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ORIGINAL INVESTIGATION

HEALTH CARE REFORM

Comparative Effectiveness of 2 -Blockersin Hypertensive PatientsEmily D. Parker, MPH, PhD; Karen L. Margolis, MD, MPH; Nicole K. Trower, BS; David. J. Magid, MD, MPH; Heather M. Tavel, BS; Susan M. Shetterly, MS;P. Michael Ho, MD, PhD; Bix E. Swain, MS; Patrick J. O’Connor, MD, MPH

Background: Randomized controlled trials have dem-onstrated the efficacy of selected -blockers for prevent-ing cardiovascular (CV) events in patients following myo-cardial infarction (MI) or with heart failure (HF). However, the effectiveness of -blockers for preventing CV events in patients with hypertension has been ques-tioned recently, but it is unclear whether this is a class effect.

Methods: Using electronic medical record and health plan data from the Cardiovascular Research Network Hy-pertension Registry, we compared incident MI, HF, and stroke in patients who were new -blocker users be-tween 2000 and 2009. Patients had no history of CV dis-ease and had not previously filled a prescription for a -blocker. Cox proportional hazards regression was used to examine the associations of atenolol and metoprolol tartrate with incident CV events using both standard co-variate adjustment (n = 120 978) and propensity score– matching methods (n = 22 352).

Results: During follow-up (median, 5.2 years), there were 3517incidentMI,3272incidentHF,and3664incidentstroke events. Hazard ratios for MI, HF, and stroke in metoprolol tartrate users were 0.99 (95% CI, 0.97-1.02), 0.99 (95% CI, 0.96-1.01), and 0.99 (95% CI, 0.97-1.02), respectively. An alternativeapproachusingpropensityscorematchingyielded similar results in 11 176 new metoprolol tartrate users, who were similar to 11 176 new atenolol users with regard to demographic and clinical characteristics.

Conclusions: There were no statistically significant dif-ferences in incident CV events between atenolol and meto-prolol tartrate users with hypertension. Large registries similar to the one used in this analysis may be useful for addressing comparative effectiveness questions that are unlikely to be resolved by randomized trials.

Arch Intern Med. 2012;172(18):1406-1412. Published online August 27, 2012. doi:10.1001/archinternmed.2012.4276

Author Affiliations:HealthPartners Institute for Education and Research, Minneapolis, Minnesota (Drs Parker, Margolis, and O’Connor and Ms Trower); Institute for Health Research, Kaiser Permanente Colorado, Denver (Dr Magid and MssTavel and Shetterly); Denver VA Medical Center and Division of Cardiology, School of Medicine, University of Colorado, Denver (Dr Ho); and Division of Research, Kaiser Permanente Northern California, Oakland (Mr Swain).

IN THE TREATMENT OF HYPERTEN-sion, -

blockers are widely used and are one of the drug classes recommended as initial treat-ment in hypertension guide-

lines based on reduction of morbidity and mortality in placebo-controlled trials.1-5

However, following the publication of 2 large trials that found that atenolol-based regimens were less effective than other antihypertensive drugs for preven-tion of cardiovascular (CV) events in pa-tients with hypertension,6,7 the first-line status of -blockers has increasingly been called into question.3,8- 11 A recent meta-analysis including these studies found that -blockers were inferior to other agents primarily with regard to stroke preven-tion, but the authors and editorialist pointed out that data on -blockers other than atenolol were sparse enough that it is unclear whether this conclusion ap-plies to the entire -blocker class.9,12 A sec-

ond meta-analysis and editorial echoed these findings and concerns.11,13

Within the drug class of -blockers, there are differences in pharmacokinetic properties.14,15 Differences in lipophilic-ity, bioavailability, and metabolism be-tween atenolol and metoprolol tartrate may have relevance for protecting the heart.10,11

See Invited Commentaryat end of article

Despite these differences, it is unlikely that they will be compared head to head in a randomized controlled trial. There-fore, we sought to compare the effective-ness of 2 commonly used -blockers, using data from a hypertension registry from 3 large integrated health care deliv-ery systems. We compared the incidence of myocardial infarction (MI), stroke, and heart failure (HF) in adult hyperten-

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sive patients who were new users of atenolol and meto-prolol tartrate.

