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original article

http://www.kidney-international.org & 2012 International Society of Nephrology

Reporting of the estimated glomerular filtration rate was associated with increased use of angiotensin-converting enzyme inhibitors and angiotensin-II receptor blockers in CKD Arsh K. Jain1,2, Meaghan S. Cuerden1, Ian McLeod3, Brenda Hemmelgarn4, Ayub Akbari5, Marcello Tonelli6, Rob R. Quinn4, Matt J. Oliver7 and Amit X. Garg1,2,8 1

London Kidney Chemical Research Unit, Division of Nephrology, Department of Medicine, London Health Science Center, University of Western Ontario, London, Canada; 2Department of Epidemiology and Biostatistics, University of Western Ontario, London, Canada; 3 Department of Statistical & Actuarial Sciences, University of Western Ontario, London, Canada; 4Faculty of Medicine, University of Calgary, Calgary, Canada; 5Department of Medicine, Kidney Research Centre, Ottawa Health Research Institute, University of Ottawa, Ontario, Canada; 6Division of Nephrology, Department of Medicine, University of Alberta, Edmonton, Canada; 7Division of Nephrology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada and 8Institute for Clinical Evaluative Sciences, Toronto, Canada

Many guidelines suggest that angiotensin-converting enzyme inhibitors and angiotensin-II receptor antagonists (collectively referred to as renin, angiotensin, aldosterone system blockers (RAAS blockers)) are the preferred treatment for hypertension in most patients with chronic kidney disease (CKD). Improving the recognition of CKD with the introduction of eGFR reporting was intended to have more patients recognized with and treated for this disease. To quantify this, we examined trends in RAAS-blocker use over an 88-month period before and after routine eGFR reporting in southwestern Ontario, Canada. An intervention analysis with seasonal time-series modeling on linked health administrative data for 45,361 ambulatory residents with CKD (eGFR stages 3–5) older than 65 years was performed with a primary outcome of RAAS-blocker usage. The reporting of eGFR was associated with a significant increase in the use of RAAS blockers, as the prescription rate was 571 per 1000 patients with CKD prior to reporting but improved to 607 per 1000 after reporting. There was a significant increase in RAAS-blocker use attributable to eGFR reporting of 19 per 1000 CKD patients. Since about 8% of the adult population has CKD, this equates to about 15,200 new patients receiving RAAS-blocker treatment by 1 year after the introduction of eGFR reporting in community laboratories. Thus, eGFR reporting contributes

Correspondence: Arsh K. Jain, London Kidney Clinical Research Unit, Division of Nephrology, Department of Medicine, University of Western Ontario, Room ELL-108, Westminster Tower, 800 Commissioners Road East, London, Ontario, Canada N6A 4G5. E-mail: [email protected] Received 12 July 2010; revised 10 November 2011; accepted 13 December 2011; published online 21 March 2012 1248

to improved, guideline-appropriate care of older patients with CKD. Kidney International (2012) 81, 1248–1253; doi:10.1038/ki.2012.18; published online 21 March 2012 KEYWORDS: angiotensin-converting enzyme inhibitors; chronic kidney disease; epidemiology and outcomes; glomerular filtration rate

Moderate to severe chronic kidney disease (CKD; CKD stages 3–5 (ref. 1)) refers to an estimated glomerular filtration rate (eGFR) o60 ml/min per 1.73 m2 and affects about 8% of the adult population in North America.2 Multiple international guidelines indicate that the use of angiotensin-converting enzyme inhibitors and angiotensin-II receptor antagonists (collectively referred to as renin, angiotensin, aldosterone system blockers (RAAS blockers)) is the preferred treatment option for hypertension in most patients with CKD.3,4 Despite these recommendations, in multiple jurisdictions a significant number of patients with CKD are not taking RAAS blockers (use ranges from 36 to 49%).5–8 This underuse has been attributed to an under-recognition of CKD primarily in the primary care setting. To improve the detection of CKD, multiple international societies now suggest that kidney function be estimated with the use of an equation, and that this eGFR result be reported along with the serum creatinine.9–11 It is believed that eGFR reporting removes the ‘guess work’ required to identify a patient as having CKD when simply looking at the serum creatinine in isolation. eGFR reporting has been shown to markedly improve detection of CKD.12 Improving the recognition of CKD with the introduction of eGFR reporting may mean that more patients are treated with RAAS blockers. To study this issue we examined trends in RAAS-blocker use after the Kidney International (2012) 81, 1248–1253

original article

AK Jain et al.: eGFR reporting increases RAAS-blocker use

introduction of eGFR reporting in southwestern Ontario, Canada.

