Original ArticlesChronic Kidney Disease

A Population-Based Analysis of Quality Indicators in CKD

Liam Manns, Nairne Scott-Douglas, Marcello Tonelli, Robert Weaver, Helen Tam-Tham, Christy Chong and Brenda Hemmelgarn
CJASN May 2017, 12 (5) 727-733; DOI: https://doi.org/10.2215/CJN.08720816

Abstract

Background and objectives Awareness of CKD remains low in comparison with other chronic diseases, such as diabetes, leading to low use of preventive medications and appropriate testing. The objective of this study was to evaluate the quality of care provided to people with and at risk of CKD.

Design, setting, participants, & measurements We conducted a population-based analysis of all Albertans with eGFR=15–59 ml/min per 1.73 m2 between April 1, 2011 and March 31, 2012 as well as patients with diabetes (as of March 31, 2012). We assessed multiple quality indicators in people with eGFR=15–59 ml/min per 1.73 m2, including appropriate risk stratification with albuminuria testing and preventive medication use and screened people with diabetes using urine albumin-to-creatinine ratio and serum creatinine measurements.

Results Among 96,480 adults with eGFR=15–59 ml/min per 1.73 m2, we found that 17.0% of those without diabetes were appropriately risk stratified with a measure of albuminuria compared with 64.2% of those with diabetes (P<0.001). Of those with eGFR=15–59 ml/min per 1.73 m2 and moderate or severe albuminuria, 63.2% of those without diabetes received an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker compared with 82.1% in those with diabetes (P<0.001). Statin use was also significantly lower in patients with eGFR=15–59 ml/min per 1.73 m2 without diabetes (39.2%) compared with those with diabetes (64.6%; P<0.001). Among 235,649 adults with diabetes, only 41.8% received a urine albumin-to-creatinine ratio and 73.2% received a serum creatinine measurement over 1 year.

Conclusions We identified large gaps in care, especially in those with CKD but no diabetes. The largest gap was in the prescription of guideline-concordant medication in those with CKD as well as appropriate screening for albuminuria in those with diabetes. Our work illustrates the importance of measuring health system performance as the first step in a quality improvement process to improve care and outcomes in CKD.

Introduction

The burden of CKD has increased significantly over time, with the years lived with disability due to CKD increasing 49.5% worldwide between 1990 and 2010 (1). Despite this, the awareness of kidney disease remains low (12.0%) among people with nondialysis CKD (2). CKD can lead to ESRD requiring dialysis or transplantation, and it increases the risk of heart disease and death, irrespective of the presence of diabetes. Although diabetes is a major risk factor for heart disease and considered a coronary heart disease equivalent, CKD is not recognized by many health care providers as an important risk factor for coronary heart disease (3), despite being associated with a higher risk for coronary heart disease than diabetes (4).

Given this, it is important that people with CKD, regardless of diabetes status, receive guideline-recommended care with the following preventive medications (5): angiotensin-converting enzyme inhibitors (ACEis) or angiotensin receptor blockers (ARBs) among people with albuminuria (6) and statins among those with diabetes or those without diabetes over age 50 years old (7). As such, early identification and optimal management of CKD are important. Because people with diabetes are at a high risk of developing CKD, screening for the presence and severity of CKD has also been identified as important (8).

To optimize the kidney health of a population, health systems should monitor the quality of care provided to people with and at risk for CKD. The overall aim of this study was to examine the care provided to people with and at risk for CKD in Alberta, Canada, with the goal of informing strategies to improve patient care and outcomes. Given that diabetes is a well established risk factor for heart disease and that CKD has been recognized as an important risk factor more recently (4), we speculated that people with CKD and diabetes would be more likely to achieve quality indicators, reflecting a higher quality of care compared with those with CKD alone. Our primary objective was, therefore, to determine whether the use of guideline-recommended care (including risk stratification with albuminuria and preventive medication use) is different among people with CKD and diabetes compared with people with CKD without diabetes. The secondary objective was to determine whether people with diabetes are being evaluated appropriately for CKD.