METHODS

STUDY SETTING ANDREGISTRY POPULATION

This report is derived from the Hypertension Registry of the Cardiovascular Research Network (CVRN). The registry in-cludes all adult patients identified as having hypertension be-tween 2000 and 2009 at 3 large integrated health care delivery systems: HealthPartners of Minnesota, Kaiser Permanente Colo-rado, and Kaiser Permanente Northern California. Electronic data on longitudinal blood pressure (BP) measurements, pre-scription drugs, laboratory test results, diagnoses, and health care utilization were available from electronic health records and administrative databases at all sites. Data from each of the health plans were restructured into a common, standardized format with identical variable names, definitions, labels, and coding.

We defined hypertension using criteria adapted from pre-vious CVRN studies16-20 based on outpatient BP readings, di-agnostic codes from outpatient and hospital records, phar-macy prescriptions, and laboratory results. Patients entered the registry on the date they first met 1 (or more) of the following criteria: (1) 2 consecutive elevated BP measurements (ie, sys-tolic BP [SBP] 140 mm Hg and/or diastolic BP [DBP] 90 mm Hg, or 130/80 mm Hg in the presence of diabetes melli-tus or chronic kidney disease [CKD]); (2) 2 diagnostic codes for hypertension (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 401.x-405.x) recorded on separate dates; (3) 1 diagnostic code for hyper-tension plus prescription for an antihypertensive medication; or (4) 1 elevated BP measurement plus 1 diagnostic code for hypertension. Blood pressure readings from emergency and ur-gent care settings were excluded because they were found to be consistently higher than other ambulatory measurements in the same patients in similar periods. To confirm that the al-gorithms designed to identify hypertensive patients were valid and that the analytic data accurately reflected the source data, we conducted a review of 450 randomly selected medical charts (150 from each site). We confirmed that hypertension was in fact incident on the date assigned by the algorithm in 96% of cases, and agreement on BP values between the electronic da-tabase and medical chart records was 98%.

VARIABLES USED IN ANALYSIS

441.7, 443.9, 444.0, and 444.2); and congestive HF (ICD-9-CM diagnosis codes 428.xx, 402.xx, and 398.91). Incident MI (ICD-9-CM code 410.xx), HF, and stroke events were de-fined using the primary International Classification of Diseases, Ninth Revision (ICD-9) codes from a discharge from an inpa-tient stay.

Other comorbidities included in the analysis were diabetes mellitus, CKD, and lipid disorders. Diabetes was defined by (1) 2 outpatient diagnoses or 1 primary inpatient discharge diag-nosis of diabetes mellitus (ICD-9-CM code 250.x); (2) a pre-scription for any antidiabetic medication other than metfor-min or thiazolidinediones; (3) a prescription for metformin or a thiazolidinedione plus a diagnosis of diabetes; or (4) a he-moglobin A1c value higher than 7% or 2 fasting plasma glu-cose values of 126 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555) on separate dates. Chronic kid-ney disease was defined by (1) 2 consecutive serum creatinine values that yield estimated glomerular filtration rates lower than 60 mL/min or (2) an International Classification of Diseases, Ninth Revision (ICD-9) diagnostic code for CKD (ICD-9-CM codes 585.1-585.9). Lipid disorders were identified by ICD-9-CM codes 272.x.

STUDY POPULATION

We used a new user design, which restricts the analysis to per-sons under observation at the start of the current course of treat-ment.21 The study population included all patients 18 years or older with hypertension during 2000 through 2009, who were started on therapy with either atenolol or metoprolol tartrate after the date of first diagnosis with no prior use of any -blocker for at least 12 months (n = 193 123). Previous use of any other class of antihypertensive drug was not an exclusion. Prescrip-tion databases were searched as far back as 1996 or to health plan enrollment if that occurred after 1996. Other -blockers, including metoprolol succinate, were not used frequently enough during the years of the study to be included in the analysis. We excluded pregnant women (n = 346). In addition, we excluded 46 809 patients who had evidence of CVD before starting therapy with atenolol or metoprolol tartrate. These exclusions were based on the previously described CV diagnosis codes as well as pro-cedure codes for cardiac bypass surgery (Current Procedural Ter-minology [CPT] codes 33510-33523 and 33533-33536) and per-cutaneous coronary interventions (CPT codes 92980-92996). To exclude patients with suspected CVD, we also excluded 24 990 patients with a visit to cardiology specialist within the year prior to starting the -blocker therapy, leaving 120 978 patients for this analysis (Figure).