Thus, even when accounting for the increasing trend in use noted in the pre-eGFR reporting time period, the initiation of eGFR reporting itself was associated with an almost 2%

RESULTS

640 Rate per 1000 CKD patients

The number of patients, 66 years or older, who had moderate to severe CKD (at least one eGFR o60 ml/min per 1.73 m2) rose gradually during the study period, from 10,854 in early 2003 to 15,846 in early 2008. These patients had characteristics that remained similar for the duration of the study (Table 1). Just under one-third were diabetic and about 70% were hypertensive. Before the introduction of eGFR reporting, the use of RAAS blockers increased slightly from early 2003 to late 2005 (571 per 1000 patients in late 2005). After the introduction of eGFR reporting in January 2006, the rate of prescriptions for these drugs increased suddenly and significantly. By early 2008, the rate of prescriptions had increased to 607 per 1000 (Figure 1). According to the model, the increase in RAASblocker use attributable to eGFR reporting (o2) was 19 per 1000 CKD patients (P ¼ 0.034). The increase in RAASblocker use attributable to eGFR reporting in each 4-month interval during the year 2006 (o1) was 4 per 1000 (P ¼ 0.093).

eGFR reporting initiated

620 600 580 560 540 520 500 2003

2004

2005

2006

2007

Study year

Figure 1 | Age- and sex-standardized rate of RAAS-blocker use. Each bar represents a 4-month interval. The solid line with 95% confidence intervals represents the forecasted rate of RAAS-blocker use using an autoregressive integrated moving average model. CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; RAAS blocker, renin, angiotensin, aldosterone system blocker.

Table 1 | Characteristics of patients with an eGFR o60 ml/min per 1.73 m2 (primary group) Before eGFR reporting

Characteristic

After eGFR reporting

January 2003 (n=10,854)

January 2004 (n=10,825)

January 2005 (n=13,094)

January 2006 (n=14,399)

January 2007 (n=15,175)

January 2008 (n=15,846)

Female (%) Median eGFR

65 51

63 50

64 50

64 50

63 50

63 50

eGFR groups (%) 59–45 44–30 29–15 o15 Median age

67 25 7 1 79

66 26 7 1 79

67 26 7 1 79

66 26 7 1 79

66 26 7 1 79

67 26 7 1 79

Age groups (%) 66–70 71–75 76–80 81–85 X86 Diabetesa (%) Hypertensionb (%) History of MIc (%) History of heart failured (%)

13 21 26 21 18 22 70 4 5

13 21 26 22 18 23 70 4 5

14 21 26 22 17 23 70 4 4

14 21 25 23 18 24 70 3 4

15 21 24 23 19 26 71 3 4

15 20 24 22 19 28 71 3 3

Medications usage (%) Thiazide diuretics a-Blockers Ca2++ channel blockers b-Blockers Vasodilators Loop diuretics Potassium sparing diuretics

31 4 31 32 2 25 14

34 4 32 33 1 25 13

35 4 32 35 1 24 12

36 4 34 35 1 23 12

37 4 35 36 1 23 11

37 3 34 36 1 22 10

Abbreviations: eGFR, estimated glomerular filtration rate (in ml/min per 1.73 m2); MI, myocardial infarction. The characteristics of the last interval are presented here. The presence of comorbidities was determined by using data for 3 years preceding cohort entry. a The presence of this diagnosis was determined with the use of the Ontario Diabetes Database, according to Hux et al.26 b The presence of this diagnosis was determined with the use of an algorithm defined by Tu et al.27 c The presence of this diagnosis was determined by any of the following codes (ICD-9: 410 ; ICD-10-CA: I21). d The presence of this diagnosis was determined by any of the following codes (ICD-9: 428; ICD-10-CA: I50).