Materials and Methods

Data Source

The Alberta Kidney Disease Network (9), a repository of laboratory and provincial administrative health data from Alberta, Canada, was used to identify people with CKD and diabetes as well as identify outcomes. Data from the Northern Alberta Renal Program and Southern Alberta Renal Program were used to exclude patients receiving dialysis or those with a kidney transplant. All data sources were linked using a unique provincial health number.

Study Cohorts

CKD is usually defined on the basis of laboratory abnormalities, including eGFR<60 ml/min per 1.73 m2 or presence of albuminuria (regardless of eGFR), and some patient groups, including those with diabetes, are at particularly high risk of developing CKD. In this study, we created three retrospective cohorts to examine the two study objectives. The first cohort included adults with eGFR=15–59 ml/min per 1.73 m2 with and without diabetes (eGFR=15–59 ml/min per 1.73 m2 cohort), and the second cohort included all adults with diabetes, regardless of CKD status (diabetes cohort). The eGFR=15–59 ml/min per 1.73 m2 cohort included all adults (age ≥18 years old) with eGFR in this range for a period of at least 90 days between April 1, 2011 and March 31, 2012 as well as adults with eGFR in this range for <90 days during the year and no further measurements. Because the eGFR=15–59 ml/min per 1.73 m2 cohort did not include patients with eGFR≥60 ml/min per 1.73 m2 who had albuminuria (because assessment for albuminuria was one of the quality indicators assessed with this cohort), we created a third cohort, which included those with eGFR≥60 ml/min per 1.73 m2 and albuminuria, that was used to assess appropriate medication use. GFR was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation (10), whereas the index date was the first eGFR between 15 and 59 ml/min per 1.73 m2 during this time period. People who were on dialysis, had received a kidney transplant before cohort entry, or started dialysis within 90 days of their index date were excluded.

The diabetes cohort consisted of adults with diabetes, regardless of CKD status. To define diabetes (in both cohorts), we used administrative data from April 1, 1994 to March 31, 2012 and a validated algorithm on the basis of the ninth and tenth revisions of the International Classification of Diseases (ICD-9 and ICD-10) diagnostic codes in physician claims and inpatient data (11). This definition required two physician claims with an ICD-9 diagnostic code for diabetes in a 2-year period or a single hospitalization with an ICD-10 code. The algorithm does not distinguish between patients with types 1 and 2 diabetes, and as such, our cohort included patients with types 1 and 2 diabetes. People were excluded if they were not residents of Alberta for both 2012 and 2013 (i.e., not registered in the Alberta Health registry in these years); died before April 1, 2012; or were younger than 18 years old on April 1, 2012. People were followed for 1 year after March 31, 2012 to assess outcomes.

Primary and Secondary Outcomes

To assess our primary objective, we measured three quality indicators. The first was the use of an ACEi or ARB among people with eGFR=15–59 ml/min per 1.73 m2 and moderate or severe albuminuria, which was included, because their use has been shown to delay ESRD (6) and is recommended by international guidelines (5). The second quality indicator was the use of statins in those with diabetes and eGFR=15–59 ml/min per 1.73 m2 and those 50 years old and older with eGFR=15–59 ml/min per 1.73 m2 and no diabetes, because statins have been shown to reduce the risk for heart attack and stroke (7) and are also recommended by international guidelines (12). ACEi, ARB, or statin use was defined as dispensation of these medications within a 1-year period after the index date as assessed using a provincial pharmacy database that captures all prescription medications dispensed in the province. As noted above, we also assessed these two indicators in the third patient cohort defined by moderate or severe albuminuria only (A2 or A3 albuminuria using the Kidney Disease Improving Global Outcomes [KDIGO] CKD classification [5]) with a normal (≥60 ml/min per 1.73 m2) or unmeasured eGFR.