STATISTICAL ANALYSISPatient age and sex were available for all patients from mem-bership databases. Race/ethnicity was obtained from outpa-tient registration data, hospital discharge records, member sat-isfaction surveys, and other research survey data sets and was available for 85% of cohort members. Systolic BPs and DBPs measured within 2 months prior to the initiation of a -blocker therapy and approximately 6 months (±60 days) after the ini-tiation of a -blocker therapy were included. Pharmacy rec-ords were used to identify dates of treatment with -blockers and other antihypertensive drug classes used within 90 days of starting the -blocker therapy.

Cardiovascular disease (CVD) was identified using diagno-ses and procedure codes from inpatient and ambulatory rec-ords. These included ischemic heart disease (ICD-9-CM diag-nosis codes 410.x-414.xx); stroke (ICD-9-CM diagnosis codes 430.xx-434.xx, 436.xx, 852.0, 852.2. 852.4, and 853.0); pe-ripheral vascular disease (ICD-9-CM diagnosis codes 441.3-

All statistical analyses were completed using SAS version 9.2 (SAS Institute Inc). Baseline characteristics were compared be-tween patients started on atenolol therapy vs metoprolol tar-trate therapy using means and standard deviations for continu-ous variables and percentages for categorical and binary variables. Cox proportional hazards models were used to compare time with outcome events between atenolol and metoprolol tar-trate. Follow-up time was computed in days from the day fol-lowing the first dispensing of the new -blocker to the date of the first observed outcome event, termination of enrollment, or December 31, 2009, whichever occurred first. Patients who were lost to follow-up were censored at the last point of con-tact. Multivariable models were adjusted for year of -blocker therapy initiation, age, sex, number of visits in the prior year, SBP at the start of -blocker therapy, lipid disorder, diabetes mellitus, CKD, and use of other antihypertensive medica-

193 123 New β-blocker users with hypertension18 years or older between 2001 and 2009

Exclusions:• 46 809 History of CVD• 24 990 Seen by cardiologist in last year• 346 Pregnant women

120 978Met inclusion criteria, included in multivariable regression analysis

91% Atenolol 9% Metoprolol tartrate

98 626 Unmatched

11 176 Atenolol users 11 176 Metoprolol tartrate usersMatched on

propensity score

22 352 Included in propensity score–matched analysis

Figure. Selection of patients for analyses. CVD indicates cardiovascular disease.

tions. In a supplemental analysis of 68 882 patients in whom we had follow-up BP data, we used linear regression to exam-ine the effect of atenolol and metoprolol tartrate on lowering SBP and DBP 6 months after the start of the -blocker therapy.

Because this is an observational study and patients were not randomized to receive either treatment, we also used alterna-tive strategies to minimize confounding by indication. To mini-mize confounding by indication, we also ran a conditional lo-gistic regression matched on propensity score. A logistic model (which included all the variables in Table 1 except for DBP) was used to generate a propensity score for the probability of being prescribed metoprolol tartrate. We then used a 5-digit greedy 1:1 matching algorithm23 to match metoprolol tartrate users to atenolol users based on propensity score. After con-ducting the propensity matching, there were 99 626 un-matched patients, leaving 22 352 matched patients for statis-tical analyses of adverse CV events and 13 908 matched patients with 6-month follow-up BPs for the analyses of BP lowering. The selection of patients for the analyses is shown in the Figure. A second alternative strategy was to conduct a sensitivity analy-sis excluding patients who had events in the first 12 months of follow-up so as to exclude those with CVD not excluded by diagnosis codes or visits to a cardiologist that could have had an impact on prescribing behavior.

RESULTS

During the follow-up period (median, 5.2 years), there were 3517 incident MIs, 3272 incident HF hospitaliza-tions, and 3664 incident strokes. Multivariable Cox pro-portional hazards regression yielded hazard ratios of 0.99, 0.99, 0.99, and 0.98 and narrow 95% confidence inter-vals that included the null value for MI, HF, stroke, and any CV event, respectively (Table 2). In the propen-sity score–matched Cox proportional hazards models, the hazard ratios for MI, HF, stroke, and any CV event were virtually identical to the multivariable results with nar-row 95% confidence intervals that included the null value (Table 2). In sensitivity analyses excluding patients who had events in the first 12 months of follow-up, the haz-ard ratios were virtually unchanged (data not shown).