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ACF of residuals ACF

0.5 –0.5 0

1

2 Lag

3

4

P-value

P-values for Ljung–Box statistic 0.6 0.0 2

4

6

8

10

Lag

Figure 2 | Confirmation of model appropriateness. Residual autocorrelation was not detected using Ljung–Box w2-test statistics for different lag periods; residual autocorrelation was not detected by visual assessment of standardized residual autocorrelation function plot. ACF, autocorrelation function.

increased use of RAAS blockers among CKD patients. Appropriateness of model fit was confirmed using suitable tests (Figure 2). Secondary outcomes

The introduction of eGFR reporting had no significant impact on incident RAAS-blocker use among those patients with moderate to severe CKD and no prior history of RAASblocker use. Just before the introduction of eGFR reporting RAAS-blocker use was 39 per 1000 patients, whereas by early 2008 the rate was 45 per 1000 patients. This group was 12% diabetic and 54% hypertensive. The group of patients, 66 years or older, whose eGFR was between 60 and 89 ml/min per 1.73 m2 had a median eGFR of 73 ml/min per 1.73 m2. They had less comorbidities and were slightly younger than the moderate to severe CKD group. After the introduction of eGFR reporting in January 2006, the rate of prescriptions for RAAS blockers also increased significantly from 443 in late 2005 to 469 per 1000 patients in early 2008. According to the model, the increase in RAAS-blocker use attributable to eGFR reporting was 21 per 1000 CKD patients (Po0.0001). There was no significant increase in RAAS-blocker use in the normal kidney function group, i.e., those with an eGFR of 90 ml/min per 1.73 m2 or greater. Additional observations

Using the same analytic strategy, no change was noted in the use of proton pump inhibitors in those with an eGFR o60 ml/min per 1.73 m2 or those with an eGFR between 60 and 90 ml/min per 1.73 m2, which could be attributed to eGFR reporting, nor was there any change in the other medications studied: thiazide diuretics, a-blockers, calcium channel blockers, b-blockers, vasodilators, loop diuretics, and potassium sparing diuretics. DISCUSSION

Our results indicate that eGFR reporting was associated with improved, guideline-appropriate, health-care delivery to 1250

older patients with CKD. After the introduction of eGFR reporting, RAAS-blocker use suddenly increased by about 2% in patients with CKD. As expected, there was no effect noted on those with normal kidney function. This result is similar to observations made in two previous studies. After the introduction of eGFR reporting, Wyatt et al.13 noted a 1.9% increase in RAAS-blocker use among patients with CKD in an urban Veterans Affairs primary care outpatient population (from 65 to 67%; Po0.0001). Chaudhry et al.14 noted a 6% increase in RAAS-blocker use in British Columbia, Canada (from 23 to 29%; Po0.03). These data indicate that eGFR reporting increases the recognition of CKD, which in turn results in more patients treated with RAAS blockers.15 However, a recent study by Hemmelgarn et al.16 did not find an association between eGFR reporting and RAAS-blocker use. This lack of impact may have been related to the decreasing use of these medications noted in the pre-eGFR reporting time period and/or the extensive use of RAAS blockers among the patients in this study (e.g., 77.5% of participants with diabetes and proteinuria). It may be assumed that the increase of 2% we have noted, even if it is statistically significant, is modest. However, at the population level this represents quite a large effect. For example, in the province of Ontario (according to the 2006 Canadian Census) there are about 10 million adult residents. If 8% of these individuals have moderate to severe CKD (stages 3–5), then there are 800,000 people with this condition in the community.2 Our results would indicate that eGFR reporting resulted in about 15,200 more people being treated with guideline-approved therapy over 1 year. It is important to note that no impact was seen when we restricted our CKD population to those who had not used a RAAS blocker in the past 1 year. This group likely represents individuals who could not tolerate or comply with RAAS-blocker use and also includes patients for whom RAAS-blocker use is not indicated (e.g., those without hypertension). Furthermore, as this group had smaller numbers, this may have resulted in some degree of variability that our model could not account for, resulting in a nonsignificant finding. Policy makers will be interested in our results for several reasons. First, in Ontario, changes in practice occurred in the absence of a resource-intensive educational effort on the interpretation of eGFR. Although it can be debated whether the change occurred as a result of the eGFR value or the laboratory prompt itself, this study does demonstrate that simple changes to laboratory reporting practices on their own can have an important influence on the quality of patient care. Applying similar reporting strategies to other areas of medicine may have similar beneficial effects, although this remains to be studied. Second, more accurate equations to estimate kidney function have recently been developed, and new staging systems for CKD, which incorporate proteinuria measurements, are being proposed.17 Before their adoption, policy makers might now consider how changing the Kidney International (2012) 81, 1248–1253