The last quality indicator, measured only in those with eGFR=15–59 ml/min per 1.73 m2, was urine albumin screening (to enable appropriate risk stratification) as recommended by guidelines (5). Albuminuria was assessed using the most recent urine albumin-to-creatinine ratio (ACR), urine protein-to-creatinine ratio, or urine dipstick (UDIP) outpatient measurements in the 2 years before the index date. Because guidelines recommend that patients with CKD receive ACR testing to assess CKD etiology and risk for progression to ESRD, the ACR was used as the primary measure of albuminuria. A protein-to-creatinine ratio measurement is also considered acceptable to screen for albuminuria; hence, a protein-to-creatinine ratio assessment was used when the ACR was not available and a UDIP was used when the protein-to-creatinine ratio was not available. We defined moderate to severe albuminuria consistent with KDIGO as ACR>30 mg/g, protein-to-creatinine ratio >150 mg/g, or a UDIP test ≥1+.

To assess the second study objective, we used a diabetes cohort that was at high risk for development of CKD to assess whether they were being evaluated properly for the presence and severity of CKD in accordance with guidelines including urine ACR and serum creatinine (SCr) (5). We included those with and without CKD in this diabetes cohort, because guidelines recommended screening for albuminuria and measurement of SCr, regardless of eGFR (5).

Definitions of Other Variables

Age and other demographic data were assessed using the Alberta Health registry file. Hypertension was identified using a validated algorithm (13), and other comorbidities, such as heart failure, stroke, myocardial infarction, cancer, peripheral vascular disease, and dementia, were determined from the Deyo classification of Charlson comorbidities (11). First Nations status was determined from the Alberta Health registry.

Statistical Methods

We analyzed the data using proportions, means, and 95% confidence intervals. All statistical analysis was conducted with STATA v11.2 (StataCorp, College Station, TX). We are secondary users of these data as defined by the Alberta Health Information Act. Ethics approval was obtained from the University of Calgary and the University of Alberta.

Results

Participant Characteristics–Cohort with eGFR=15–59 ml/min per 1.73 m2

After the exclusion criteria were applied, the final eGFR=15–59 ml/min per 1.73 m2 cohort included 96,480 people (Figure 1). Participants in this cohort had a mean age of 75.9 years old, and 57.3% were women. The majority (63.5%) had eGFR between 45 and 59 ml/min per 1.73 m2 (Table 1). Hypertension, myocardial infarction, stroke, and peripheral vascular disease were all more common among people with eGFR=15–59 ml/min per 1.73 m2 and diabetes compared with those with eGFR=15–59 ml/min per 1.73 m2 without diabetes.

Figure 1.
Figure 1.

eGFR=15–59 ml/min per 1.73 m2 cohort. sCr, serum creatinine.

View this table:
Table 1.

Characteristics of Albertans with eGFR=15–59 ml/min per 1.73 m2 overall and by diabetes status

We also assessed the first two quality indicators in an additional 65,015 people with CKD defined by moderate or severe albuminuria only (with normal or unmeasured eGFR). The mean age of this group was substantially younger than the eGFR=15–59 ml/min per 1.73 m2 cohort at 53.1 years old, with 50.0% being women and 37% having diabetes.

Use of Albuminuria to Risk Stratify People with eGFR=15–59 ml/min per 1.73 m2

Table 2 compares the proportion of people with eGFR=15–59 ml/min per 1.73 m2 with a measure of albuminuria by diabetes status. In a 2-year period, 64.2% of people with eGFR=15–59 ml/min per 1.73 m2 and diabetes had an ACR measurement compared with 17.0% of people with eGFR=15–59 ml/min per 1.73 m2 and no diabetes. When comparing assessment of albuminuria by either ACR or protein-to-creatinine ratio, the eGFR=15–59 ml/min per 1.73 m2 group with diabetes was more likely to receive ACR or protein-to-creatinine ratio testing than the group with eGFR=15–59 ml/min per 1.73 m2 with no diabetes (67.7% versus 21.5%; P<0.001). Overall, 36.8% of those with eGFR=15–59 ml/min per 1.73 m2 had either ACR or protein-to-creatinine ratio assessed, although 82.6% of individuals had their albuminuria assessed using any method, including urinalysis (although information on the rationale for urinalysis was not available).

View this table:
Table 2.

Albuminuria measurement in 2 years before the index date among people with eGFR=15–59 ml/min per 1.73 m2 by type of measurement and diabetes status

Of those with eGFR=15–59 ml/min per 1.73 m2 and diabetes, more people (42.3%) had albuminuria (assessed with any method) compared with people with eGFR=15–59 ml/min per 1.73 m2 and no diabetes (18.0%; P<0.001) (Table 2).

