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Published ahead of print on May 31, 2006
Clin J Am Soc Nephrol 1: 796-801, 2006
© 2006 American Society of Nephrology
doi: 10.2215/CJN.00150106
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Epidemiology and Outcomes

Kidney Function and Use of Recommended Medications after Myocardial Infarction in Elderly Patients

Wolfgang C. Winkelmayer, David M. Charytan, M. Alan Brookhart, Raisa Levin, Daniel H. Solomon, and Jerry Avorn

Division of Pharmacoepidemiology and Pharmacoeconomics and the Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

Address correspondence to: Dr. Wolfgang C. Winkelmayer, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, 1620 Tremont Street, Suite 3030, Boston, MA 02120. Phone: 617-278-0036; Fax: 617-232-8602; E-mail: wwinkelmayer{at}partners.org


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Several studies have found reduced use of recommended medications after myocardial infarction (MI) in patients with impaired kidney function. However, the reasons for such undertreatment are not well understood. A total of 1380 Medicare patients who survived at least 90 d after MI and had prescription drug coverage through Pennsylvania’s medication assistance program for the elderly were studied. Filled prescriptions were used to assess use of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), ß blockers, and statins within 90 d of MI. Patients’ demographics, comorbidities, and health care utilization before MI also were ascertained. We used logistic regression to test the association between kidney function and postdischarge use of each medication. Overall, 619 (45%) patients filled a prescription for a ß blocker, 675 (49%) received an ACEI or ARB, and 406 (29%) received a statin after discharge but within 90 d after their admission for MI. Reduced kidney function was associated with both lower ß blocker and statin use (P = 0.01 and P = 0.002, respectively), but after multivariate adjustment, these associations disappeared (P = 0.23 and P = 0.62, respectively). Use of ACEI or ARB was nearly half in patients with estimated GFR <30 ml/min compared with patients with better kidney function in univariate and multivariate analyses (P < 0.001). Analyses using serum creatinine measurements rather than estimations of GFR yielded similar results. Differences in other characteristics such as age, rather than kidney function, may be responsible for much or all the reported reduction in use of preventive medications after MI seen in patients with chronic kidney disease.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
In the United States, approximately 565,000 first and 300,000 recurrent myocardial infarctions (MI) are estimated to have occurred in 2005, and the case fatality remains high (approximately 9%) despite recent advances in acute coronary care (1). In addition to other established risk factors for MI, several recent studies have revealed a clear relationship between reduced kidney function and increased risk for MI (2). Diminished kidney function also has been linked to reduced survival after MI (3–5). This excess mortality has been attributed to a greater prevalence of other cardiovascular risk factors (e.g., smoking, hyperlipidemia, hypertension, hyperhomocysteinemia) in patients with more advanced chronic kidney disease (CKD) (6). Patients with more advanced stages of CKD also receive less acute therapeutic intervention, such as thrombolysis, angiography, and coronary revascularization, and are less likely to be administered recommended medications during their hospitalization for MI (aspirin, ß blockers, and statins) (3–5,7,8). Whereas reduced kidney function has been reported to be associated with fewer medications in hospital or at discharge, use of such therapies after discharge has not been well studied. Furthermore, previous studies have described crude associations that were not adjusted for other important patient characteristics. We sought to determine whether outpatient use of recommended medications within 90 d of MI was lower in patients with reduced kidney function when accounting for differences in other potentially confounding characteristics.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Study Population
We studied 2103 patients who were >65 yr old and had experienced an MI in Pennsylvania in 1999 and 2000. Primary hospital records were used to validate the diagnosis of acute MI (9); this population also was used for a validation study of claims-based algorithms to identify patients with CKD (10). The source population included all Medicare beneficiaries in Pennsylvania who were also enrolled in that state’s Pharmaceutical Assistance Contract for the Elderly (PACE) in 1999 and 2000. The Pharmaceutical Assistance Contract for the Elderly and the related PACE Needs Enhancement Tier (PACENET; from here on, both are referred to as PACE) are state-run pharmacy benefits programs that pay for medications for low- and middle-income elderly (annual gross income ≤$23,500 if single or ≤$31,500 if married). We previously identified all hospitalization episodes for review when they met the following criteria: International Classification of Diseases, Ninth Revision, Clinical Modification 410.xx ("acute myocardial infarction") as a discharge diagnosis in the primary or secondary position or diagnosis-related group (DRG) codes of 121, 122, or 123 during 1999 or 2000.

