- Evaluate why the measure is not widely used and develop implementation guidance to support use of the measure.
- Conduct empirical validity testing at the entity level for both reliability and validity.
Percentage of patients aged 18 years and older with a diagnosis of chronic kidney disease (CKD) (Stages 1-5, not receiving Renal Replacement Therapy (RRT)) and proteinuria who were prescribed ACE inhibitor or ARB therapy within a 12-month period.
This measure is NQF Measure 1662/MIPS Measure 489.
-
-
1.5 Measure Type1.6 Composite MeasureNo1.7 Electronic Clinical Quality Measure (eCQM)1.8 Level Of Analysis1.9 Care Setting1.10 Measure Rationale
This measure is aimed at increasing the number of patients with CKD and proteinuria who are prescribed ACE inhibitor or ARB therapy. ACE inhibitors and ARBs are preferred agents for diabetic kidney disease and nondiabetic kidney diseases with proteinuria (albuminuria), even in the absence of hypertension. In these diseases, ACE inhibitors and ARBs lower blood pressure, reduce proteinuria (albuminuria), slow the progression of kidney disease, and likely reduce cardiovascular disease risk by mechanisms in addition to lowering blood pressure. These benefits have been shown across high quality, multi-center, randomized controlled trials such as RENAAL (Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan) (Brenner et al., New England Journal of Medicine, 2001). A meta-analysis of randomized trials showed that ACEi/ARB therapy lowered the odds of kidney failure (also known as end-stage renal disease [ESRD]) by 30-39 percent and of cardiovascular disease events by 18 percent-24 percent (Xie et al., Am J Kidney Dis, 2016). In a meta-analysis including primarily diabetic patients with proteinuria, use of ACEi/ARB therapy had a 0.36 to 0.78 odds of incident kidney failure (Cai et al., Nephrology, dialysis, transplantation, 2018). Similarly, in a Cochrane meta-analysis, patients with early (stage 1 to 3) non-diabetic CKD who were treated versus not treated with ACEi/ARB had 31 percent lower risk of kidney failure (Jafar et al., Annals of internal medicine, 2001). Based upon this robust evidence, ACE inhibitors and ARBs are recommended for patients with CKD and proteinuria by the Kidney Disease: Improving Global Outcomes (KDIGO) international guidelines and the Kidney Disease Outcomes Quality Initiative.
CKD is a major public health problem; a total of 37 million Americans have CKD. There is a clear performance gap in ACE inhibitor and ARB usage among patients with CKD, with only 40 percent of CKD patients receiving an ACEi/ARB in NHANES data (Murphy et al., JASN, 2019). Population health efforts to increase the use of ACEi/ARB in American Indians and Alaska Natives have been associated with a decrease in incident kidney failure related to diabetic kidney disease (Bullock et al., MMWR Morbidity and mortality weekly report, 2017). In summary, this measure is a central component of high-quality nephrology care, as ACE inhibitors and ARBs decrease the rate of kidney failure, cardiovascular outcomes, and mortality in patients with CKD and proteinuria.
References:
Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH, Remuzzi G, Snapinn SM, Zhang Z, Shahinfar S, et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345(12):861–9.
Xie X, Liu Y, Perkovic V, Li X, Ninomiya T, Hou W, Zhao N, Liu L, Lv J, Zhang H, Wang H. Renin-Angiotensin System Inhibitors and Kidney and Cardiovascular Outcomes in Patients With CKD: A Bayesian Network Meta-analysis of Randomized Clinical Trials. Am J Kidney Dis. 2016 May;67(5):728-41. doi: 10.1053/j.ajkd.2015.10.011. Epub 2015 Nov 18. PMID: 26597926.
Cai J, Huang X, Zheng Z, Lin Q, Peng M, Shen D. Comparative efficacy of individual renin–angiotensin system inhibitors on major renal outcomes in diabetic kidney disease: a network meta-analysis. Nephrol Dial Transplant. 2018; 33(11):1968–1976. doi: 10.1093/ndt/gfy001
Jafar TH, Schmid CH, Landa M, et al. Angiotensin-Converting Enzyme Inhibitors and Progression of Nondiabetic Renal Disease: A Meta-Analysis of Patient-Level Data. Ann Intern Med. 2001;135:73-87. [Epub 17 July 2001]. doi:10.7326/0003-4819-135-2-200107170-00007
Murphy DP, Drawz PE, Foley RN. Trends in Angiotensin-Converting Enzyme Inhibitor and Angiotensin II Receptor Blocker Use among Those with Impaired Kidney Function in the United States. J Am Soc Nephrol. 2019 Jul;30(7):1314-1321. doi: 10.1681/ASN.2018100971.
Bullock A, Burrows NR, Narva AS, et al. Vital Signs: Decrease in Incidence of Diabetes-Related End-Stage Renal Disease among American Indians/Alaska Natives — United States, 1996–2013. MMWR Morb Mortal Wkly Rep 2017;66:26-32. DOI: 10.15585/mmwr.mm6601e1.
1.11 Measure Webpage1.20 Testing Data Sources1.25 Data SourcesData is contained in patient chart; no specialized collection instrument is needed. Entity will review CPT codes, ICD-10 codes and lab values to calculate the measure.
-
1.14 Numerator
Patients who were prescribed ACE inhibitor or ARB therapy within a 12-month period.
Definition:
Prescribed – May include prescription given to the patient for ACE Inhibitor or ARB therapy OR patient already taking ACE Inhibitor or ARB therapy as documented in the current medication list.
1.14a Numerator DetailsPatients who were prescribed ACE inhibitor or ARB therapy within a 12-month period
Definition:
Prescribed – May include prescription given to the patient for ACE Inhibitor or ARB therapy OR patient already taking ACE Inhibitor or ARB therapy as documented in the current medication list.
Numerator Options:
Performance Met: ACE Inhibitor (ACE-I) or ARB therapy prescribed during the measurement period (M1200)
OR
Denominator Exception: Documentation of medical reason(s) for not prescribing ACE inhibitor (ACE-I) or ARB therapy during the measurement period (e.g., pregnancy, history of angioedema to ACE-I, other allergy to ACE-I and ARB, hyperkalemia or history of hyperkalemia while on ACE-I or ARB therapy, acute kidney injury due to ACE-I or ARB therapy, other medical reasons.) (M1201)
OR
Denominator Exception: Documentation of patient reason(s) for not prescribing ACE inhibitor or ARB therapy during the measurement period (e.g., patient declined, other patient reasons). (M1202)
OR
Performance Not Met:
ACE inhibitor or ARB therapy not prescribed during the measurement period, reason not given. (M1203)
-
1.15 Denominator
All patients aged 18 years and older with the diagnosis of CKD (Stages 1-5, not receiving RRT) and proteinuria.
Definitions:
Proteinuria:
1. >300mg of albumin in the urine per 24 hours OR
2. Urine albumin-to-creatinine ratio (ACR) >300 mg/g OR
3. Urine protein-to-creatinine ratio (PCR) > 0.3 g/g
Renal Replacement Therapy (RRT) – For the purposes of this measure, RRT includes hemodialysis, peritoneal dialysis, and kidney transplantation.
1.15a Denominator DetailsAll patients aged 18 years and older with the diagnosis of CKD (CKD Stages 1-5, not receiving RRT) and proteinuria.
Definitions:
Proteinuria:
1. >300mg of albumin in the urine per 24 hours OR
2. Urine albumin-to-creatinine ratio (ACR) >300 mg/g OR
3. Urine protein-to-creatinine ratio (PCR) > 0.3 g/g
Renal Replacement Therapy (RRT) – For the purposes of this measure, RRT includes hemodialysis, peritoneal dialysis, and kidney transplantation.
Patients receiving RRT –
The following codes would be sufficient to define the Denominator Exclusion (M1199) of receiving RRT: 90951, 90952, 90953, 90954, 90955, 90956, 90957, 90958, 90959, 90960, 90961, 90962, 90963, 90964, 90965, 90966, 90967, 90968, 90969, 90970, I70.1, N18.6, Z49.31, Z49.32, Z99.2
Denominator Criteria (Eligible Cases):
All patients aged 18 years and older on the date of the encounter.AND
Diagnosis of CKD (Stages 1-5) (ICD-10-CM): E11.22, N18.1, N18.2, N18.30, N18.31, N18.32, N18.4, N18.5, N18.9
AND
Diagnosis of Proteinuria (ICD-10-CM): R80.1, R80.8, R80.9
AND
Patient encounter during the performance period (CPT): 99202, 99203, 99204, 99205, 99212, 99213, 99214, 99215, 99304, 99305, 99306, 99307, 99308, 99309, 99310, 99341, 99342, 99344, 99345, 99347, 99348, 99349, 99350
AND NOTDENOMINATOR EXCLUSION:
Patients receiving RRT: M1199
-
1.15b Denominator Exclusions
Patients receiving RRT: M1199.
The following codes would be sufficient to define the Denominator Exclusion (M1199) of receiving RRT: 90951, 90952, 90953, 90954, 90955, 90956, 90957, 90958, 90959, 90960, 90961, 90962, 90963, 90964, 90965, 90966, 90967, 90968, 90969, 90970, I70.1, N18.6, Z49.31, Z49.32, Z99.2.