Estimates and standard errors of the supplemental analysis of the BP-lowering effects of the 2 -blockers in the subgroup with follow-up measures are given in Table 3. In multivariable analysis of new -blocker us-ers, at baseline there were statistically significant differ-ences between atenolol and metoprolol tartrate users in SBPs (148.5 and 145.4 mm Hg, respectively; P .001) and DBPs (84.2 and 82.5, respectively; P .001). At the 6-month follow-up, SBPs were 137.4 and 137.5 mm Hg in the atenolol- and metoprolol tartrate-treated pa-tients, respectively (P = .82). At 6 months, DBPs were 77.3 and 77.7 mm Hg in the atenolol- and metoprolol tartrate– treated patients, respectively (P = .005). There was no sta-tistically significant difference in change in SBP and a small but statistically significant difference in change in DBP (5.9 and 5.5 mm Hg for atenolol and metoprolol tar-trate, respectively P = .005). The propensity score– matched analysis of BP lowering had similar results when comparing new atenolol and metoprolol tartrate users in SBP (144.2 and 143.3 mm Hg, respectively; P = .007) and DBP (81.3 and 80.2 mm Hg, respectively; P .001). At the 6-month follow-up, there were no statistically sig-nificant differences between atenolol and metoprolol tar-trate users in SBP or DBP. In the propensity-matched model, the mean BP lowering was slightly greater in aten-olol vs metoprolol tartrate users (7.7 and 6.7 mm Hg, re-spectively; P = .02). Atenolol lowered DBP slightly more than metoprolol tartrate (4.7 and 3.4 mm Hg, respec-tively; P .001).

COMMENT

The baseline characteristics for this cohort of new -blocker users are given in Table 1. A total of 120 978 patients without history of CVD events from the CVRN Hypertension Registry initiated treatment with either aten-olol or metoprolol tartrate between 2000 and 2009. Dur-ing this period atenolol was used in approximately 10-fold more patients than metoprolol tartrate. Patients who filled a prescription for metoprolol tartrate tended to be older, have a government insurance payer, and have more ambulatory visits. Metoprolol tartrate users had slightly lower SBPs and DBPs at the start of -blocker treat-ment, were more likely to be using other antihyperten-sive medications, and more often had lipid disorders, dia-betes, and CKD.

The objective of this study was to assess the compara-tive effectiveness of 2 -blockers, atenolol and metopro-lol tartrate, in patients without a history of CVD. To our knowledge, this study is among the first to address this important clinical question. In this retrospective cohort study comparing patients initiating -blocker treat-ment with either atenolol or metoprolol tartrate, there were no statistically significant differences in rates of in-cident MI, HF, or stroke after adjusting for potential con-founders. In addition, there were no statistically signifi-cant differences in SBP-lowering effects comparing atenolol and metoprolol tartrate.

Until recently, -blockers had been widely recom-mended as first-line therapy for hypertension,1-5 but many of the trials supporting their use had given investigators

Table 1. Descriptive Characteristic of Patients Initiating -Blocker (BB) Use Between 2001 and 2009 (CVRN Hypertension Registry)a

Total Matched on Propensity Score(n = 120 978) (n = 22 352)

Atenolol Metoprolol Tartrate Atenolol Metoprolol TartrateVariable (n = 109 798) (n = 11 180) (n = 11 176) (n = 11 176)Year of BB therapy initiation

2000 14 10 87 102001 11 6 7 62002 15 9 9 92003 15 10 10 102004 14 12 12 122005 12 15 16 152006 10 16 16 162007 8 14 14 142008 2 5 5 52009 2 4 4 4

Age, mean (SD), y 60.8 (13.0) 65.1 (13.6) 65.0 (13.4) 65.2 (13.6)Age category, y

50 20 13 14 1350-59 27 21 20 2160-69 25 25 25 2570-79 20 26 26 2680 7 15 15 15

Male 43 44 43 44Race/ethnicity

White 62 66 63 66African American 10 11 12 11Asian 9 7 8 8Nonwhite Hispanic 1 1 1 1Other/multiple/unknown 19 14 15 14

Median ambulatory visits in prior year 5.0 7.0 7.0 7.0Insurance payer

Commercial 79 71 70 71Government 21 29 28 29

SBP at start of BB therapy, mean (SD), mm Hg 148.5 (20.2) 144.0 (21.5) 144.8 (20.4) 144.0 (21.5)DBP at start of BB therapy, mean (SD), mm Hg 84.5 (13.2) 82 (12.5) 82.1 (12.6) 80.9 (12.9)Lipid disorderb 32 42 47 42Diabetes mellitusc 21 31 30 30Chronic kidney diseased