Main Exposure Variable: Assessment of Kidney Function
For each patient, we considered the first serum creatinine measurement after admission for MI, as well as age, gender, and race to estimate the GFR (eGFR) using the abbreviated version of the modification of diet in renal disease (MDRD) formula (11): eGFR = 186 x (serum creatinine level [in mg/dl])–1.154x (age [in years])–0.203. For women and black individuals, the product of this equation was multiplied by a correction factor of 0.742 and/or 1.21, respectively. We then categorized all patients into three strata of baseline kidney function: eGFR ≥60, 30 to <60, and <30 ml/min per 1.73 m2 indicating stages ≤3, 4, and 5 of CKD, respectively. For trend analyses, individuals with a physiologically implausible high eGFR were assigned a maximum of 200 ml/min per 1.73 m2 (12). Several recent studies of renal function and post-MI care and outcomes used the MDRD eGFR as the main exposure variable (2,5,7). Because eGFR may not have been used widely by physicians in 1999/2000, we also conducted analyses that used categories of serum creatinine concentration as the main exposure variable. Originally, we intended to use three categories: ≤1.5, >1.5 to 3.0, and >3.0 mg/dl. Because only 39 (2.8%) patients had a serum creatinine concentration >3.0 mg/dl, we collapsed the last two categories, thereby creating a binary variable for these analyses.

Outcome Measures
For all studies of medication utilization, we required that patients survive at least 90 d from the day of admission for MI and that their hospitalization length of stay not exceed 30 d. Therefore, each patient had at least 2 mo to fill prescriptions for the study medications after discharge. Between discharge from their MI hospitalization and 90 d after admission, we ascertained whether a patient filled at least one prescription for three classes of drugs: (1) ß blockers, (2) blockers of the renin-angiotensin-aldosterone system (i.e., angiotensin-converting enzyme inhibitors [ACEI] or angiotensin receptor blockers [ARB]), and (3) statins.

Patient Covariates
From PACE eligibility files, we ascertained each patient’s race, gender, and age on the day of admission for MI. Medicare claims from the 365 d before MI admission were used to define several diagnosed comorbidities (coronary artery disease, congestive heart failure, cerebrovascular disease, peripheral artery disease, diabetes, hypertension, depression, chronic obstructive pulmonary disease, and malignancy). We also measured previous health care utilization in terms of number of hospital days and all use within the year preceding the MI of several cardiovascular drug classes: ACEI, ARB, ß blockers, calcium-channel blockers, diuretics, nitrates, and statins. These drugs were identified from all claims reflecting filled prescriptions for each patient. These include the identifier of the prescriber, the specific drug dispensed (the National Drug Code, a unique indicator of the specific compound, strength or concentration, manufacturer, and other information), and the number of days of medication supplied.

Statistical Analyses
We then stratified the study cohort by category of kidney function and estimated the proportions of patients using each drug class within 90 d and calculated the corresponding 95% confidence intervals (CI). Univariate logistic regression models were used for significance tests among the three categories of eGFR and for a test of trend using eGFR as an ordinal variable. We then inserted the components of the MDRD formula (except for creatinine) into multivariate logistic regression models, as well as all other variables that were associated with the outcome of interest at a multivariate P < 0.10 to determine multivariate odds ratios (OR) and 95% CI.