1.15c Denominator Exclusions DetailsThe following codes would be sufficient to define the Denominator Exclusion (M1199) of receiving RRT: 90951, 90952, 90953, 90954, 90955, 90956, 90957, 90958, 90959, 90960, 90961, 90962, 90963, 90964, 90965, 90966, 90967, 90968, 90969, 90970, I70.1, N18.6, Z49.31, Z49.32, Z99.2.
-
OLD 1.12 MAT output not attachedAttached1.13a Data dictionary not attachedNo1.16 Type of Score1.17 Measure Score InterpretationBetter quality = Higher score1.18 Calculation of Measure Score
- Start with Denominator.
- Check All patients aged 18 years and older on the date of the encounter:
a. If All patients aged 18 years and older on the date of the encounter equals No, do not include in Eligible Population/Denominator. Stop processing.
b. If All patients aged 18 years and older on the date of encounter equals Yes, proceed to check Diagnosis of CKD (Stages 1-5) as listed in Denominator. - Check Diagnosis of CKD (Stages 1-5) as listed in Denominator:
a. If Diagnosis of CKD (Stages 1-5) as listed in Denominator equals No, do not include in Eligible Population/Denominator. Stop processing.
b. If Diagnosis of CKD (Stages 1-5) as listed in Denominator equals Yes, proceed to check Diagnosis of Proteinuria. Check Diagnosis of Proteinuria:
a. If Diagnosis of Proteinuria equals No, do not include in Eligible Population/Denominator. Stop processing.
b. If Diagnosis of Proteinuria equals Yes, proceed to check Patient encounter during the performance period as listed in Denominator.5. Check Patient encounter during the performance period as listed in Denominator:
a. If Patient encounter during the performance period as listed in Denominator equals No, do not include in Eligible Population/Denominator. Stop processing.
b. If Patient encounter during the performance period as listed in Denominator equals Yes, proceed to check Patients receiving RRT.6. Check Patients receiving RRT:
a. If Patients receiving RRT equals Yes, do not include in Eligible Population/Denominator. Stop processing.
b. If Patients receiving RRT equals No, include in Eligible Population/Denominator.7. Denominator Population: Denominator Population is all Eligible Patients in the Denominator. Denominator is represented as Denominator in the Sample Calculation (attached). Letter d equals 80 patients in the Sample Calculation.
8. Start Numerator
9. Check ACE Inhibitor (ACE-I) or ARB therapy prescribed during the measurement period:
a. If ACE Inhibitor (ACE-I) or ARB therapy prescribed during the measurement period equals Yes, include in Data Completeness Met and Performance Met. Data Completeness Met and Performance Met letter is represented as Data Completeness and Performance Rate in the Sample Calculation (attached). Letter a equals 40 patients in the Sample Calculation.
b. If ACE Inhibitor (ACE-I) or ARB therapy prescribed during the measurement period equals No, proceed to check Documentation of medical reason(s) for not prescribing ACE inhibitor (ACE-I) or ARB therapy during the measurement period.
10. Check Documentation of medical reason(s) for not prescribing ACE inhibitor (ACE-I) or ARB therapy during the measurement period:
a. If Documentation of medical reason(s) for not prescribing ACE inhibitor (ACE-I) or ARB therapy during the measurement period equals Yes, include in Data Completeness Met and Denominator Exception. Data Completeness Met and Denominator Exception letter is represented as Data Completeness and Performance Rate in the Sample Calculation l. Letter b1 equals 10 patients in the Sample Calculation.b. If Documentation of medical reason(s) for not prescribing ACE inhibitor (ACE-I) or ARB therapy during the measurement period equals No, proceed to check Documentation of patient reason(s) for not prescribing ACE inhibitor or ARB therapy during the measurement period.
11. Check Documentation of patient reason(s) for not prescribing ACE inhibitor or ARB therapy during the measurement period:
a. If Documentation of patient reason(s) for not prescribing ACE inhibitor or ARB therapy during the measurement period equals Yes, include in Data Completeness Met and Denominator Exception. Data Completeness Met and Denominator Exception letter is represented as Data Completeness and Performance Rate in the Sample Calculation. Letter b2 equals 0 patients in the Sample Calculation.
b. If Documentation of patient reason(s) for not prescribing ACE inhibitor or ARB therapy during the measurement period equals No, proceed to check ACE inhibitor or ARB therapy not prescribed during the measurement period, reason not given.12. Check ACE inhibitor or ARB therapy not prescribed during the measurement period, reason not given:
a. If ACE inhibitor or ARB therapy not prescribed during the measurement period, reason not given equals Yes, include in Data Completeness Not Met and Performance Not Met. Data Completeness Met and Performance Not Met letter is represented as Data Completeness in the Sample Calculation listed at the end of this document. Letter c equals 20 patients in the Sample Calculation.b. If ACE inhibitor or ARB therapy not prescribed during the measurement period, reason not given equals No, proceed to check Data Completeness Not Met.
13. Check Data Completeness Not Met: If Data Completeness Not Met, the Quality Data Code or equivalent was not submitted. 10 patients have been subtracted from the Data Completeness Numerator in the Sample Calculation.
Sample Calculations
• Data Completeness equals Performance Met (a equals 40 patients) plus Denominator Exception (b1 + b2 = 10 patients) plus Performance Not Met (c equals 20 patients) divided by Eligible Population / Denominator (d equals 80 patients). All equals 70 patients divided by 80 patients. All equals 87.50 percent.
• Performance Rate equals Performance Met (a equals 40 patients) divided by Data Completeness Numerator (70 patients) minus Denominator Exception (b1 + b2 = 10 patients). All equals 40 patients divided by 60 patients. All equals 66.67 percent.
1.18a Attach measure score calculation diagram, if applicable1.19 Measure Stratification DetailsThis measure is not stratified.
1.26 Minimum Sample SizeThe CMS MIPS program requires performance data for at least 70% of the denominator eligible cases for each quality measure in order to achieve data completeness.
-
Most Recent Endorsement ActivityAdvanced Illness and Post-Acute Care Fall 2023Initial EndorsementLast Updated
-
StewardRenal Physicians AssociationMeasure Developer Secondary Point Of Contact
Adonia Calhoun Groom
Renal Physicians Association
1700 Rockville Pike, Ste 320
Rockville , MD 20852
United StatesMeasure Developer Secondary Point Of Contact Email
-
-
-
2.1 Attach Logic Model2.2 Evidence of Measure Importance
Clinical practice guidelines support the use of ACE and ARB in CKD patients not on RRT.
The Kidney Disease Improving Global Outcomes (KDIGO) 2012 guidelines for the evaluation and management of CKD recommend that “an ARB or ACE-I be used in both diabetic and non-diabetic adults with CKD and urine albumin excretion >300 mg/24 hours (or equivalent)” (Recommendation 3.1.7, 1B). Guideline available at https://kdigo.org/wp-content/uploads/2017/02/KDIGO_2012_CKD_GL.pdf
The KDIGO 2021 Clinical Practice Guideline on the Management of Blood Pressure (BP) in CKD recommends “starting renin-angiotensin-system inhibitors (RASi) (ACEi or ARB) for people with high BP, CKD, and severely increased albuminuria (G1–G4, A3) without diabetes” and “for people with high BP, CKD, and moderately-to-severely increased albuminuria (G1–G4, A2 and A3) with diabetes” (Recommendations 3.2.1 and 3.2.3, 1B). Guideline available at https://kdigo.org/wp-content/uploads/2016/10/KDIGO-2021-BP-GL.pdf.
This measure was rated as HIGH for Overall Measure Validity in Mendu ML, Tummalapalli SL, Lentine KL, Erickson KF, Lew SQ, Liu F, Gould E, Somers M, Garimella PS, O'Neil T, White DL, Meyer R, Bieber SD, Weiner DE. Measuring Quality in Kidney Care: An Evaluation of Existing Quality Metrics and Approach to Facilitating Improvements in Care Delivery. J Am Soc Nephrol. 2020 Mar;31(3):602-614. doi: 10.1681/ASN.2019090869. Epub 2020 Feb 13. PMID: 32054692; PMCID: PMC7062216.
-
2.6 Meaningfulness to Target Population
Members of the American Society of Nephrology (ASN) Quality Committee performed an environmental scan of existing kidney quality metrics. To assess the measures’ validity, we conducted two rounds of structured metric evaluation, on the basis of the American College of Physicians criteria: importance, appropriate care, clinical evidence base, clarity of measure specifications, and feasibility and applicability. ASN examined 60 quality metrics, including seven for CKD prevention, two for slowing CKD progression, two for CKD management, one for advanced CKD and kidney replacement planning, 28 for dialysis management, 18 for broad measures, and two patient-reported outcome measures. The ACEi/ARB measure was the only measure related to evidence-based medications that prevent kidney failure, cardiovascular events, and death in patients with CKD. There are no other metrics related to ACEi/ARB use for the CKD population currently in the CMS Measures Inventory Tool.
