full endorsement.
The measure is a good idea,…
Importance
There is clearly a problem with ESRD, and concomitant with it is the problem with early diagnosis. This measure form documents that need. I am less sure that this measure, as written, is important to meeting the identified need.
Closing Care Gaps
The form does not make clear that the measure will achieve early detection. As such, it does not clearly close the gap. It would benefit from clearer articulation of the relationship between meeting the measure criteria and closing the care gap. The developers do a good job of reviewing the literature. They also show the guidelines. Those are good and necessary parts of the submission. They do not complete the work. They do not explain what this measure will accomplish. Assessment is not achievement.
Feasibility Assessment
The attached spreadsheet shows null values for most cells. The document is inadequate for evaluation.
Scientific Acceptability
Among the most important ways to prevent ESRD is weight management, proper nutrition, good food habits, and exercise. This measure does not address any of these. A better measure will move further upstream.
The form provides excellent information on the science and the value of early detection. It provides little connection between them. It assumes that asking questions and drawing blood will prevent ESRD. This is a naive assumption. It lacks referrals, follow-ups, or any reasonable after care consideration.
Use and Usability
Inadequate sample size in a uniquely capable organization. Six clinics using Epic does not support use and usability in rural facilities in the south and west.
Summary
The measure is a good idea, but suffers from naive execution. It needs better data collection from a broader range of facilities. It needs better documentation. It needs better feasibility analysis—or any. Overall, the developers need to bring more to the table.
Logical measure, limited by testing data
Importance
Recommended by multiple professional society recommendations.
Closing Care Gaps
Feasibility Assessment
Single EHR review which seems limited. Nearly all data fields should be easily obtainable using emerging data standards (i.e. FHIR). The data fields with challenges, such as palliative care and hospice care, are challenges inherent to most quality measures with similar exclusion criteria.
Scientific Acceptability
Lower than expected clinician-level reliability.
Main limitation is single EHR system in 2 clinics.
Use and Usability
Summary
Logical, relatively straightforward measure in-line with multiple professional society recommendations. The testing data presented would ideally include more than on EHR.
Very valuable measure
Importance
This is a critical measure and the diagnostic interventions are too often not rendered.....or not followed up. This could save many lives if it is only measured and communicated with the patient. I have heard too many patients say, "it was falling off a cliff" --- no one had told them about their condition or interventions they could take until it was too late -- and they were facing dialysis.
Closing Care Gaps
We know there are care gaps --- and perhaps we can better define Medicaid Care organizations from Medicaid......and hopefully as time goes on, monitor the Medicaid organizations for their testing. In Chicago, 60% of the population on dialysis are black, but blacks make up one third of the Chicago population.....
In addition to the care gaps on kidney disease, I would like to also see the percent of patients that were informed of the results of their tests --- not just if the test was performed.
Feasibility Assessment
As more clinics and non-hospital settings move to electronic collection and reporting, it will be more feasible to collect and manage the data. May be easier said than done, but healthcare is moving toward all digital information for care and payment.
Scientific Acceptability
It can be achieved as the measures are steady and should be a priority for patients with diabetes or hypertension.
My only concern is that we are not doing the full circle and also checking to see if the patient was informed of their results. This can be difficult in certain populations due to lack of phones or internet access,,,,but perhaps a way in which we can know the patient was contacted with the results
Use and Usability
Very important measure from a person, community, population, and financial support perspective.
Public Reporting - Yes
Payment Program - Yes
Quality Improvement with Benchmarking (external benchmarking to multiple organizations) - Yes
Quality Improvement (Internal to the specific organization) - Yes
Summary
This is a way overdue measure --- it can radically change the trajectory of the lives of people that have CKD and don't know it ---- or need to know the interventions to slow down the disese.
Overall, I support this…
Importance
I concur that CKD is a large source of morbidity and mortality to patients, and of extreme expense to the healthcare system. Historically we had very little to prevent progression of CKD, but we now have new medications which can slow progression and conserve nephrons. The health system I work for already agreed to put health maintenance prompts in place to encourage UACR and serum creatinine testing in patients with HTN and CAD, in addition to DM. I agree this is a good measure.