We used SAS for Windows (release 8.2; SAS Institute, Cary, NC) for all calculations. All tests were two-sided and used a statistical significance threshold of P = 0.05. The protocol was approved by the Center for Medicare and Medicaid Services and the Institutional Review Board of Brigham and Women’s Hospital.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
Of 2103 patients in the data set, creatinine was unavailable in 227 (10.8%). Of the remaining 1876 patients, 492 (26.2%) died during the first 90 d after admission. We also excluded four patients whose length of stay exceeded 30 d. Of the remaining 1380 patients, 503 (37%) had an eGFR ≥60 ml/min per 1.73 m2, 693 (50%) had an eGFR between 60 and 30 ml/min per 1.73 m2, and 184 (13%) had an eGFR <30 ml/min per 1.73 m2. One thousand (76.3%) patients had a serum creatinine of ≤1.5 mg/dl, whereas 380 (23.7%) had values >1.5 mg/dl. Table 1 describes important demographic and health-related characteristics of these patients, stratified by baseline kidney function. Patients with lower eGFR were older, tended to be women, and were more likely to have received a diagnosis of congestive heart failure, diabetes, hypertension, and peripheral artery disease before their MI. They also had spent more days in the hospital in the year before their MI, and their hospitalization for MI took longer (all P < 0.001 except for gender, where P = 0.004). Before MI admission, ß blocker (P = 0.38) and statin (P = 0.06) use did not differ by eGFR, whereas ACEI/ARB (P < 0.001) were used more frequently in patients with lower kidney function.


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  		Table 1. Characteristics of study patients by level of kidney functiona

 
Use of Recommended Medications after Discharge
Overall, 619 (45%) patients filled a prescription for a ß blocker, 675 (49%) received an ACE inhibitor or ARB, and 406 (29%) received a statin after discharge but within 90 d after their admission for MI. The proportions of patients who filled each of these drugs in each group of kidney function are shown in Figure 1.


Figure 1
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  		Figure 1. Use of recommended medications after myocardial infarction (MI) by level of kidney function (estimated GFR [eGFR] in ml/min per 1.73 m2; unadjusted). ACE, angiotensin-converting enzyme; ARB, angiotensin-II receptor blocker.

 
Outpatient use of ß blockers after discharge seemed initially to decline with lower kidney function. In univariate analyses, it was 49.0% (95% CI 44.7% to 53.4%), 43.3% (95% CI 39.6% to 47.0%), and 39.1% (95% CI 32.1% to 46.2%) among patients with eGFR of ≥60, 30 to <60, and <30 ml/min per 1.73 m2, respectively (P = 0.01 for trend; Figure 1). However, multivariate adjustment rendered this association nonsignificant (P = 0.23). In categorical analysis, patients in the lowest category of eGFR had 33% lower odds of ß blocker use compared with those in the highest group (OR 0.67; 95% CI 0.47 to 0.94). After multivariate adjustment, however, no association was found (OR 0.80; 95% CI 0.55 to 1.15; Table 2).


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  		Table 2. Determinants of medication use within 90 d after MIa

 
A similar pattern was found for statin use. A prescription for a statin was filled by 32.9% (95% CI 28.8% to 37.0%), 28.6% (95% CI 25.2% to 31.9%), and 23.4% (95% CI 17.3% to 29.5%) of patients in the three eGFR groups (P = 0.002 for trend; Figure 1), but after multivariate adjustment, no detectable association persisted between eGFR and statin use (multivariate P = 0.62 for trend). Patients in the group with the lowest kidney function initially seemed 37% less likely to fill a statin prescription compared with individuals with better kidney function (OR compared with eGFR ≥60 ml/min per 1.73 m2 0.63; 95% CI 0.42 to 0.92), but after adjustment for the noncreatinine components of the MDRD formula and other significant predictors of statin use, this association was no longer present (OR 0.88; 95% CI 0.58 to 1.33; Table 2).