This measure was rated as HIGH for Overall Measure Validity in Mendu ML, Tummalapalli SL, Lentine KL, Erickson KF, Lew SQ, Liu F, Gould E, Somers M, Garimella PS, O'Neil T, White DL, Meyer R, Bieber SD, Weiner DE. Measuring Quality in Kidney Care: An Evaluation of Existing Quality Metrics and Approach to Facilitating Improvements in Care Delivery. J Am Soc Nephrol. 2020 Mar;31(3):602-614. doi: 10.1681/ASN.2019090869. Epub 2020 Feb 13. PMID: 32054692; PMCID: PMC7062216.
-
Table 1. Performance Scores by Decile
Performance Gap Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum Mean Performance Score see note above N of Entities see note above N of Persons / Encounters / Episodes see note above
-
-
-
3.1 Feasibility Assessment
Not applicable during the Fall 2023 cycle.
3.3 Feasibility Informed Final MeasureThe measure was not changed as a result of the feasibility assessment as it was found to be feasible as specified.
-
3.4a Fees, Licensing, or Other Requirements
No fees are charged by the Renal Physicians Association for use of this measure.
CPT codes in the measure are copyright of the American Medical Association.3.4 Proprietary InformationProprietary measure or components (e.g., risk model, codes), without fees
-
-
-
4.1.3 Characteristics of Measured Entities
The RPA, AMA and IFMC members of the team mutually agreed that the testing site model would consist of a nephrology practice alpha site local to IFMC and three sites distributed geographically across the United States of various practice sizes and medical record types (electronic vs. paper). Potential nephrology practice sites were contacted by an IFMC representative for the purpose of soliciting participation in measure testing activities. The criteria above were paramount; however, selection was subject to willingness by the practice to participate.
RPA provided a list of their Practice Managers Committee containing names and contact information. From this list, three clinics were chosen based on the criteria discussed above. The desired geographic mix was attained. The alpha site was located in the upper Midwest, one test site in the East, one in the South, and one in the Western United States.Four nephrology practice sites representing various types, locations and sizes were identified to participate in testing the measures:
• The number of nephrologists per site ranged from 5-62 physicians.
• The sites were located in four different regions: Midwestern, Western, Eastern, and Southern.
• Patient visit volume ranged from 60-2250 CKD patients seen per month.Sample selection: Data were collected from the medical records of the first up to 35 CKD patients on each type of dialysis seen at each site after July 1, 2007. The measurement period was July 1, 2007-June 30, 2008.
Sample size per physician organization ranged from 24-30 for a total of 112 CKD patients.
4.1.1 Data Used for TestingThe measure was developed by the Renal Physicians Association (RPA) and the American Medical Association (AMA)-convened Physician Consortium for Performance Improvement (Consortium). The AMA requested that the Iowa Foundation for
Medical Care (IFMC) perform on-site validity and feasibility testing of the measure. In addition, IFMC was asked to compare the claims information submitted for the Physician Quality Reporting Initiative (PQRI) to the medical record documentation in
support of each measure submitted for PQRI payment. The objective of the feasibility and implementation testing was to assess the feasibility of the collection, the measurement, and reporting of the data while also monitoring the costs of doing so. Reliability
testing was also performed to determine whether the specifications, including the data definitions prepared by us in collaboration with RPA/AMA, resulted in consistent measurements. In addition, IFMC tested the reliability of PQRI submissions on claims when compared to medical record documentation. Another objective was to perform testing in a variety of environments,
i.e., electronic health record (EHR), paper medical record and administrative/claims data-based measurement.A measure testing protocol was drafted by IFMC and vetted through the AMA. For reliability testing of the measures two IFMC staff performed on-site manual data collection on the same medical records to determine if the measures could be collected reliably. To explore whether electronic capture of all necessary data elements to compute each measure was inherent in the EHR-based sites, a follow up grid was sent to each EHR site requesting this detailed information. Information obtained for each element included whether the data element was located in a discrete field in the EHR and whether that field was in a standard codified format.
A sample of patients > 18 years of age was to be pulled by each site of the first 35 patients seen in each category using a start date of July 1, 2007. Each sample was oversampled by five patients in an effort to ensure a remaining sample of 30
patients in each category from each site.Four nephrology practice sites representing various types, locations and sizes were identified to participate in testing the measures:
• The number of physicians per site ranged from 5-62 physicians
• The sites were located in four different regions: Midwestern, Western, Eastern, and Southern
• Patient visit volume ranged from 60-2250 CKD patients seen per monthSample size per physician organization ranged from 24-30 (as shown below) for a total of 112 CKD patients
• Site 1: 24 CKD patients
• Site 2 : 29 CKD patients
• Site 3 : 29 CKD patients
• Site 4 : 30 ESRD patients (30 PD patients, 30 HD patients)
• Sample selection: Data were collected from the medical records of the first up to 35 CKD patients on each type of dialysis seen at each site after July 1, 2007. The measurement period was July 1, 2007-June 30, 2008.
• Data abstraction was performed in 2008.Sampling Method
- To arrive at a sample of 30 records per condition/per site, we will over-sample for 35 records.
- For practices participating in CMS’ Physician Quality Reporting Initiative (PQRI), the sample will be drawn from a population of patients for whom: 1) a CPT II code was submitted on a claim in 2008, AND 2) the patients had at least 2 office visits with the nephrologist in the 2007 calendar year.
- For sites that are not participating in PQRI, the sample with be drawn from a patient population in which patients had at least 2 office visits with the nephrologist in the 2007 calendar year.
- IFMC will provide one of the following sampling methodologies to the practice sites:
- Identify records for 35 patients (with CKD , not on RRT) whose Social Security number ends in a specific number, i.e., 2 and 4
OR
- Identify records for the first 35 patients seen during the first month of the timeframe used for testing (this method would help assure that the sampled patients would potentially have 12 months of data for review).
The nephrology office sites were visited by a two-person abstractor team to conduct feasibility and reliability testing. The two abstractors individually abstracted each medical record, compared the results, and evaluated the mismatches.
4.1.4 Characteristics of Units of the Eligible PopulationThis measure is not stratified. Data testing included gender, age and CKD stage.
4.1.2 Differences in DataThe same sample was used for all testing.
-
4.2.1 Level(s) of Reliability Testing Conducted4.2.2 Method(s) of Reliability Testing
Data abstracted from patient records were used to calculate inter-rater reliability for the measure.
Patients were randomly selected from visits for CKD.
Data analysis included:
• Percent agreement
• Kappa statistic with 95% confidence interval to adjust for chance agreement
Cohen's kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation since κ takes into account the agreement occurring by chance.
4.2.3 Reliability Testing ResultsThe statistical results from reliability testing:
Measure (N, % Agreement, Kappa ( 95% Confidence Interval))ACE Inhibitor or ARB Therapy Measure (73, 93.15%, 0.8047 (0.6395- 0.9699).
4.2.4 Interpretation of Reliability ResultsThis measure is highly reliable, as shown in results from the inter-abstrator analysis.
-
4.3.1 Level(s) of Validity Testing Conducted4.3.2 Type of accountable entity-level validity testing conducted4.3.3 Method(s) of Validity Testing
Face Validity
An expert panel was used to assess face validity of the measure. This panel consisted of 21 members, with representation from the following specialties: nephrology, pediatric nephrology, endocrinology, nursing, methodology, internal medicine, preventive medicine and family medicine.
Face validity of the measure score as an indicator of quality was systematically assessed as follows:
After the measure was fully specified, the expert panel was asked to rate their agreement with the following statement:
Please rate your agreement with the following statement for each measure- the scores obtained from the measure as specified will accurately differentiate quality across providers.
Scale 1-5, where 1=Strongly Disagree; 3=Neither Disagree nor Agree; 5=Strongly Agree
Additional Review
In 2019, the American Society of Nephrology’s (ASN) Quality Committee performed a systematic compilation and evaluation of national kidney metrics. To assess the measures’ validity, the ASN Quality Committee conducted two rounds of structured metric evaluation, on the basis of the American College of Physicians criteria: importance, appropriate care, clinical evidence base, clarity of measure specifications, and feasibility and applicability. Quality metrics were included if they met the following criteria: (1) had defined numerator, denominator, and exclusion criteria; (2) were physician-directed measures relevant to the care of kidney disease patients or were classified as a kidney disease metric by the organization; and (3) were published or endorsed by an organization recognized nationally for quality metrics, as opposed to single health system or organization-specific metrics. ASN did not include clinical practice guidelines without specified numerator and denominator parameters. We organized these metrics on the basis of applicability across the spectrum of kidney disease care delivery: CKD prevention, slowing CKD progression, CKD management, advanced CKD and kidney replacement planning, and dialysis management. ASN also classified broad measures as applying across the spectrum of kidney disease care and metrics that were patient reported outcome measures (PROMs).
Ratings were completed similar to the approach outlined by the ACP Performance Measurement Committee, utilizing the RAND Corporation/University of California, Los Angeles (UCLA) method of evaluating a medical intervention. As a part of their effort to review existing performance measures, the Performance Measurement Committee of the ACP developed and applied five criteria to evaluate measures included in the Quality Payment Program.
- Importance: The metric will lead to measurable and meaningful improvement in clinical outcome or there is an opportunity for improvement.
- Appropriate care: The metric will stem overuse or underuse of a test or treatment.
- Clinical evidence base: The metric is on the basis of high-quality and high-quantity evidence, and has consistent data representing clinical knowledge.