Closing Care Gaps
We often do not improve what we do not measure. I agree that clinicians are more likely to check UACR and CR if they have tools built in their EHR prompting them to do so.
Feasibility Assessment
My biggest concern about feasibility is not the ease with which health systems can build this into their EMRs, but the very large number of patients that will now be due for this testing. When our health system agreed to add a health maintenance modifier to screen hypertensive and CAD patients with UACR and Serum Cr, we chose to use a 3-year lookback window rather than a 1-year one. While we realize that patients can decline more over a longer period, it seemed wise to phase this in.
Scientific Acceptability
The link between proteinuria and CKD progression is already settled. We should start screening for it.
Statistics is not my area of expertise. This measure appropriately targets a marker for CKD that can lead to care modifications that improve patient outcomes.
Use and Usability
The metric is clear, and clinicians are already accustomed to doing this for diabetic patients.
Summary
Overall, I support this metric. My only concern is the large volume of testing this will create, with expense to the healthcare system. I am uncertain if other lookback periods were considered, rather than requiring this yearly.
Important measure - perhaps it can be strengthened
Importance
agree with staff assessment. Need for greater specification of educational and treatment approaches along with provider benefits for adhering to measure and treatment guidelines.
Closing Care Gaps
what are the findings based on age? looks like the data is from the NYC area. Is this representative of all demographics, including rural?
Feasibility Assessment
agree with staff assessment
Scientific Acceptability
agree with staff assessment
agree with staff assessment
Use and Usability
while the measures for evaluation are clear -I wonder about the need to include related SDOH factors which are relevant to patient/family education and ongoing treatment. Even if these SDOH factors can't be addressed, they can be documented and correlated with kidney disease measures so that they can be taken into consideration when treating the while person, and not just the disease
Summary
Agree that this is a very important measure that can improve life span, wellbeing, and save healthcare dollars.
I wonder if the measure could be made even stronger by including an emphasis on the evidence based educational and treatment approaches that need to accompany the evaluation. I also wonder about the value of and need for sensitivity to demographic variables, including age, rural locations, culture, preferred language and to SDOH factors. I also wonder about ways to embed this measure in practice through enhanced provider benefits. And finally, I can't help but wonder about the systemic factors that need to be addressed - either challenged or introduced - to give this measure the traction needed to make a substantial difference.
Important Population Health Measure
Importance
Disagree with staff assessment. As designed, the measure is consistent with the applicable guidelines from the specialty societies in this area. and there is an important performance gap. Although staff are correct to identify that the submitted logic model could be improved with additional detail, the submission includes (in the Impact section) specifics on guideline-directed medical therapy following diagnosis, including specific interventions that would be used in addition to patient education.
Closing Care Gaps
Concur with staff assessment. Gaps and disparities are clearly described.
Feasibility Assessment
Concur with staff assessment. Data elements are available within structured fields in the EHR.
Scientific Acceptability
Concur with staff assessment. Data element testing showed near-perfect kappa for all elements, with one exception at 88%.
Concur with staff assessment. Face validity showed 100% agreement from clinicians and a patient on an expert panel.
Use and Usability
Concur with staff assessment. The developer identifies extant similar measures with different concepts currently in use with no unintended consequences, and intends to have the measure incorporated in MIPS, providing a clear path to use.
Summary
The measure addresses an important and somewhat under-addressed area of population health, although this is rapidly evolving. A number of public comments show a strong consensus for the measure in the field, suggesting that were it to be endorsed and added to MIPS it would see strong uptake. Minor issues with the details of the submission should not affect the endorsement.
Support for Endorsement of CBE #5611e
Importance
I rated this criterion as Met because the measure addresses a significant and well-documented gap in the early identification of chronic kidney disease among individuals with diabetes and hypertension. Expanding the denominator to include patients with hypertension reflects current evidence and clinical guidelines and extends accountability to a high-risk population that has historically been underrepresented in kidney health quality measurement.
While I agree that the submission could be strengthened by providing additional detail regarding the logic model and Technical Expert Panel composition, I view these as opportunities to improve the supporting documentation rather than limitations of the measure's underlying importance. The demonstrated variation in performance across clinicians and practices further supports the opportunity for meaningful quality improvement.