By descending stratum of kidney function, a prescription for an ACEI or ARB was filled by 50.6% (95% CI 46.2% to 55.0%), 51.2% (95% CI 44.5% to 55.0%), and 35.9% (95% CI 28.9% to 42.8%) of patients, respectively. From logistic regression, we found no linear association between eGFR and the likelihood of receiving an ACEI or ARB in univariate (P = 0.14 for trend) or multivariate models (adjusted for other demographic and clinical characteristics; multivariate P = 0.21 for trend). Using categorical analysis, however, patients whose eGFR was <30 ml/min per 1.73 m2 had a lower rate of ACEI or ARB use compared with each of the other eGFR groups (P < 0.001; Figure 1, Table 2). After adjustment for the other components of the MDRD formula and other variables with multivariate P < 0.10 (number of hospital days in year before MI, previous diagnosis of diabetes, previous diagnosis of hypertension), these findings remained essentially identical: Patients in the lowest eGFR group were 45% less likely to receive an ACEI or ARB compared with those in the highest eGFR group (OR 0.55; 95% CI 0.39 to 0.77; Table 2) and 47% less likely compared with patients in the intermediate eGFR group (OR 0.53; 95% CI 0.38 to 0.75).

Compared with these analyses based on eGFR, analyses that used serum creatinine concentrations yielded very similar findings. Figure 2 shows the proportion of patients who received the study medications, stratified by baseline serum creatinine concentration. In patients whose kidney function was worse (serum creatinine >1.5 mg/dl), the respective odds of receiving the study drugs after MI were reduced by 25% (ß blockers), 31% (statins), and 32% (ACEI or ARB) in univariate analyses (Table 3), compared with patients whose serum creatinine concentrations were lower (≤1.5 mg/dl). As seen in the analyses based on eGFR, only the association between lower kidney function and reduced use of ACEI or ARB remained after adjustment for other factors (OR 0.69; 95% CI 0.54 to 0.88).


Figure 2
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  		Figure 2. Use of recommended medications after MI by level of kidney function (serum creatinine; unadjusted). Serum creatinine concentration in mg/dl.

 

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  		Table 3. Serum creatinine as a determinant of medication use within 90 d after MIa

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
In this study of elderly MI survivors, we found that use of several recommended medications for secondary prevention after discharge from MI differed by baseline kidney function. Although our findings confirm earlier observations of univariate associations between kidney function and medication use after MI conceptually (3–5,8), they are novel in that medication use was assessed in the outpatient setting after discharge rather than during the time of hospitalization or at discharge, and we used claims data reflecting prescriptions that were actually filled by the patient rather than based on physicians’ recorded prescribing. Therefore, our analyses are one step closer to the relevant event, the patient’s taking the recommended medication. Our study goes beyond the scope of previous reports in that we evaluated whether differences in other patient characteristics may have confounded the graded rates of medication use found across the spectrum of kidney function. When adjusting for age, gender, race, and several comorbid conditions, kidney function was no longer an independent predictor of ß blocker and statin use after discharge from MI. The only difference that remained after multivariate adjustment was a lower rate of ACEI or ARB use in patients with stage 5 CKD compared with patients with eGFR ≥30 ml/min per 1.73 m2. A similar reduction of ACEI or ARB use was found in patients with lower serum creatinine concentration. These observations may be explained by physicians’ reluctance to prescribe these drugs in elderly patients with advanced kidney disease for the fear of adverse events such as hyperkalemia or a sudden increase in serum creatinine (13). It is interesting that ACEI or ARB and, maybe, statins were used more frequently in patients with lower eGFR before their event.

The lack of association between kidney function and ß blocker and statin use offers an alternative explanation to what has been termed "renalism" or "therapeutic nihilism" in patients with kidney disease. Instead, other factors seem to confound these apparent associations (5,7). A leading candidate is age, because numerous studies have demonstrated clearly that age is associated with lower treatment rates in several diseases and indications (14–18), and the prevalence of reduced kidney function increases with age (5,19,20). Indeed, solely adjusting these associations for age rendered most of them no longer significant (data not shown). If this finding is confirmed in larger studies, then "renalism" may be simply another manifestation of the known treatment bias against older patients. By contrast, unrelated comorbidity has been shown to be associated with reduced treatment rates in other diseases (21). Further research is needed to clarify this point.