- Measure specifications: The metric has clarity (a clearly defined numerator and denominator), validity, reliability, and appropriate risk adjustment.
- Feasibility and applicability: The metric is under the influence of the individual or entity being assessed, attribution level is appropriate, data collection is feasible and burden acceptable, and results will help improve care.
ACP criteria as well as consideration of unintended consequences were applied to all measures to evaluate validity. Validity is defined by this methodology as “the measure is correctly assessing what it is designed to measure, adequately distinguishing good and poor quality.” Two rounds of metric evaluation were conducted by 11 members of the ASN Quality Committee. In the first round, members evaluated the measures independently in spring of 2019; the second round of ratings was conducted during an in-person meeting in July 2019, using a formal group process, with a senior committee member (D.E.W.) serving as moderator. After a group discussion of each measure, members rated the ACP criteria on a 9-point scale, with 1 to 3 indicating “does not meet criteria,” 4 to 6 “meets some criteria,” and 7 to 9 “meets criteria.” Each measure also received an overall high/medium/low rating. The overall metric ratings were unchanged from round one to two, and there was strong correlation between domain criteria ratings and overall ratings (see Supplemental Figure 1). Intraclass correlation coefficients (ICCs) showed a moderate correlation among overall ratings (ICC=0.68) and ACP domain ratings (ICC range: 0.59–0.82) (see Supplemental Figure 2 and Supplemental Table 2). Individual comments were collected from the first round of ratings, and group comments were collected from the second round. After the individual metrics were rated for validity, a subset of the committee (M.L.M., S.L.T., D.E.W., and S.D.B.) organized them into subcategories and globally assessed the scope and attribution of the metrics. Please see Supplemental Figures and full article from the Journal of the American Society of Nephrology (JASN) available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062216/.
4.3.4 Validity Testing ResultsFace Validity
The statistical results of validity testing were:
The results of the expert panel rating of the validity statement were as follows:
N = 19; Mean rating = 4.47
Frequency Distribution of Ratings
1 - 0 (Strongly Disagree)
2 - 0
3 - 0 (Neither Disagree nor Agree)
4 - 10
5 - 9 (Strongly Agree)
Additional Review by ASN Quality Committee
Median and interquartile ranges (IQR) of ACP domain ratings.
ACP 1: Importance Median [IQR]: 9 [9-9]
ACP 2: Appropriateness Median [IQR]: 9 [8-9]
ACP 3: Clinical Evidence Median [IQR]: 8 [7-9]
ACP 4: Specifications Median [IQR]: 5 [4-6]
ACP 5: Feasibility Median [IQR]: 6 [5-8]
This measure was rated as HIGH for Overall Measure Validity by the ASN Quality Committee.
4.3.5 Interpretation of Validity ResultsThese results indicate that face validity of the measure score as an indicator of quality was consistent.
-
4.4.1 Methods used to address risk factors4.4.1b If an outcome or resource use measure is not risk adjusted or stratified
This is a process measure. It is not risk adjusted.
4.4.4 Risk Adjustment Modeling and/or Stratification ResultsThis measure is not risk stratified.
Risk adjustment approachOffRisk adjustment approachOffConceptual model for risk adjustmentOffConceptual model for risk adjustmentOff
-
-
-
5.1 Contributions Towards Advancing Health Equity
Chronic kidney disease (CKD) is a major public health problem; a total of 37 million Americans have CKD. There is a clear performance gap in ACE inhibitor and ARB usage among patients with CKD, with only 40% of CKD patients receiving an ACEi/ARB in NHANES data (Murphy et al., JASN, 2019) [4]. Population health efforts to increase the use of ACEi/ARB in American Indians and Alaska Natives have been associated with a decrease in incident kidney failure related to diabetic kidney disease (Bullock et al., MMWR Morbidity and mortality weekly report, 2017) [5]. In summary, this measure is a central component of high-quality nephrology care, as ACE inhibitors and ARBs decrease the rate of kidney failure, cardiovascular outcomes, and mortality in patients with CKD and proteinuria.
- Xie X, Liu Y, Perkovic V, et al. Renin-Angiotensin System Inhibitors and Kidney and Cardiovascular Outcomes in Patients With CKD: A Bayesian Network Meta-analysis of Randomized Clinical Trials. Am J Kidney Dis 2016;67:728-41.
- Cai J, Huang X, Zheng Z, Lin Q, Peng M, Shen D. Comparative efficacy of individual renin-angiotensin system inhibitors on major renal outcomes in diabetic kidney disease: a network meta-analysis. Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 2018;33:1968-76.
- Jafar TH, Schmid CH, Landa M, et al. Angiotensin-converting enzyme inhibitors and progression of nondiabetic renal disease. A meta-analysis of patient-level data. Annals of internal medicine 2001;135:73-87.
- Murphy DP, Drawz PE, Foley RN. Trends in angiotensin-converting enzyme inhibitor and angiotensin II receptor blocker use among those with impaired kidney function in the United States. Journal of the American Society of Nephrology. 2019 Jul 1;30(7):1314-21.
- Bullock A, Burrows NR, Narva AS, et al. Vital Signs: Decrease in Incidence of Diabetes-Related End-Stage Renal Disease among American Indians/Alaska Natives - United States, 1996-2013. MMWR Morbidity and mortality weekly report 2017;66:26-32. PMC5687264.
African-Americans have the highest reported prevalence and incidence of treated ESRD. Overall, African-Americans are four times more likely to progress to ESRD compared to whites (988 vs. 254 patients per million) and at a higher-than-average risk for developing ESRD in the Southeastern US. Diabetes Mellitus (DM) is the leading cause of ESRD in all racial and ethnic groups, but occurs at a much higher rate among African-Americans, Hispanics and Native Americans (422,382.9, and 307.2 vs. 115 per million, respectively) compared to whites. In addition, African-Americans have the highest rate of hypertension-related ESRD, which far exceeds other racial and ethnic groups. As a result, hypertension remains a close second to DM as the leading cause of ESRD in the African-American community.(1)
In individuals with early CKD, African American women (odds ratio [OR], 1.47; 95% confidence interval [CI], 1.14 to 1.88), white men (OR, 1.85; 95% CI, 1.39 to 2.46), and white women (OR, 1.69; 95% CI, 1.28 to 2.22) had greater odds of hypertension control (blood pressure <130/80 mm Hg) than African American men. In individuals with late CKD, white men (OR, 1.66; 95% CI, 1.10 to 2.52) and white women (OR, 1.67; 95% CI, 1.13 to 2.46) had greater odds of hypertension control than African American men. No differences were seen between African American men and women with late CKD.[6]
In the United States, the incidence of ESRD from hypertensive CKD in African American men is 5 times that in white men and 1.4 times that in African American women.[7]6. Alves TP, Lewis, J. Racial differences in chronic kidney disease (CKD) and end-stage renal disease (ESRD) in the United States: a social and economic dilemma. Clinical Nephrology.2010;74(1):S72-S77.
7. Duru OK, Li S, Jurkovitz C, Bakris G, et al. Race and Sex Differences in Hypertension Control in CKD: Results From the Kidney Early Evaluation Program (KEEP). Am J Kidney Dis. 2008 February;51(2):192-198.
-
-
-
6.1.3 Current Use(s)6.1.4 Program DetailsMerit-based Incentive Payment System (MIPS) - Centers for Medicare and Medicaid (CMS), https://qpp.cms.gov/mips/reporting-options-overview, The Merit-based Incentive Payment System (MIPS) is one way to participate in the Quality Payment Program (QPP)., Medicare Part B patients in the United States., Eligible clinicians may participate as an individual, group, virtual group or alternative payment model (APM) Entity. IndividualClinicians can collect
-
6.2.1 Actions of Measured Entities to Improve Performance
Entities must review lab results of all CKD patients for the presence of proteinuria, identify relevant patients without contraindications, discuss the value of taking ACEi/ARBs with the patients for whom it is indicated and prescribe ACEi/ARBs to those patients. Once patients are on the medication, dosage needs to be reviewed and up-titrated to achieve maximal benefit. Improvement requires patient monitoring and review.
6.2.2 Feedback on Measure PerformanceFeedback received from the ASN Quality Committee included:
- Strong evidence for ACEi/ARB use in delaying CKD progression; evidence stronger with higher proteinuria and earlier CKD stages.
- May cause increased rates of hyperkalemia and/or creatinine elevation, particularly in advanced CKD stages, and requires monitoring.
6.2.3 Consideration of Measure FeedbackThe RPA agrees that patients must be monitored for increased rates of hyperkalemia and/or creatinine elevation. However, RPA believes this to be part of high quality patient care and does not require changes to the measure specifications.
6.2.4 Progress on ImprovementRPA awaits the release of MIPS data regarding this measure to be able to benchmark progress. Review of the 2022 USRDS report does not show significant change in the use of ACEi/ARBs in CKD patients (based on 2020 data). It is hoped that the inclusion of the measure in the MIPS program may spur improvement in the use of these medications that can slow the progression of CKD.
6.2.5 Unexpected FindingsIt is possible for the measure to be satisfied by prescribing a low dose of ACEi/ARB without an effort to up-titrate to the maximal dose, which is what is needed to achieve maximal benefit. The “unintended” consequence is suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated. In order to overcome this, it may be valuable for providers to link the measure to an electronic clinical reminder to review dosage.