12 28 28 28ACE inhibitor or ARB use within 6 mo prior 36 46 47 46

to BB therapy initiationCCB use within 6 mo prior to BB therapy initiation 14 23 17 23Diuretic use within 6 mo prior to BB therapy initiation 51 49 54 49Other antihypertensive medication use within 6 mo 7 13 9 13

prior to BB therapy initiationAntihypertensive medications within 6 mo prior

to BB therapy initiation, No.0 32 26 26 261 26 31 32 312 26 30 32 303 6 11 8 114 1 2 1 2

Abbreviations: ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; CVRN, the Cardiovascular Research Network.

a Data are given as percentage of patients unless otherwise indicated. Percentages may not add to 100 because of rounding. b Lipid disorders were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM ) codes 272.x.

c Diabetes mellitus was defined by (1) 2 outpatient diagnoses or 1 primary inpatient discharge diagnosis of diabetes ( ICD-9-CM code 250.x); (2) prescription for any antidiabetic medication other than metformin or thiazolidinediones; (3) prescription for metformin or a thiazolidinedione plus a diagnosis of diabetes; or (4) hemoglobin A1c value higher than 7% or 2 fasting plasma glucose values of 126 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555) on separate dates.

d Chronic kidney disease was defined by (1) 2 consecutive serum creatinine values that yield estimated glomerular filtration rates lower than 60 mL/min when the Modification of Diet in Renal Disease equation22 is applied or (2) an International Classification of Diseases, Ninth Revision (ICD-9 ) diagnostic code for chronic kidney disease (ICD-9-CM codes 585.1-585.9).

the choice of using either a thiazide diuretic or -blocker alone or in combination as “conventional therapy.” The combination compared favorably against other antihy-pertensive drugs classes for prevention of CV events.1,24

The use of -blockers as a first-line therapy has recently been challenged based on evidence of a weak effect on stroke25 and the absence of an effect on coronary heart disease25-27 compared with placebo, as well as inferiority

Table 2. Incident Cardiovascular (CV) Events Associated With Metoprolol Tartrate Compared With Atenolol

Multivariablea Cox Proportional Hazards Regression Propensity Score–Matchedb Cox Proportional Hazards ModelVariable No. of Events Person-years Hazard Ratio (95% CI) No. of Events Person-years Hazard Ratio (95% CI)MI 3517 631 403 0.99 (0.97-1.01) 712 94 261 0.99 (0.97-1.02)HF 3272 633 987 0.99 (0.97-1.01) 831 94 257 0.99 (0.96-1.01)Stroke 3664 632 386 0.99 (0.97-1.01) 773 94 346 0.99 (0.97-1.02)Any CV event 9353 616 028 0.98 (0.99-1.00) 2064 91 191 0.98 (0.95-1.00)

Abbreviations: HF, heart failure; MI, myocardial infarction.a Multivariable model adjusted for year of -blocker therapy initiation, age, sex, number of visits in prior year, systolic blood pressure at start of -blocker therapy,

lipid disorder, diabetes mellitus, chronic kidney disease, and if using other antihypertensive medications. b Matched on propensity score. Propensity score adjusted for year of -blocker therapy initiation, age, sex, number of visits in prior year, systolic

blood pressure at start of -blocker therapy, lipid disorder, diabetes, chronic kidney disease mellitus, and if using other antihypertensive medications.

Table 3. Comparison of Blood Pressure (BP)-Lowering Effects at 6 Months in New -Blocker Users

Multivariablea Linear Regression Propensity Score–Matchedb Linear RegressionBP Estimate (SE), mm Hg BP Estimate (SE), mm Hg

Atenolol Metoprolol Tartrate Atenolol Metoprolol TartrateVariable (n = 61 869) (n = 7013) P Value (n = 6907) (n = 7010) P ValueBaseline SBP and DBP

SBP 148.5 (0.25) 145.4 (0.33) .001 144.2 (0.25) 143.3 (0.25) .007DBP 84.2 (0.15) 82.5 (0.20) .001 81.3 (0.15) 80.2 (0.15) .001

SBP and DBP at 6 mo after-blocker therapy initiation

SBP 137.4 (0.23) 137.5 (0.30) .82 136.5 (0.23) 136.6 (0.23) .85DBP 77.3 (0.13) 77.7 (0.17) .005 76.6 (0.13) 76.7 (0.13) .41