Our study has several limitations. We were not able to study use of aspirin. This medication cannot reliably be ascertained in databases such as ours, because it is available over the counter at a smaller cost than the required copayment. We pooled ACEI and ARB use even though the efficacy of ARB after MI was not established until after our study period (22,23). Therefore, our approach assumes that off-label use of ARB at that time, especially in patients who did not tolerate ACEI, was acceptable to comply with this quality-of-care indicator. Only 7% of patients received an ARB after MI in our study. We cannot be certain whether patients actually took the prescriptions that they filled, but filled prescription data are closer to this event of interest than other frequently used methods to assess medication use, such as questionnaires or medical records. In the case of failure to record a filled prescription, we cannot differentiate whether the patient did not receive a prescription at discharge or chose not to fill it. Similarly, we do not know whether outpatient physicians had access to these patients’ measurements of renal function during hospitalization. Our assessment of kidney function is limited by the questionable reliability of the MDRD formula in elderly patients. However, analyses based on serum creatinine measurements yielded similar results. Finally, we were unable to dissect further the relationship between use of a study drug before MI and after discharge from it. This may be important especially in light of the observed greater use of ACEI or ARB and statins in patients with more reduced kidney function before their MI. Larger study populations or studies based solely on patients who had not previously received these drugs are needed to understand this behavior.


   Conclusion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
This study investigating use of recommended medications casts some doubt on whether kidney function is an independent determinant of quality care after discharge from MI. Renal function may be a proxy solely for the well-described phenomenon of treatment bias against the elderly. Further study on adoption of and persistence with long-term treatment strategies to prevent recurrent cardiovascular events is warranted.


   Acknowledgments
 
This work has been supported by a Scientist Development Grant from the American Heart Association (AHA 0535232N) and a Norman S. Coplon Extramural Research Program Award from Satellite Research, Mountain View, CA. W.C.W. was a 2004 T. Franklin Williams Scholar in Geriatric Nephrology and a recipient of the ASN-ASP-Junior Development Award in Geriatric Nephrology, jointly sponsored by the Atlantic Philanthropies, the American Society of Nephrology (ASN), the John A. Hartford Foundation, and the Association of Subspecialty Professors (ASP).


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

Received January 12, 2006. Accepted April 17, 2006.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

  1. Heart Disease and Stroke Statistics—2005 Update, Dallas, American Heart Association, 2005
  2. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY: Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351: 1296–1305, 2004[Abstract/Free Full Text]
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  5. Anavekar NS, McMurray JJ, Velazquez EJ, Solomon SD, Kober L, Rouleau JL, White HD, Nordlander R, Maggioni A, Dickstein K, Zelenkofske S, Leimberger JD, Califf RM, Pfeffer MA: Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. N Engl J Med 351: 1285–1295, 2004[Abstract/Free Full Text]
  6. Shlipak MG, Fried LF, Cushman M, Manolio TA, Peterson D, Stehman-Breen C, Bleyer A, Newman A, Siscovick D, Psaty B: Cardiovascular mortality risk in chronic kidney disease: Comparison of traditional and novel risk factors. JAMA 293: 1737–1745, 2005[Abstract/Free Full Text]
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  9. Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH: Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: Estimating positive predictive value on the basis of review of hospital records. Am Heart J 148: 99–104, 2004[CrossRef][Medline]
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  22. Pfeffer MA, McMurray JJ, Velazquez EJ, Rouleau JL, Kober L, Maggioni AP, Solomon SD, Swedberg K, Van de Werf F, White H, Leimberger JD, Henis M, Edwards S, Zelenkofske S, Sellers MA, Califf RM; Valsartan in Acute Myocardial Infarction Trial I: Valsartan, captopril, or both in myocardial infarction complicated by heart failure, left ventricular dysfunction, or both. N Engl J Med 349: 1893–1906, 2003[Abstract/Free Full Text]
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W. C. Winkelmayer, R. Levin, and S. Setoguchi
Associations of Kidney Function with Cardiovascular Medication Use after Myocardial Infarction
Clin. J. Am. Soc. Nephrol., September 1, 2008; 3(5): 1415 - 1422.
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