As our population continues to age and life expectancy increases, I would also encourage future evaluation of the upper age limit of 85 years to ensure the measure continues to reflect the needs of older adults who remain active candidates for preventive kidney health evaluation. This consideration does not affect my support for the measure's importance but may be worth revisiting during future maintenance.
Closing Care Gaps
I rated this criterion as Met because the developer clearly identifies meaningful gaps in kidney health evaluation among populations at increased risk for chronic kidney disease. The submission demonstrates variation in performance across demographic and practice characteristics, including sex, race, payer, and practice site, and supplements these findings with published evidence describing disparities in access to recommended kidney health testing.
Expanding the measure to include patients with hypertension is a meaningful enhancement that extends accountability to another high-risk population where screening and early detection remain suboptimal. By identifying populations with lower rates of recommended kidney health evaluation, the measure has the potential to support targeted quality improvement efforts and reduce disparities in care.
I appreciate that the developer incorporated subgroup analyses rather than relying solely on overall performance rates. This approach provides actionable information for clinicians and healthcare organizations seeking to improve equitable delivery of evidence-based kidney health evaluation and supports the measure's ability to meaningfully address existing care gaps.
Feasibility Assessment
I rated this criterion as Met because the developer demonstrated that the majority of required data elements are routinely captured within structured electronic health record fields and are consistent with existing clinical workflows. Leveraging the data infrastructure of an existing endorsed kidney health evaluation measure supports implementation while minimizing additional documentation burden for clinicians.
I appreciate that the developer transparently identified data elements that presented feasibility challenges, particularly those related to hospice and palliative care, and proposed a practical approach using diagnostic coding to improve data capture. Identifying implementation barriers and providing a reasonable mitigation strategy strengthens confidence that the measure can be operationalized in routine practice.
Because this is an electronic clinical quality measure that builds upon an existing measure with similar data elements, I believe the feasibility evidence is sufficient for initial endorsement. Continued implementation across additional health systems and EHR vendors during future maintenance cycles will provide further opportunities to evaluate real-world performance and strengthen the evidence base.
Scientific Acceptability
I rated reliability as Met because the developer provided sufficient evidence that the required data elements can be consistently captured and reported for this electronic clinical quality measure. The measure builds upon an existing endorsed kidney health evaluation measure that relies on the same core data elements, supporting confidence in the reliability of data collection within structured electronic health record workflows.
I also agree that accountable entity-level reliability testing is not required for initial endorsement. Given the measure's foundation on an existing eCQM and the evidence presented for data element reliability, I believe the submission satisfies the expectations for reliability at this stage of endorsement. Continued monitoring during implementation and future maintenance will provide additional opportunities to evaluate performance across a broader range of organizations and electronic health record systems.
I rated validity as Met because the developer presented sufficient evidence that the measure captures the intended clinical concept and appropriately reflects performance for the proposed population. Data element testing demonstrated high agreement across nearly all elements, with substantial to excellent kappa statistics where reported, and face validity testing showed strong agreement among participating clinicians and patients that the measure distinguishes quality performance and aligns with current clinical evidence.
I acknowledge the limitations identified regarding the lower agreement observed for the uACR data element, the inability to calculate specificity for several elements, and the need for clarification of certain reported results. However, I view these as opportunities to strengthen the supporting evidence rather than evidence that the measure lacks validity. Given the overall consistency of the testing results and the measure's expansion of an established kidney health evaluation measure, I believe the evidence is sufficient to support validity for initial endorsement, with additional evaluation appropriately occurring during future maintenance cycles.
Use and Usability
I rated this criterion as Met because the measure provides actionable information that can be used by clinicians, healthcare organizations, and quality improvement programs to improve early identification of chronic kidney disease among patients at increased risk. Expanding the measure to include individuals with hypertension broadens its applicability while maintaining alignment with established clinical practice guidelines and existing quality measurement programs.
I appreciate that the developer described how the measure complements existing kidney health evaluation measures rather than creating a separate or duplicative reporting framework. This approach supports implementation across multiple accountability programs and encourages consistent, evidence-based kidney health evaluation using existing clinical workflows.