-
-
-
CBE# 1662 Staff Assessment
Importance
ImportanceStrengths:
- Submission cites evidence from published literature (seven studies) of the association between prescribed ACE inhibitor or ARB therapy and a material outcome (i.e., risk of kidney failure, ESRD) even in the absence of hypertension in this population.
- Developer reports performance data on this measure obtained in the CMS Physician Quality Reporting Initiative (PQRI) in 2008, demonstrating a gap in performance. Physicians at the 25th percentile prescribed ACEi/ARB appropriately for 33.3% of patients, while physicians at the 75th percentile prescribed it for 100% of patients, for an IQR of 66.8%.
- The developer also reports evidence from the National Health and Nutrition Examination Survey that finds among patients with any CKD, use of ACEIs/ARBs is 40% (in 2011-2014). Among those with severely increased albuminuria (urine albumin-to-creatinine ratio of >300 mg/g) and hypertension without diabetes, ACEi/ARB use was only 33%. According to 2020 USRDS data, only 56% of Medicare beneficiaries are receiving ACEi/ARBs.
Limitations:
- There is no empirical demonstration of an association between the measure focus and a material outcome.
- There is no empirical demonstration of the number of persons with a diagnosis of chronic kidney disease (CKD) and symptom of proteinuria.
- PQRI performance data are from 2008, and the developer notes that "this measure is currently in use in the CMS Merit-based Payment System (MIPS) for 2022 and 2023; however data on its performance has not yet been released by CMS."
- Submission notes a potential “unintended” consequence of suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated.
- Submission does not mention any other potential adverse effects requiring monitoring (e.g., hyperkalemia) (Momoniat, 2019; PMID: 31498767).
Rationale:
- Although there is no empirical demonstration of importance, further studies are highly unlikely to have a significant impact on domain rating
Feasibility Acceptance
Feasibility AcceptanceStrengths:
- Measure implementation requires mostly administrative/coded data, including CPT codes and ICD-10-CM codes.
- Developer comment: The proteinuria/albuminuria criteria may be screened for electronically (e.g., value of test with LOINC 9318-7 >300 mg/g) in a practice's electronic health record.
Limitations:
- Measure implementation requires manual chart review, in particular for the denominator exceptions (medical reason or patient reason for not prescribing). However, there is no description of the resource needs for this type of data collection and reporting.
- Developer comment: While denominator exclusions may be more difficult to automate without development of specific codes or the use of sophisticated technology, such as Natural Language Processing to electronically “read” the chart for exclusions, we do not believe this makes the measure unfeasible.
Rationale:
- An explicit articulation of the people, processes, and technology required for data collection and reporting is likely to have a significant impact on domain rating. However, the developer does not anticipate that the current lack of coded data for denominator exclusions makes the measure infeasible. The committee may consider asking the developer to elaborate on this.
Scientific Acceptability
Scientific Acceptability ReliabilityStrengths:
- Measure is well defined and specified.
- Patient level reliability was assessed with inter-abstractor analysis on a dataset from 2007-2008. The results show 93.15% agreement across 73 patients for the numerator (ACE Inhibitor or ARB Therapy Measure). The 95% confidence interval for Cohen's Kappa is (0.6395-0.9699), well above the threshold of 0.4.
- Although no accountable entity-level reliability analysis was performed, there was a significant gap in performance (the 10th percentile of the score was 11.4% and over 25% of the entities scored 100%), which may suggest high entity level reliability.
Limitations:
- Entity level reliability has not been assessed.
- Performance data are from 2008. If the gap in performance has narrowed, entity level reliability may be low, especially for entities with low denominator size. A signal-to-noise analysis of current data would provide important information about entity level reliability and the impact of denominator size on reliability.
Rationale:
- Data element assessments are 15 years old, and no entity level reliability testing conducted. The committee may consider whether care practices have changed significantly that would require updated testing, including accountable-entity-level testing.
Scientific Acceptability ValidityStrengths:
- For validity, the submission relies primarily on face validity evidence from a technical expert panel, a American Society of Nephrology (ASN) quality committee, KDIGO guidelines, and published literature
The developer convened a technical expert panel (N=19), which reached consensus of agreement on the statement, "the scores obtained from the measure as specified will accurately differentiate quality across providers." - In addition, the ASN quality committee (2019) conducted a structured measure evaluation and the ACEi/ARB measure was assessed as "high" on validity. The Kidney Disease Improving Global Outcomes (KDIGO) 2012 guidelines recommend that “an ARB or ACE-I be used in both diabetic and non-diabetic adults with CKD and urine albumin excretion >300 mg/24 hours (or equivalent)” (Recommendation 3.1.7, 1B). The KDIGO 2021 Clinical Practice Guideline on the Management of Blood Pressure (BP) in CKD recommended “starting renin-angiotensin-system inhibitors (RASi) (ACEi or ARB) for people with high BP, CKD, and severely increased albuminuria (G1–G4, A3) without diabetes” and “for people with high BP, CKD, and moderately-to-severely increased albuminuria (G1–G4, A2 and A3) with diabetes” (Recommendations 3.2.1 and 3.2.3, 1B).
- The developer cites evidence from published literature (six studies) on the performance gap in ACE inhibitor and ARB usage, and the impact of population health efforts to increase the use of ACEi/ARB.
Limitations:
- Submission cites empirical testing from an earlier PQRS version of the measure (conducted in 2007) from four (4) sites with CKD/ESRD samples ranging from 24-30.
- The developer reports data element testing results as: Measure (N, % Agreement, Kappa ( 95% Confidence Interval))
- ACE Inhibitor or ARB Therapy Measure (73, 93.15%, 0.8047 (0.6395- 0.9699)
- There is no empirical demonstration of entity-level (measure score) validity.
Rationale:
- The developer provides testing from a prior measure submission, with data that are a decade old. The developer should consider providing updated testing with empirical demonstrations of data element reliability/validity, accountable entity reliability, and accountable entity validity using current data for the measure, as specified, which is likely to have a significant impact on the domain rating. Specifically, the developer may consider exploring whether any association between the entity and the measure focus may be attributable to known and effective ways to increase prescribing.
Equity
EquityStrengths:
- Developer cites literature showing:
1) a gap in ACEi/ARB use among patients with CKD (40% of CKD patients don't use);
2) differences in progression to ESRD (AfAm> White) and risk factors for ESRD by race (including htn and diabetes);
3) race- and gender-based disparities in htn control among persons with early CKD (AfAm men worse than white women or white men);
4) population health efforts to increase use of ACEi/ARB in AI/ANs have been associated with a decrease in kidney failure
Limitations:
- Sources cited are highly suggestive of reduced usage of ACEi/ARB as a potential factor explaining differences in chronic conditions based on social risk factors but do not connect lower use/prescribing with a specific social risk factor.
- This is a maintenance measure but no performance data are used to explore possible disparities in the measure due to "data on its performance has not yet been released by CMS."
Rationale:
- There is substantial literature showing higher incidence of ESRD and ESRD risk factors among African Americans, Hispanics, and American Indians/Alaskan Natives compared with whites. While use of ACEi/ARB is a reasonable factor to consider for explaining this difference, evidence linking social risk factors directly to use of ACEi/ARB is not reviewed. The developer may wish to consider this in the future.
Use and Usability
Use and UsabilityStrengths:
- Measure currently in use in MIPS (eligible entities can receive performance-based incentives).
- Developer explains steps entities are expected to take to comply with the measure intent/report the measure, including reviewing lab results of all CKD patients for the presence of proteinuria, identifying relevant patients without contraindications, and discussing the value of taking ACEi/ARBs with the patients for whom it is indicated and prescribe ACEi/ARBs to those patients.
- Developer notes that a possible unintended consequence is suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated. To overcome this, the developer suggests it may be valuable for providers to link the measure to an electronic clinical reminder to review dosage.
- Developer comment: Feedback on the measure may be provided directly to the developer, or can be provided through the MIPS annual update process spearheaded by QMMS.
Limitations:
- No information was provided regarding whether feedback about the measure has been received from users or how any feedback has been used.
- Improvement on performance is not reported; developers indicate they are waiting for release of MIPS data to benchmark progress, and developers comment that they anticipate the usual 2-year lag between the performance year and data release.
Rationale:
- This measure is currently in use in MIPS and the developers do not report any unexpected findings.
- There is no information regarding whether feedback about the measure has been received or how any feedback has been addressed. Improvement is not reported and developers explain that MIPS data for the 2022 reporting year (its first in MIPS) has not yet been released; the customary 2-year lag between the performance year and release of data prevents release of these data at this time. Developers note that a potential unintended consequence is that providers can satisfy the metric without fully assessing the patient for appropriate dose, and suggest use of a clinical reminder system.
Summary
-
Met or not met with addressable factors
Importance
ImportanceACEi are accepted as a revolution for anti-HTN'ves in CKD, further study to elucidate the efficacy and utilization of that knowledge is of high value, or.. important.
Staff notes copied here:
- Submission cites evidence from published literature (seven studies) of the association between prescribed ACE inhibitor or ARB therapy and a material outcome (i.e., risk of kidney failure, ESRD) even in the absence of hypertension in this population.