Use of Guidelines for Concordant Preventive Medications among Those with CKD with and without Diabetes

Among those with eGFR=15–59 ml/min per 1.73 m2 and moderate or severe albuminuria, a significantly higher proportion of people with diabetes were prescribed guideline-recommended medications (an ACEi/ARB or a statin) in the following year compared with those without diabetes (Table 3). In those with eGFR=15–59 ml/min per 1.73 m2 and albuminuria, 82.1% of those with diabetes received an ACEi or an ARB compared with 63.2% of those without diabetes. Overall, more individuals with eGFR=15–59 ml/min per 1.73 m2 and diabetes (irrespective of albuminuria) were on ACEi/ARB (78.3%) compared with those without diabetes (58.1%), suggesting that ACEi/ARB use may have been driven more by having diabetes than the presence of albuminuria (Table 3).

View this table:
Table 3.

Proportion of people with CKD defined by either eGFR=15–59 ml/min per 1.73 m2 or albuminuria (with normal or unmeasured eGFR) dispensed an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker or a statin in the following year by diabetes status

We compared the use of statins in those with diabetes and eGFR=15–59 ml/min per 1.73 m2 and those age 50 years old and older with eGFR=15–59 ml/min per 1.73 m2 and no diabetes given that guidelines recommend statin use in both of these groups (12). Those with diabetes were prescribed a statin 64.6% of the time compared with only 39.2% in those without diabetes (P<0.001).

Among the 65,015 people with CKD defined by moderate or severe albuminuria only (with normal or unmeasured eGFR), we also noted that a significantly higher proportion of people with diabetes were prescribed guideline-recommended medications compared with people without diabetes. Specifically, 76.3% of those with diabetes were prescribed an ACEi/ARB compared with 26.8% of those without diabetes, whereas 63.1% of those with diabetes were prescribed a statin compared with 31.6% of those without diabetes but over the age of 50 years old (both P<0.001) (Table 3).

Participant Characteristics–Diabetes Cohort

The diabetes cohort initially included 329,774 people with a diagnosis of diabetes between April 1, 1994 and March 31, 2012. Of these, 92,470 people were excluded, because they were not included in the Alberta Health registry for the 2012 and 2013 fiscal years (many because they had died as of March 31, 2012), and 1655 people were excluded, because they were younger than 18 years old as of March 31, 2012. The final diabetes cohort included 235,649 people. The mean age was 61.7 years old, with 46.8% women and 4.4% of First Nations status (Table 4). Duration of diabetes was between 1 and 10 years for the majority (58.6%) of the cohort. Only 31.8% of the population had good glycemic control (A1c under 6.5%), whereas 21.2% had poor glycemic control (A1c over 8.0%), and 6.6% had no prior A1c measurement (Table 4).

View this table:
Table 4.

Characteristics of the diabetes cohort (n=235,649)

Screening among Those with Diabetes

We found that only 41.8% of those with diabetes (regardless of CKD status) received an ACR measurement and that 73.2% received an SCr measurement over a 1-year period.

Discussion

In this population-based cohort, we noted a significant evidence to care gap among all patients with CKD, although the gap was largest among people with CKD and no diabetes. Despite guidelines recommending that all people with albuminuria should receive an ACEi or an ARB (5), we found that, even among those with albuminuria, approximately 80% of those with diabetes, albuminuria, and eGFR=15–59 ml/min per 1.73 m2 received such treatments compared with only approximately 60% of those with eGFR=15–59 ml/min per 1.73 m2 and albuminuria but no diabetes. There was an even greater difference in those with albuminuria only (and GFR≥60 ml/min per 1.73 m2) who were prescribed an ACEi or an ARB by diabetes status (76.3% of those with diabetes were prescribed an ACEi or ARB compared with only 26.8% of those without diabetes). We also noted significant differences in care for those with and without diabetes with respect to statin use, with statin use occurring in at least 25% more people with diabetes (compared with those without diabetes), suggesting that health care providers may identify diabetes as a more important marker of risk for cardiovascular disease compared with CKD (4). In addition, we noted that a minority of people with CKD were risk stratified using ACR or protein-to-creatinine ratio tests and that this assessment was more common in people with concomitant diabetes. Taken together, this information suggests that, although care can be improved across all people with CKD, particular attention is required for people without diabetes. These findings illustrate the importance of routine measurement of quality indicators with the goal of improving the performance of a health care system.