As a process measure focused on preventive screening, the results are readily interpretable and can inform targeted quality improvement initiatives at both the clinician and organizational levels. The developer also reported no anticipated unintended consequences from expanding the denominator, further supporting the measure's usability in routine clinical practice. Overall, I believe the measure provides meaningful, actionable information that can support improvements in patient care and population health.
Summary
I support endorsement of this measure because it addresses an important opportunity to improve early identification of chronic kidney disease among patients with diabetes and hypertension. Expanding the measure to include hypertension aligns with current clinical evidence and extends accountability to a high-risk population that has historically been underrepresented in kidney health quality measurement.
Overall, I found the evidence sufficient to support initial endorsement. The measure demonstrates meaningful potential to improve care, close existing gaps in kidney health evaluation, and provide actionable information for clinicians and healthcare organizations. While there are opportunities to strengthen the supporting documentation and expand testing during future maintenance cycles, I do not believe these limitations outweigh the overall value of the measure or preclude endorsement at this stage.
As our population continues to age and life expectancy increases, I encourage the developer to periodically evaluate the upper age limit of 85 years during future maintenance reviews to ensure the measure continues to reflect evolving patient demographics and clinical practice. This observation does not affect my support for endorsement but represents an opportunity for future refinement.
Thank you to the developer and staff for a thorough submission and review.
Overall comments
Importance
Agree with staff preliminary assessment.
Closing Care Gaps
The screening measure is considered now to be the lab tests eGFR and UACR; the numerator for the measure counts either test in the numerator. UACR, which is a newer recommendation, is the one that is missed more often because eGFR is traditionally done with the diabetes or hypertension lab work up. From a workflow standpoint following up on the eGFR to get the follow up UACR done is where clinics have the greatest challenge.
I recommend separate measure indicators for each test, along with an indicator of the percentage of patients who had both tests. This will allow clinics and others to pinpoint exactly where the care gap occurs.
Feasibility Assessment
Agree with staff preliminary assessment.
Scientific Acceptability
Agree with staff preliminary assessment.
Agree with staff preliminary assessment.
Use and Usability
The addition of the patients with hypertension is important for closing care gaps for that population. However, clinic workflows for patients with diabetes and patients with hypertension/heart disease are separate (with a lot of overlap, of course). I recommend separate measures indicators for patients with diabetes and patients with hypertension (w/o diabetes). There are two primary reasons for this:
- Separating patients with diabetes will allow for comparisons to the historical HEDIS measures
- The measure indicators can be more easily “packaged” with other measures related to diabetes or hypertension/heart disease. This is important for value-based payment arrangements that focus on a specific medical conditions.
Summary
The addition of patients with hypertension to this measure closes an important care gap. However, I think this measure would be more useful with the separate indicators that I described in my feedback.
And as always, process measures are important, but it is also important to move towards measuring that appropriate follow-up occurs for patients.
CKD
Importance
This measure is important and should move forward. If there are TEPS for measures moving forward you should require them to provide the makeup of the committee (number of patients, providers, other, etc.) this is a disease state were finding patients/families should not be difficult.
The financial cost of this disease and the emotional strain warrant it.
Closing Care Gaps
I was happy to see the diverse population from gender to race with this measure. While it was just in the state of New York the diversity of clinics and population was nice to see. Not sure how we get rural community representation.
Feasibility Assessment
The more measures that move to easy extraction from EHR's the less pushback we will hear about measure burden.
Scientific Acceptability
Use and Usability
HEDIS and MIPS have CKD measures so this will not be new terrain for providers.
Summary
I am very supportive of this measure particularly because of the support and feedback from the kidney associations and community. I appreciate that industry weighed in too.
From a patient perspective, getting upstream of this disease with this measure will be beneficial to patients and families emotionally and financially.
Very Important Measure
Importance
The developers clearly established a strong rationale for developing the measure.
Closing Care Gaps
The measure has a strong potential to close care gaps. However, there is a need for broader implementation of different types of health and EHR systems to adequately meet this criterion.
Feasibility Assessment
Following the comments I made earlier, the developers need to adequately address concerns related to the items in the 7 data elements scoring 0 on the feasibility scorecard. As others have rightly mentioned, the narrative was cursorily discussed.
Another concern is that the implementation process was not adequately discussed.
Scientific Acceptability
Other key measures are not included in the scorecard.
Issues related to the implementation sites and the number of EHR systems.