- Developer reports performance data on this measure obtained in the CMS Physician Quality Reporting Initiative (PQRI) in 2008, demonstrating a gap in performance. Physicians at the 25th percentile prescribed ACEi/ARB appropriately for 33.3% of patients, while physicians at the 75th percentile prescribed it for 100% of patients, for an IQR of 66.8%.
- The developer also reports evidence from the National Health and Nutrition Examination Survey that finds among patients with any CKD, use of ACEIs/ARBs is 40% (in 2011-2014). Among those with severely increased albuminuria (urine albumin-to-creatinine ratio of >300 mg/g) and hypertension without diabetes, ACEi/ARB use was only 33%. According to 2020 USRDS data, only 56% of Medicare beneficiaries are receiving ACEi/ARBs.
Feasibility Acceptance
Feasibility AcceptanceThis certainly seems feasible and I agree that the data is available from the EHR. That said, the developer might benefit (and strengthen the studies validity and value) from detailing this process.
Staff notes copied here:
- Developer comment: While denominator exclusions may be more difficult to automate without development of specific codes or the use of sophisticated technology, such as Natural Language Processing to electronically “read” the chart for exclusions, we do not believe this makes the measure unfeasible.
Rationale:
- An explicit articulation of the people, processes, and technology required for data collection and reporting is likely to have a significant impact on domain rating. However, the developer does not anticipate that the current lack of coded data for denominator exclusions makes the measure infeasible. The committee may consider asking the developer to elaborate on this.
Scientific Acceptability
Scientific Acceptability ReliabilityAppreciate the staff noting the gap in the performance component below - this was close to not met for me based on the 2007 (and therefore legacy retrieval in many EHRs). This might be accomplished from more recent data, but ok. The developer might look at the entity reliability and continue on.
Strengths:
- Measure is well defined and specified.
- Patient level reliability was assessed with inter-abstractor analysis on a dataset from 2007-2008. The results show 93.15% agreement across 73 patients for the numerator (ACE Inhibitor or ARB Therapy Measure). The 95% confidence interval for Cohen's Kappa is (0.6395-0.9699), well above the threshold of 0.4.
- Although no accountable entity-level reliability analysis was performed, there was a significant gap in performance (the 10th percentile of the score was 11.4% and over 25% of the entities scored 100%), which may suggest high entity level reliability.
Limitations:
- Entity level reliability has not been assessed.
- Performance data are from 2008. If the gap in performance has narrowed, entity level reliability may be low, especially for entities with low denominator size. A signal-to-noise analysis of current data would provide important information about entity level reliability and the impact of denominator size on reliability.
Rationale:
- Data element assessments are 15 years old, and no entity level reliability testing conducted. The committee may consider whether care practices have changed significantly that would require updated testing, including accountable-entity-level testing.
Scientific Acceptability ValidityThe times are changing and electronic Rx'ing for example, may significantly change this study - let alone online pharmacies (yet this would not be prudent to expect, given the still novel nature of this concept).
The CI worried me, as mentioned above.
Staff notes copied below:
- For validity, the submission relies primarily on face validity evidence from a technical expert panel, a American Society of Nephrology (ASN) quality committee, KDIGO guidelines, and published literature
The developer convened a technical expert panel (N=19), which reached consensus of agreement on the statement, "the scores obtained from the measure as specified will accurately differentiate quality across providers." - In addition, the ASN quality committee (2019) conducted a structured measure evaluation and the ACEi/ARB measure was assessed as "high" on validity. The Kidney Disease Improving Global Outcomes (KDIGO) 2012 guidelines recommend that “an ARB or ACE-I be used in both diabetic and non-diabetic adults with CKD and urine albumin excretion >300 mg/24 hours (or equivalent)” (Recommendation 3.1.7, 1B). The KDIGO 2021 Clinical Practice Guideline on the Management of Blood Pressure (BP) in CKD recommended “starting renin-angiotensin-system inhibitors (RASi) (ACEi or ARB) for people with high BP, CKD, and severely increased albuminuria (G1–G4, A3) without diabetes” and “for people with high BP, CKD, and moderately-to-severely increased albuminuria (G1–G4, A2 and A3) with diabetes” (Recommendations 3.2.1 and 3.2.3, 1B).
- The developer cites evidence from published literature (six studies) on the performance gap in ACE inhibitor and ARB usage, and the impact of population health efforts to increase the use of ACEi/ARB.
Limitations:
- Submission cites empirical testing from an earlier PQRS version of the measure (conducted in 2007) from four (4) sites with CKD/ESRD samples ranging from 24-30.
- The developer reports data element testing results as: Measure (N, % Agreement, Kappa ( 95% Confidence Interval))
- ACE Inhibitor or ARB Therapy Measure (73, 93.15%, 0.8047 (0.6395- 0.9699)
- There is no empirical demonstration of entity-level (measure score) validity.
Equity
EquityThis is a topic which deserves further focus. Here is a known fx in a study assessing the improvement in maximizing a benefit - to groups which may stand to benefit.
Staff notes copied below:
Strengths:
- Developer cites literature showing:
1) a gap in ACEi/ARB use among patients with CKD (40% of CKD patients don't use);
2) differences in progression to ESRD (AfAm> White) and risk factors for ESRD by race (including htn and diabetes);
3) race- and gender-based disparities in htn control among persons with early CKD (AfAm men worse than white women or white men);
4) population health efforts to increase use of ACEi/ARB in AI/ANs have been associated with a decrease in kidney failure
Limitations:
- Sources cited are highly suggestive of reduced usage of ACEi/ARB as a potential factor explaining differences in chronic conditions based on social risk factors but do not connect lower use/prescribing with a specific social risk factor.
- This is a maintenance measure but no performance data are used to explore possible disparities in the measure due to "data on its performance has not yet been released by CMS."
Rationale:
- There is substantial literature showing higher incidence of ESRD and ESRD risk factors among African Americans, Hispanics, and American Indians/Alaskan Natives compared with whites. While use of ACEi/ARB is a reasonable factor to consider for explaining this difference, evidence linking social risk factors directly to use of ACEi/ARB is not reviewed. The developer may wish to consider this in the future.
Use and Usability
Use and UsabilityNo feedback is available, so unclear usability. The need to uptitrate is often based on a study where the efficacy was assessed at a specific dose, in a tertiary center. More isn't necessarily better and let's be careful to not hammer = nail. Please don't suggest the clinical reminder pop up..
Copy of valuable staff rationale:
Rationale:
- This measure is currently in use in MIPS and the developers do not report any unexpected findings.
- There is no information regarding whether feedback about the measure has been received or how any feedback has been addressed. Improvement is not reported and developers explain that MIPS data for the 2022 reporting year (its first in MIPS) has not yet been released; the customary 2-year lag between the performance year and release of data prevents release of these data at this time. Developers note that a potential unintended consequence is that providers can satisfy the metric without fully assessing the patient for appropriate dose, and suggest use of a clinical reminder system.
Summary
Met or not met with addressable factors - further study of ACEi on CKD is certainly a warranted and valuable study. Some details above on maximizing are further group discussion will be an asset to the community.
This measure clearly has…
Importance
ImportanceThe staff review includes a comprehensive summary of the supporting research including practice guideline support and results of meta-analyses of RCTs. The data provided to address performance gap is over 15 years old. While the staff review justified a "met" rating based on an assessment that updated data will likely not change the importance of this measure, the measure still should be reported with more current data. The MIPS 2022-2023 should meet this need.
Feasibility Acceptance
Feasibility AcceptanceThis measure relies on record review for CPT codes, ICD-9 codes and lab results. The measure developers do not provide information about the time needed to abstract data and any difficulties encountered.
Scientific Acceptability
Scientific Acceptability ReliabilityThe data provided to support reliability is old (2007) and based on a relatively small sample size (4 practices with 24-30 patient records/practice). Interrater reliability is based on a kappa analysis of just two abstractors. Data need to be updated.
Scientific Acceptability ValidityValidity of the measure is supported through face validity only (2007, 2019). While face validity is clearly strong, given the amount of time this measure has been used in quality performance programs, there should be more empirical testing connecting the measure to outcomes.
Equity
EquityThe measure developers provide general information about an equity gap, e.g., populations at greater risk at adverse outcomes. They need to provide specific information about the measure rates across different patient groups.
Use and Usability
Use and UsabilityThe measure is currently being used in MIPS - however the data relevant to use and usability (and maintenance endorsement criteria) are not available. These data should be provided to support this review.
Summary
This measure clearly has face validity - given that it is a maintenance measure currently in use with MIPS - additional information is needed to support continued use. In general, I agree with each of the staff reviews - and would also suggest that importance requires updated empirical support.
See above
Importance
ImportanceACE/ARB use in CKD with proteinuria is nearly universally recognized as a critical way to delay CKD progression. Definition of amount of proteinuria that necessitates treatment can be debated (as > 300 mg/g on UACR or UPCR for this metric; other sources suggest UACR > 300 mg/g or UPCR > 500 mg/g others might say > 1000 mg/g).