Currently, there are no validated quality indicators for CKD. Recently, a group has used a modified Delphi method to develop quality indicators for CKD (14), although not all of these quality indicators can be measured using routinely collected laboratory and administrative data. Of note, the quality indicators that we selected were included in their list, including the use of ACEis and ARBs and the control of LDL/non-HDL cholesterol (typically done using statins). The authors also recommended use of a general urine test in people with diabetes to evaluate for kidney disease, which we also assessed (14).

In comparison with other published literature, a previous study has shown that 64% of people with CKD and an indication for renin-angiotensin system blockers received an ACEi or an ARB (15), similar to our study. Statin use has also been reported to be low in people with CKD, with a previous study done before the release of KDIGO guidelines noting that only 47% of people with CKD were treated with a statin (16). With respect to evaluating people with diabetes for the presence and severity of CKD, a previous study conducted in France identified similar findings, with only 29% of 8926 adults having a claim for albuminuria tests (17), similar to what we observed. The consistency of our findings with other studies emphasizes the need for monitoring quality indicators across other health care systems, because similar gaps in care likely exist in other health systems.

There were several limitations of this paper, including that our analysis was limited to 1 year and because we used administrative data, that we lack the detailed clinical information as to why people were not receiving guideline-recommended medications and testing. It is likely that many barriers exist at the level of the patient/provider system, including inadequate knowledge about the importance of CKD as a risk factor for heart disease, the importance of albuminuria in assessing risk in CKD, and other issues, such as poor communication between patients and providers and patient-level financial barriers. However, our study had important strengths, including its population-based design and a very large sample size (enabling precise estimates). In addition, we only included people with confirmed CKD (GFR<60 ml/min per 1.73 m2 persistent for at least 3 months), and the quality indicators used were on the basis of high-quality guidelines supported by data from randomized trials. As such, our results are likely comparable with those from other first world countries with universal health care.

The differences that we observed in patients with CKD with and without diabetes may be due to the facts that people with diabetes need more active ongoing management (in comparison with early CKD, which is largely silent), may be better attuned to the importance of preventive medications, and may visit their primary care providers more often. Primary care providers may also be more familiar with guidelines for people with diabetes than guidelines for the care of those with CKD.

In conclusion, we have identified large gaps in care, including lower than expected prescription of guideline-recommended medications. We also identified low use of risk stratification in those with CKD and screening in people with diabetes. The largest care gap was noted in those with CKD and no diabetes (regardless of whether CKD was determined using eGFR=15–59 ml/min per 1.73 m2 or albuminuria). Future studies are needed to understand why patients are not receiving these cost-effective preventive medications and determine why there is such a large difference in care between patients with CKD with and without diabetes. Our work illustrates the importance of measuring health system performance as the first step in a quality improvement process to improve care and outcomes for people with and at risk for CKD.

Disclosures

None.

Acknowledgments

This study is, in part, on the basis of data provided by Alberta Health and Alberta Health Services.

This study was funded through an Alberta Innovates–Health Solutions Collaborative Research & Innovation Opportunity Team Grant.

The interpretation and conclusions contained herein are those of the researchers and do not necessarily represent the views of the Government of Alberta or Alberta Health Services. The Government of Alberta, Alberta Health, and Alberta Health Services do not express any opinion in relation to this study.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • Received August 16, 2016.
  • Accepted January 25, 2017.

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A Population-Based Analysis of Quality Indicators in CKD
Liam Manns, Nairne Scott-Douglas, Marcello Tonelli, Robert Weaver, Helen Tam-Tham, Christy Chong, Brenda Hemmelgarn
CJASN May 2017, 12 (5) 727-733; DOI: 10.2215/CJN.08720816
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