Use and Usability
Please refer to my comments about the EHR system.
Summary
This is a timely and important measure with a strong potential to reduce mortality and morbidity. However, concerns about equity, feasibility of implementation in rural clinics, and limited implementation feasibility must be addressed,
Public Comments
Support for Endorsement of CBEID 5611e
Dear Members of the Endorsement and Maintenance (E&M) Committee:
Boehringer Ingelheim Pharmaceuticals, Inc. writes in support of endorsement of CBE #5611e, the Rate of Annual Kidney Health Evaluation among adults with diabetes and/or hypertension. We commend the National Kidney Foundation for advancing a measure that moves kidney care upstream—toward earlier identification of risk—and we support both the measure and its expansion to include adults with hypertension.
Boehringer Ingelheim is a biopharmaceutical company active in both human and animal health. As one of the industry’s top investors in research and development, the company focuses on developing innovative therapies that can improve and extend lives in areas of high unmet medical need. Independent since its foundation in 1885, Boehringer takes a long-term perspective, embedding sustainability along the entire value chain.
The expansion to hypertension closes a measurement gap that current measures leave open
Chronic kidney disease is common, costly, and largely silent: an estimated 90% of adults with CKD are unaware of their condition,1 and the only way to detect it before irreversible loss of function is laboratory testing. A complete kidney health evaluation requires two tests together—a blood test to estimate glomerular filtration rate (eGFR) and a urine test for the albumin-to-creatinine ratio (uACR)—and rates of completing both remain low. Diabetes and hypertension are the two leading contributors to CKD,3 yet the existing endorsed measures—MIPS Quality ID #488 and the NCQA Kidney Health Evaluation for Patients with Diabetes (KED)—capture only the diabetes population.2 Testing tends to be more complete among patients with diabetes, who benefit from these established measures and greater clinical awareness, while adults with hypertension—who are at comparably elevated risk3—remain outside existing accountability framework and are tested less often.
This gap is not theoretical. National data show that complete guideline-recommended kidney health testing (eGFR and uACR) is lowest precisely where this measure would extend—Medicare fee-for-service members with hypertension are tested least often (8.0%), compared with 59.6% for Medicare Advantage members with diabetes.4,5 The 2025 multi-society AHA/ACC hypertension guideline now explicitly recommends uACR testing at the time of a hypertension diagnosis (COR 1),6 and the 2026 AHA/ACC/ADA/ASN cardiovascular-kidney-metabolic (CKM) syndrome guideline reaffirms annual eGFR and uACR assessment for adults in CKM stage 2 or higher (Class 1 recommendation),3 giving the expanded population a firm, current, and guideline-concordant basis. Importantly, when the National Kidney Foundation tested the measure, the share of patients who received both tests (the measure’s performance score) was similar for the hypertension and diabetes populations (23.6% vs. 24.7%),2 indicating that adding hypertension extends accountability to an equally undertested, equally high-risk group without distorting how the measure scores performance.
Endorsement would align measurement across programs and make prevention a shared priority
The value of this measure grows through its synergistic alignment across programs and accountable entities. A common kidney-health evaluation specification—reported at the clinician level (MIPS and MIPS Value Pathways) and the health-plan level (HEDIS and Medicare Advantage Star Ratings),2 and adopted as a quality-improvement target by CMS QIN-QIOs—lets providers, plans, and quality-improvement partners work toward the same evidence-based goal. That alignment reduces reporting burden and, more importantly, reinforces early detection as a core, system-wide priority of care. We encourage the E&M Committee to view endorsement as a step toward this national alignment in kidney health.
The measure performs well against the PQM evaluation rubric
Reviewed against the five PQM measure evaluation rubric domains, the submission is strong, and the areas that may invite discussion are readily addressable rather than reasons to withhold endorsement.
A call to align with national kidney health efforts
Endorsement of CBE #5611e would align the measurement enterprise with the direction of national clinical guidelines and public-health priorities, and would extend accountability to a high-risk population that current measures leave unmeasured. We respectfully encourage the E&M Committee to endorse the measure and, in doing so, to advance a consistent national standard for early kidney health evaluation.
Thank you for the opportunity to comment and for the E&M Committee’s continued work advancing high-quality, evidence-based, patient-centered care.