‘definition’ of CKD in the community will influence the effects of eGFR reporting as observed here. The greatest strength of this study is our large diverse sample and robust methodology. We studied more than 40,000 CKD (stages 3–5) patients over the course of the study. We used time-series analysis, a more robust and precise methodology than a traditional pre-post study design.18 The use of this population and study design helps protect against other secular and demographic changes that may affect other studies. Our signal was unique to RAAS blockers. There was, as expected, no impact of eGFR reporting on the use of other medications such as thiazide diuretics, a-blockers, calcium channel blockers, b-blockers, vasodilators, loop diuretics, and potassium sparing diuretics. This strengthens the assertion that eGFR reporting had a specific impact on RAAS blockers and not on all medications. Furthermore, the signal was isolated to those with mild and moderate kidney dysfunction, with no effect noted in those with normal kidney function. This demonstrates that there was specific impact on those who were identified as having abnormal kidney function according to the laboratory prompt. These two observations indicate that eGFR reporting resulted in improved recognition and adherence to CKD guidelines. However, limitations of our research approach bear consideration. RAAS blockers are typically used to reduce proteinuria and blood pressure in patients with CKD; however, the rationale for the use of RAAS blockers was not recorded in our data sets. The baseline blood pressures or degree of proteinuria have not been captured for this patient population. In addition, there are other reasons (e.g., congestive heart failure, myocardial infarction, or diabetes) for prescribing RAAS blockers. However, there was no major change in our patient comorbidities over the course of the study. This means that the change we noted is not related to changes in a particular subgroup of CKD patients. Other contemporaneous educational efforts (e.g., guidelines, education programs) could have influenced the prescription rate. However, on extensive review of the literature and in discussion with physicians from around the province, no such efforts from the government, pharmaceutical industry, or other organizations were identified that coincided with the observed changes in RAAS-blocker use. We could not reliably assess whether there was an exaggerated rate of adverse events such as hyperkalemia or acute kidney injury after the increase in RAAS-blocker use, as there were too few events in administrative data to allow for meaningful analysis. Because of insufficient sample size, we could not reliably examine RAAS-blocker use in those with an eGFR o30 ml/min per 1.73 m2 or assess other outcomes such as mortality and the development of end-stage renal disease. In conclusion, these results inform the debate as to the utility of a new method of kidney function reporting. This study demonstrates that the introduction of eGFR reporting resulted in improved adherence to CKD guidelines. Patient care has improved under the assumption that the guidelines are correct. Future work should focus on understanding the Kidney International (2012) 81, 1248–1253