Feasibility Acceptance
Feasibility AcceptanceI would like more details on how it is being determined if CKD is present. Is this based on a single ICD10 code of CKD within the last 12 months? Or an ICD10 code of CKD and proteinuria? How is it being determined if the degree of proteinuria is > 300 mg/g on UACR or UPCR? What if there is 1 value of UPCR > 300 mg/g and a repeat value of less than 300 mg/g? Is N18.6 automatically an exclusion? What if the patient isn't on dialysis and a provider entered this ICD10 code erroneously?
Regarding the numerator, how is it being determined if the patient has been prescribed an ACE/ARB? Is this solely based on the medication list irrelevant of the accuracy of the medication list? If the patient is not on ACE/ARB due to a documented medical or patient specific reason, how is this being determined, and how accurate is whatever process exists to determine this?
Scientific Acceptability
Scientific Acceptability ReliabilitySee above, I want more details on how this metric is being calculated
Scientific Acceptability ValiditySee above, I want more details on how this metric is being calculated
Equity
EquityCKD disproportionately affects vulnerable populations. As such, to the extent that the existence of this measure can help contribute to improved CKD care, this should benefit vulnerable populations the most.
Use and Usability
Use and UsabilitySee above
Summary
See above
CKD is a major public health problem
Importance
ImportanceThe measure is strongly supported by various medical entities but recent data should be provided to see what has transpired since the measure was initially introduced in 2015.
Feasibility Acceptance
Feasibility AcceptanceWith Epic software for electronic medical records it should be simplistic to update this quality measure. Epic is used by hospitals and even rural clinical systems and (according to their website) holds medical records of 78% of patients in the United States. Using Epic could provide the analytics and insights to further enhance this measure.
Scientific Acceptability
Scientific Acceptability ReliabilityVarious medical entities support this measure.
Scientific Acceptability ValidityVarious medical entities support this measure.
Equity
EquityWhile there are performance gaps noted, what should also be noted is the high percentage of individuals over the age of 65 that will get CKD
Use and Usability
Use and Usability“Feedback received from the ASN Quality Committee included:
- May cause increased rates of hyperkalemia and/or creatinine elevation, particularly in advanced CKD stages, and requires monitoring.”
More information or analytics should be presented on what these numbers look like over various demographics.
Summary
It appears that this quality measure was endorsed in 2015 which would explain why most of the data is old. References cited throughout the measure recommending the use of ACE inhibitors and ARB’s are also relatively old, it would benefit this measure to provide current data references for trends.
Some referenced data (…Among patients with any CKD, use of ACEIs/ARBs is 40% according to data from the National Health and Nutrition Examination Survey) cannot be viewed/located by a patient/caregiver.
As for the scientific data, and as a patient/caregiver, I find the sample sizes too small. 4 Sites had approximately 30 patients each for a total of 112 CKD patients. As a patient/caregiver I would have expected at least 25 sites throughout the US and the sample size of 50 CKD patients per site. This data could most likely be retrieved from an electronic medical records system like Epic.
CKD
Importance
ImportanceAn important measure, but more current data is needed.
Feasibility Acceptance
Feasibility AcceptanceChart review is very difficult
Scientific Acceptability
Scientific Acceptability ReliabilityNeed more current data
Scientific Acceptability ValidityNeed more current data
Equity
EquityNeed current data
Use and Usability
Use and UsabilityNeed current information
Summary
N/A
Underdeveloped measure
Importance
ImportanceThe concept of utilizing ACE or ARB to aid in kidney function early in CKD treatment still seems to be supported. See https://pubmed.ncbi.nlm.nih.gov/31939197/ and https://www.ajkd.org/article/S0272-6386(22)00522-4/fulltext Measure 1662 identifies correctly the medical considerations for using RAAS well with the following limitations:
1. The renal benefit of these drugs is primarily in the reduction of proteinuria. So while diabetic patients are important, it is not a criteria for inclusion. The measure cites several studies that focus on diabetic nephropathy.2. In evaluating the numerator and denominator, I would suggest that the stages of kidney disease be removed. ACE inhibitors are known to reduce creatinine clearance at least initially due to the reducing blood pressure/ flow. With patients in stage 5, they need all the clearance possible. The decision about the tradeoffs between hypertension and creatinine is best left to the patient’s nephrologist. To have a measure that includes this population is to discourage patient focused care.
3. Is there a need to distinguish secondary hypertension from primary hypertension? Not all CKD is tied to diabetes or primary hypertension. Is it the expectation of the measure that both groups would be treated the same?
I concur with these additional limitations expressed by the PDQ staff:
There is no empirical demonstration of an association between the measure focus and a material outcome.
See my comments in Use/ Usefulness.
Submission notes a potential “unintended” consequence of suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated.
This goes to the diversity of the diversity of CKD patients' health complexity. However I see this same issue with Measure 0383. This is a challenging issue with all measures. If PDQ and CMS are wiling to take a more active role in monitoring standard of care, thinks might change. Although I have no objective data, it would seem that the current financial incentives under MIPS are not making a material difference.
Submission does not mention any other potential adverse effects requiring monitoring (e.g., hyperkalemia)
The study mentions hyperkalemia as a concern but does not screen for it when the data is initially collected. It also has no mechanism for accounting for hyperkalemia after the initiation of RAAS medications.
Feasibility Acceptance
Feasibility AcceptanceSuch as the data is, the measure collected data and provided a rudimentary analysis.
Scientific Acceptability
Scientific Acceptability ReliabilityThis section is sparse. While the data gathering technique is well defined, the sample size was low and only included one year’s data.
Scientific Acceptability ValidityI do not understand face value as a validity tool. I would like a fuller explanation before making a conclusion.
Equity
EquityThis is an optional evaluation but the measure did offer some cursory information.
Use and Usability
Use and UsabilityThe evidence cited that “only 40 percent of CKD patients receiving an ACEi/ARB” does not take into account the diversity of CKD patients’ medical condition. As a practical matter, unless a measure provides more detail about how to evaluate RAAS treatments in relation to the diagnosis, it is difficult to see the value. Diabetes nephropathy may be addressed correctly with this measure if it includes a tool to measure follow up. However will it be equally as meaningful for lupus, polycystic disease or cancer diagnosis? Clinicians know the differences already without checking their performance in an aggregate measure.
It appears that most of the institutions involved in the results presented are large institutions. If the measure developer is only focused on larger providers, there might be a path to resolve this. Otherwise, the measure’s lack of direction renders it marginally useful to practitioners.Patients especially in the early stages of CKD are not well educated. They will tend to follow the regime of their nephrologist. If they fail to understand the long term effects of hypertension, they will not investigate further.
If there was a way to certify that urea, liver panel, potassium levels and renal panel labs were checked, it would be easier to sign on for this measure. Without evidence of follow up lab work to monitor the start of a RAAS protocol, CMS will not have a clear picture of its usefulness.
Summary
This measure was adopted in 2015. This measure is currently in use for MIPS. As it stands, I am not in favor of continuing this measure.
Comments reported under each…
Importance
ImportanceClinical guidelines support increasing the number of patients with CKD and proteinuria who are prescribed ACE inhibitor or ARB therapy slows the progression of kidney disease.
Feasibility Acceptance
Feasibility AcceptanceMeasure specifications identify ICD-10 codes, including exclusions and an algorithm for calculation of the measure score.
Scientific Acceptability
Scientific Acceptability ReliabilityPerformance data are from 2008, would like to see testing with newer data.
Scientific Acceptability ValidityPerformance data are from 2008, would like to see testing with newer data.
Equity
EquityNew performance data could be used to explore possible disparities in the measure
Use and Usability
Use and UsabilityDon’t know as no current performance data
Summary
Comments reported under each domain.
Current data needed to support endorsement
Importance
ImportanceThe measure steward states, “CKD is a major public health problem; a total of 37 million Americans have CKD. There is a clear performance gap in ACE inhibitor and ARB usage among patients with CKD, with only 40 percent of CKD patients receiving an ACEi/ARB in NHANES data (Murphy et al., JASN, 2019). Population health efforts to increase the use of ACEi/ARB in American Indians and Alaska Natives have been associated with a decrease in incident kidney failure related to diabetic kidney disease (Bullock et al., MMWR Morbidity and mortality weekly report, 2017). In summary, this measure is a central component of high-quality nephrology care, as ACE inhibitors and ARBs decrease the rate of kidney failure, cardiovascular outcomes, and mortality in patients with CKD and proteinuria.”
This is compelling rationale for a quality measure in this arena to improve the use of ACE inhibitors and ARBs in patients with CKD. In the absence of performance data from the MIPS program, the literature appears to support a gap in care- “Among patients with any CKD, use of ACEIs/ARBs is 40% according to data from the National Health and Nutrition Examination Survey. Among those with severely increased albuminuria (urine albumin-to-creatinine ratio of >300 mg/g) and hypertension without diabetes, ACEi/ARB use was only 33%. According to 2020 USRDS data, only 56% of Medicare beneficiaries are receiving ACEi/ARBs.”
Feasibility Acceptance
Feasibility AcceptanceAlthough this is a registry-based measure, there are clearly defined values and definitions for the numerator, denominator, exclusion, and exceptions. This measure could be manually chart abstracted or automated by an EHR vendor to support the measure calculation or data set required by a 3rd party registry to compute. A more current study showing inter-rater reliability would validate that this measure is feasible to abstract.