References
Support for the Rate of Annual Kidney Health Evaluation (5611e)
The Renal Physicians Association (RPA) is the professional organization of nephrologists whose goals are to ensure optimal care under the highest standards of medical practice for patients with kidney disease and related disorders. RPA acts as the national representative for physicians engaged in the study and management of patients with kidney disease. RPA appreciates the opportunity to submit comments on the kidney related quality measures currently under review by the Partnership for Quality Measures.
RPA supports endorsement of the Rate of Annual Kidney Health Evaluation Among Adults with Diabetes and/or Hypertension measure (CBEID 5611e). This measure addresses a well-documented gap in the use of an annual kidney health evaluation for adults at high risk for chronic kidney disease due to its two leading causes, diabetes and hypertension.
CKD is a an underrecognized public health crisis affecting approximately 37 million adults in the United States. Because kidney disease often progresses silently and presents with few signs or symptoms until advanced stages, many individuals remain undiagnosed until irreversible damage has occurred. Adults with diabetes and hypertension are at particularly high risk for CKD and represent populations for whom timely screening and early intervention are critically important, as earlier identification of CKD creates an opportunity for earlier and more effective interventions, including lifestyle modification, risk factor management, and emerging therapies.
Comments
The American Medical Association (AMA) believes that a case minimum greater than 20 should be required when applying this measure at the individual clinician level as a part of endorsement. This minimum would ensure that the measure’s minimum reliability is close to 0.7, which is what we believe should be the standard for endorsed measures.
Support for Endorsement of CBEID 5611e
Please find attached the American Kidney Fund’s formal letter supporting the endorsement of CBEID 5611e.
CBEID 5611-Rate annual Kidney among adults diabetes/hypertension
The Academy of Nutrition and Dietetics is writing to express our strong support for endorsement of the measure, Rate of Annual Kidney Health Evaluation Among Adults with Diabetes and/or Hypertension (CBEID 5611e). This measure addresses a well-documented gap in the use of an annual kidney health evaluation for adults at high risk for chronic kidney disease due to its two leading causes, diabetes and hypertension. Please see the attached letter for an ex[lanation of our position.
Support of CBEID 5611e
Please see attached comments
ASN Comments on CBE ID 5611e
ASN expresses strong support for the endorsement of CBE 5611e: Rate of Annual Kidney Health Evaluation Among Adults with Diabetes and/or Hypertension This measure addresses a well-documented gap in the use of an annual kidney health evaluation for adults at high risk for chronic kidney disease (CKD) due to its two leading causes, diabetes and hypertension.
CKD is an underrecognized public health crisis affecting approximately 37 million adults in the United States. Because kidney disease often progresses silently and presents with few signs or symptoms until advanced stages, many individuals remain undiagnosed until irreversible damage has occurred. Adults with diabetes and hypertension are at particularly high risk for CKD and represent populations for whom timely screening and early intervention are critically important, particularly with the emergence of multiple therapeutic agents, such as ACE inhibitors, angiotensin receptor blockers, and sodium glucose cotransporter 2 inhibitors (SGLT2is), that can slow CKD progression.
Measure performance is based on the percentage of patients aged 18–85 years with a diagnosis of diabetes and/or hypertension who received both an eGFR and uACR during the measurement period. The measure reflects current clinical guideline recommendations by Kidney Disease Improving Global Outcomes (KDIGO), the American Heart Association (AHA) and American College of Cardiology (ACC), the National Kidney Foundation (NKF), and the American Diabetes Association (ADA). Assessment of eGFR and uACR in these populations is the foundation of earlier identification of CKD, more accurate staging and risk assessment, and timely implementation of interventions that can slow disease progression and reduce complications.
Importantly, endorsement of this measure would help drive greater accountability and consistency in kidney health screening practices across healthcare systems. Increased uptake of evidence-based kidney health evaluation will improve screening, encourage diagnosis, support gap closure in CKD detection and treatment, and lower long-term healthcare costs associated with cardiovascular complications and kidney failure.
Measure Comments
Please see comments from the Coalition for Kidney Health attached.
Kind Regards,
Miriam Godwin, on behalf of the Coalition for Kidney Health