original article

impact of eGFR reporting on outcomes such as mortality or end-stage renal disease. MATERIALS AND METHODS Setting and design We conducted a population-based, time-series analysis using healthcare databases in southwestern Ontario, Canada, from 1 January 2003 to 30 April 2008. All residents received universal access to hospital and physician services, and those over the age of 65 years received coverage for prescription medications such as RAAS blockers. Coverage for medical services and medications from a single provincial payer provided a comprehensive set of health administrative data that have been validated in previous epidemiologic studies.19–23 We created and followed a prespecified protocol, and obtained ethics approval from the institutional review board at Sunnybrook Health Sciences Centre, Toronto, Canada. Intervention In January 2006, Ontario medical laboratories began adding eGFR results (using the abbreviated MDRD (Modification of Diet in Renal Disease) study equation24) to serum creatinines on their laboratory reports. This change affected all adult outpatient tests. Hospital-based laboratories were not required to report eGFR. This change in reporting was implemented without a concurrent educational effort at the provincial level. In particular, no guidance was delivered to physicians about appropriate treatments for these patients. However, all eGFRs were presented with a laboratory prompt that included an explanation of eGFR ranges (Table 2). Medical laboratories continued to report a serum creatinine level along with reference ranges. Data sources Outpatient prescription medications dispensed to Ontario residents 65 years of age and older are accurately recorded in the Ontario Drug Benefits Program (ODB) database.25 Information about all Ontario hospitalizations is recorded in the Canadian Institute for Health Information Discharge Abstract Database. Vital statistics including dates of birth and death and place of residence are recorded in the Registered Persons Database. Individuals in Ontario with diabetes are recorded in the Ontario Diabetes Database using a validated algorithm.26 Hypertension was identified by following a validated algorithm developed by Tu et al.27 Gamma-Dynacare is the

Table 2 | Estimated glomerular filtration rate (eGFR) reporting laboratory prompts from Gamma-Dynacare for patients of varying degree of chronic kidney disease eGFRa 90–120 Normal eGFR. eGFRa 60–89 Slightly reduced eGFR is seen in B30% of adults 20 years or older. Rule out kidney damage in those at high risk for chronic kidney disease. eGFRa 30–59 Consistent with moderate chronic kidney disease if result confirmed by repeat measurement, with persistence for 3 months or more. eGFRa 15–29 Consistent with severe chronic kidney disease. Consider nephrology referral. Consistent with kidney failure. eGFRa o15 Reports followed by: For African Canadians, the reported eGFR should be multiplied by a factor of 1.21 and reinterpreted accordingly. Standardized serum creatinine is in widespread use throughout Ontario.29 a eGFR measured by abbreviated MDRD (Modification of Diet in Renal Disease) study equation in ml/min per 1.73 m2.

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largest outpatient laboratory service provider in southwestern Ontario. We archived all serum creatinine tests performed at Gamma-Dynacare since January 2002 in a database. The fourvariable MDRD equation24 was applied to each serum creatinine value to determine an individual’s eGFR. All serum creatinine values for the period of interest were standardized for use in the MDRD equation.28 All the databases were linked with encrypted, unique identifiers (10-digit health-card numbers). Identification of patients and outcomes We divided each year of the study period into three 4-month intervals (January through April, May through August, and September through December). In total, this resulted in 16 consecutive intervals, 9 before the reporting of eGFR, 3 the year eGFR reporting was adopted, and 4 after the full adoption of eGFR reporting. We chose to use 4-month intervals because the Ontario Drug Benefit Program will not reimburse prescriptions exceeding 100 days in duration. We examined the records of patients 66 years of age or older, which ensured that all individuals were eligible for a full year of prescription drug coverage before being considered for the study. To identify CKD patients, we identified all individuals in the ambulatory renal database at the beginning of each interval who met the prespecified eGFR cutoff. Our primary group was patients with moderate to severe (stages 3, 4, and 5) CKD, i.e., those with an eGFR o60 ml/min per 1.73 m2 at least once in the past year. We excluded all patients who were on dialysis or had received a prior kidney transplant. As a secondary outcome, incident use of RAAS blockers was assessed by limiting this group to those who had no history (in the past 1 year) of RAAS-blocker use. Also secondarily, we followed up those with mild kidney dysfunction and normal kidney function. Among those patients with mild kidney dysfunction, we hypothesized that they may be affected by eGFR reporting as their reduced kidney function was presented to their physicians (Table 2). We defined this group as those who had an eGFR between 60 and 89 ml/min per 1.73 m2 at least once in the past year. Because we did not have other laboratory data we could not confirm that these patients met the true Kidney Dialysis Outcome Quality Initiative stage 2 definition, as this would require other markers of damage, including abnormalities in blood or urine tests or imaging studies.1 We hypothesized that patients with normal kidney function (as identified by the prompt, Table 2) would be unaffected by eGFR reporting. We defined this group as those who had an eGFR 490 ml/min per 1.73 m2 at least once in the past year. For each 4-month interval, we identified all patients with at least one prescription for a drug of interest. Our primary outcome was the age- and sex-standardized prescription rate for RAAS blockers (either angiotensin-converting enzyme inhibitors and/or angiotensin-II receptor antagonists) per 1000 CKD patients. To assess the specificity of the impact of eGFR reporting, we used a tracer outcome of proton pump inhibitors. We expected eGFR reporting to have no impact on proton pump inhibitor prescriptions, because this medication is unrelated to CKD. Similarly, we did not expect eGFR reporting to impact a list of other medications, as unlike RAAS blockers these medications did not receive emphasis in Canadian guidelines for the treatment of CKD: thiazide diuretics, a-blockers, calcium channel blockers, b-blockers, vasodilators, loop diuretics, and potassium sparing diuretics. Statistical analysis We used interventional autoregressive integrated moving average models with a seasonal autocorrelation parameter to assess whether 1252