Scientific Acceptability
Scientific Acceptability ReliabilityData submitted to support the measure’s validity and reliability is outdated. Given the changes in EHRs and medical practice, the measure should have been retested for validity and reliability. The inter-rater reliability testing showed promising results, but this should need to be current for re-endorsement.
Historically, Empirical Validity Testing has been required for maintenance evaluation. This would indicate that Face Validity is insufficient. Statistical analysis, such as a correlation coefficient, is a more robust assessment of validity.
Scientific Acceptability ValidityAt this time, there are no results available through the MIPS program; therefore, there is no evidence that this measure has been implemented by EHR or registry vendors to enable participation and reporting.
Equity
EquityNot addressed, but not required.
Use and Usability
Use and UsabilityAt this time, there are no results available through the MIPS program; therefore, there is no evidence that this measure has been implemented by EHR or registry vendors to enable participation and reporting.
Summary
Although the measure aims to improve the use of critical medicine to support patients with CKD, the statistical data provided is outdated and potentially not applicable, given the changes to EHRs and medical practice. Previous data relied on face validity, when typically maintenance endorsement requires emperical validity testing.
Moreover, there is no evidence that the measure has been adopted and implemented for use in the MIPS program at this time. Preliminary CMS data could help address this issue.
NA, see above for each…
Importance
Importancethis is acceptable and have decent evidence behind it
Feasibility Acceptance
Feasibility AcceptanceNeed to look more into CMS data from 22-23 to ensure there is no feasibility issue from EMR reporting specially way to streamline the exclusion to avoid burden
Scientific Acceptability
Scientific Acceptability Reliabilitythe 2007 data is old, sample size were small to enroll. while in clinical setting , this measure has been used and assessment in QI, MIPS data will be crucial
Scientific Acceptability Validitysame
Equity
Equitythe data in the papers is there but the developper should show this in the current data set
Use and Usability
Use and UsabilityNeed more current data to show use by stages . while it is important to be on this meds for all stages according to clinical judgement, there is gap in starting this meds in early in CKD in primary care . slicing the data this way will help
Summary
NA, see above for each comment
Measure 1662 is recommended…
Importance
ImportanceMeasure developers provided evidence of importance and its inclusion by national and international organizations as an indicator of high quality of care for those with kidney disease.
Feasibility Acceptance
Feasibility AcceptanceMeasure developers did not address potential burden of identifying criteria for exclusion from denominator based on medical contraindications or patient choice.
Scientific Acceptability
Scientific Acceptability ReliabilityMeasure developers did not provide recent data regarding reliability testing.
Scientific Acceptability ValidityMeasure developers did not provide recent data regarding validity testing.
Equity
EquityMeasure developers provided information that indicated that there could be differences in use of ACE Inhibitors and Angiotensin Receptor Blockers among different patient populations, but did not specifically provide this data.
Use and Usability
Use and UsabilityMeasure developers did not provide evidence of how this maintenance measure has been used to improve the quality of care for patients with CKD and did not address how it sought/responded to feedback from end users.
Summary
Measure 1662 is recommended for endorsement with additional information from measure developers as noted above.
Summary
Importance
ImportanceStrong data to support use of ACEi and ARB therapy in this population.
Feasibility Acceptance
Feasibility AcceptanceThere are limitations associated with determining the population of interest using administrative/coded data or chart review but these can be addressed.
Scientific Acceptability
Scientific Acceptability ReliabilityWould benefit from more recent assessment of reliability.
Scientific Acceptability ValidityWould benefit from additional assessment of validity.
Equity
EquityHighlights concern regarding access and equitable care in different patient populations.
Use and Usability
Use and UsabilityIn use for MIPS but unclear if associated with improvement.
Summary
Criteria met or addressable across domains.
Agree with PQM staff…
Importance
ImportanceAgree with PQM staff assessment
Feasibility Acceptance
Feasibility AcceptanceAgree with PQM staff assessment; movement to eCQM or more automated abstraction would be preferred by next endorsement
Scientific Acceptability
Scientific Acceptability ReliabilityAgree with PQM staff assessment
Scientific Acceptability ValidityAgree with PQM staff assessment
Equity
EquityAgree with PQM staff assessment
Use and Usability
Use and UsabilityAgree with PQM staff assessment
Summary
Agree with PQM staff assessment
n/a
Importance
ImportanceWould want clarification/more discussion of this point:
- Submission notes a potential “unintended” consequence of suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated.
Can this be mitigated?
Feasibility Acceptance
Feasibility AcceptanceAgree w/ staff feedback
Scientific Acceptability
Scientific Acceptability ReliabilityAgree particularly with comment:
- Performance data are from 2008. If the gap in performance has narrowed, entity level reliability may be low, especially for entities with low denominator size. A signal-to-noise analysis of current data would provide important information about entity level reliability and the impact of denominator size on reliability.
Scientific Acceptability Validityas above
Equity
EquityThis section would benefit from current data as well
Use and Usability
Use and UsabilityRegarding this comment:
- Developer notes that a possible unintended consequence is suboptimal treatment with patients placed on very low doses of RAS blockade rather than having the dose properly titrated. To overcome this, the developer suggests it may be valuable for providers to link the measure to an electronic clinical reminder to review dosage.
What is the data that such a clinical reminder would be effective?
Summary
n/a
Measure shows promise but lacks fresh data
Importance
ImportanceThe summary of the literature highlighting the association between ACE/ARB and a material outcome is robust.
There is a documented gap in the use of ACE/ARB among the target population.
Feasibility Acceptance
Feasibility AcceptanceAgree with staff assessment of feasibility. A detailed, near-universal method for identifying denominator exclusions is important.
Scientific Acceptability
Scientific Acceptability ReliabilityThe developer did not perform entity-level reliability tests. I'm concerned that the top 25% being at 100% may indicate that the measure is topped-out, which will negatively affect its entity-level reliability results. A test and findings will be necessary to adequately rate this section, but it appears this won't be possible until the MIPS data are released.
IRR scores for data elements appear very robust.
Scientific Acceptability ValidityThe developer provided adequate evidence of face validity, albeit somewhat stale. Empirical tests of validity do not seem possible until the MIPS data are released.
Equity
EquityAgree with staff assessment. Empirical evidence of equity relationship would be helpful but may not be possible without MIPS data.
Use and Usability
Use and UsabilityAgree with staff assessment.
Summary
This measure appears to be clinically important and useable, and aside from concerns about exclusions, the measure also appears to be feasible. Without MIPS data, we are unable to assess this measure fully.
The proposed measure…
Importance
ImportanceStudies referenced indicate a gap in care/treatment that, if impacted, could result in slowing of disease progression and/or prevention of patient death. The current limitations presented by the measure include a lack of evidence of any performance improvement as a result of the data and measure information that has already been collected.
Feasibility Acceptance
Feasibility AcceptanceA significant portion of the measure definition requires manual chart review in order to determine measure outcome scores. Manual data extraction is labor intensive and also more susceptible to human error and/or intentional skewing of measure results. It is highly recommended that specific codes be identified for inclusion in the EMR to allow for automated calculation and identification, eliminating the need for manual chart review and data extraction. Manual chart review and data extraction also increases labor intensity because it requires an additional manual data entry once the chart has been reviewed. Coded, automated responses built into the EMR eliminates the need for manual data entry once the review has taken place.
Scientific Acceptability
Scientific Acceptability ReliabilityMeasure documentation provided indicates a high level of reliability.
Scientific Acceptability ValidityMeasure documentation provided indicates a high level of reliability.
Equity
EquityThe measure proposal indicates an opportunity to impact improved outcomes for various populations however, given that this measure is a process measure and does not address outcomes once the measure has been "met", it is unclear as to whether or not this measure will truly impact patient care outcomes. The literature cited does not specify as to whether or not patients in at-risk populations have a lower incidence of prescribed ACE inhibitors/ARB therapy because it is not being offered, or for some other reason. While the literature does indicate that there are poorer outcomes in certain at-risk populations, it is unclear that this measure would impact those outcomes and to what level.
Use and Usability
Use and UsabilityThere is no evidence offered in the measure explanation indicating that the results of the measure would be/are utilized to improve patient care. Because the proposed measure is purely a process measure, looking at whether or not a medication is prescribed, it does not capture what happens after that measure has been met. In other words, a physician might prescribe a medication but there is no proposed follow-up offered as part of this measure to capture whether or not the prescription was filled, whether or not the patient is taking the medication, whether or not the patient is taking the medication according to provider instructions, as well as whether or not the prescribed dose is being adjusted over time in order to have maximum efficacy for the patient. This measure addresses only a small portion of the potential need and the overall process.
Summary
The proposed measure presents some opportunity to initially address what has been identified in the literature as a lack of treatment proven to positively impact outcomes for particular patients with CKD. However, there are concerns related to feasibility, as it (as written) requires a manual chart review process and subsequent manual data entry of results of the chart review for some portions of the measure. In addition, being a process measure addressing only one aspect of the need (prescription of the ACE inhibitor/ARB therapy), it is unclear as to usability and how actionable the results would be. Finally, because the measure addresses only the initial prescribing of the medication and not the many other factors impacting patient outcomes once that medication has been prescribed, the efficacy of the measure in achieving positive clinical outcomes, including to at-risk populations, is also unclear and unproven.
-
N/A