eGFR reporting significantly affected the age- and sex-standardized rate of prescriptions per 1000 CKD patients. This type of analysis can account for the correlated nature of the data. First-order differencing was used to create a stationary time series with constant mean and variance over time. Two intervention parameters were included in the Autoregressive Integrated Moving Average time series model to estimate the effect of eGFR reporting on RAASblocker use per 1000 patients (RAAS-blocker use rate): a ramp function at each time period during the year of eGFR adoption (call this intervention parameter ‘o1’, the covariate associated with o1 was equal to 1 for April 2006, 2 for August 2006, and 3 for December 2006) and a different step function for the time points after this year (call this intervention parameter ‘o2’, the covariate associated with o2 was equal to 1 for all periods between April 2007 and April 2008, and 0 otherwise). A ramp function was chosen for the three time periods in 2006, because in April 2006 approximately 1/3 of the patients with eGFR o60 ml/min per 1.73 m2 were subject to eGFR reporting (see ‘Identification of patients and outcomes’ subsection); similarly, approximately 2/3 of the patients with eGFR o60 ml/min per 1.73 m2 in August 2006 were subject to eGFR reporting, and 3/3 of the patients with eGFR o60 ml/min per 1.73 m2 in December 2006 were subject to eGFR reporting. Therefore, it was reasonable to expect that the effect of eGFR reporting on RAASblocker use rate would be detected incrementally over the year 2006. Parameter o1 can be interpreted as the change in RAAS-blocker use rate for a one-unit change in time interval for time intervals April, August, and December 2006. Parameter o2 is the difference in average RAAS-blocker use rate between April 2007 and April 2008 (four time periods in total), to the average RAAS-blocker use rate between April 2003 and December 2005 (nine time periods in total). If o2 was found to be significantly different from 0 using a two-sided Z-test, there was a significant intervention effect. Once a model was fit, we confirmed model appropriateness by examining the autocorrelation function at different lag periods using the Ljung–Box w2-statistic; autocorrelation functions were visually examined to detect residual autocorrelation. We forecasted intra- and post-intervention values along with 95% confidence intervals. All P-values were twosided and we interpreted a value o0.05 as statistically significant. We performed our analyses using R for Windows, Version 2.10.0 (R Foundation for Statistical Computing). DISCLOSURE

All the authors declared no competing interests. ACKNOWLEDGMENTS

This study was supported by grants provided by the Canadian Institutes of Health Research (CIHR) and the Academic Medical Organization of Southwestern Ontario. AKJ was supported by a Health Professional Research Fellowship from the CIHR. AXG was supported by a Clinician Scientist Award from the CIHR. The Institute for Clinical Evaluative Sciences received funding from the Ontario Ministry of Health and Long-term Care. The opinions, results, and conclusions reported in this paper are those of the authors, and are independent from the funding sources. We thank Barbara Jones, Jeff Lamond, and the late Milton Haines for their help in providing access to Gamma-Dynacare laboratory data. This archive has been instrumental for many projects including this one. REFERENCES 1.

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