Change in SBP and DBP overthe 6 mo follow-up

SBP 9.8 (0.23) 9.8 (0.30) .82 7.7 (0.29) 6.7 (0.29) .02DBP 5.9 (0.13) 5.5 (0.17) .005 4.7 (0.16) 3.4 (0.16) .001

Abbreviations: DBP, diastolic BP; SBP, systolic BP.a Multivariable model adjusted for year of -blocker therapy initiation, age, sex, number of visits in prior year, SBP at start of -blocker therapy, lipid disorder,

diabetes mellitus, chronic kidney disease, and if using other antihypertensive medications. b Matched on propensity score. Propensity score adjusted for year of -blocker therapy initiation, age, sex, number of visits in prior year, SBP at -

blocker therapy initiation, lipid disorder, diabetes mellitus, chronic kidney disease, and if using other antihypertensive medications.

compared with other treatments for total mortality, coro-nary heart disease, and stroke.6,7,28 Meta-analyses and a Cochrane review of recent trials that looked specifically at -blockers used as monotherapy or as the first-line drug in a stepped care approach concluded that the evidence did not support use of -blockers as a first-line therapy.11 Based on these findings, recently issued guidelines have relegated -blockers to third- or fourth-line treatment for uncomplicated hypertension.29

-blockers differ in selectivity for the 1- and 2- and -adrenergic receptors, lipophilicity, penetration across the blood-brain barrier, duration of action, vasodilation properties, and type 3 antiarrhythmic activity.14,15,30 Dif-ferent types of -blockers may be indicated depending on patient profiles and tolerances. Given that most of the evidence comes from trials where atenolol was the -blocker used,11 it is unclear if the observed effects of -blockers in comparison with other antihypertensive medications are due to properties of atenolol or the en-tire class of -blockers. However, there have been no trials comparing the different subtypes of -blockers. While both atenolol and metoprolol tartrate are both 1-adrenergic receptors, they differ in lipophilicity, bioavail-ability, and metabolism.10,11,31 Metoprolol is lipid soluble

and tends to have highly variable bioavailability and a short plasma half-life. In contrast, atenolol is more water soluble, shows less variance in bioavailability, and has a longer plasma half-life. Despite these differences, both drugs have the effect of increasing vagal tone and causing a reduc-tion in sympathetic outflow, likely via peripheral -ad-renergic blockade.32,33 Our findings that there are no dif-ferences between atenolol and metoprolol tartrate in event rates and effectiveness at BP lowering in a cohort of adults without prior CV events suggest that the unfavorable trial data with atenolol may also apply to other -blockers.

As with any observational study, there are potential limitations and caveats. We were unable to compare aten-olol with any -blocker other than metoprolol tartrate because of the low use of other agents in our study popu-lation during the years of observation. The use of meto-prolol succinate, a once-daily drug that may have better adherence rates compared with twice-daily metoprolol tartrate, has been increasing owing to the availability of generic versions in recent years, but the shift away from -blockers after 2007 may make comparative effective-ness analyses more difficult.

Most importantly, patients were not randomly as-signed to treatment with either atenolol or metoprolol tar-

trate. The decision on the part of the clinician to choose one drug over another may be related to patient charac-teristics associated with BP control or CV risk or physi-cian characteristics associated with differences in quality of care. To reduce the potential bias related to confound-ing by indication, we took 2 approaches: (1) we used a new user design21,34 and restricted the sample to those pa-tients with no evidence of diagnosed or suspected CVD34 and (2) we used propensity score matching to ensure that patients were comparable with regard to baseline covari-ates and the probability of receiving each treatment.

Despite these robust methods, no observational study can rule out the impact of unmeasured confounding. If unmeasured variables associated with poorer prognosis were more common in patients prescribed metoprolol tar-trate, it could mask a beneficial effect of metoprolol. We excluded patients who had seen a cardiologist in the 12 months prior to the initiation of -blocker therapy, but this strategy may have been insufficient to rule out sus-pected CVD. However, in a recent study using data from one of the study sites, we found no evidence of sus-pected heart disease in audits of physician medical chart notes in 240 patients lacking specific ICD-9 codes for heart disease (410-414 and 420-429).35 Other important po-tential unmeasured confounders that are not available in electronic medical records include behavioral or envi-ronmental risk factors, such as poor diet, low level of physical activity, or exposure to second-hand smoke, al-though we have no reason to believe that patients with these risk factors would be more likely to be prescribed metoprolol tartrate rather than atenolol.

In conclusion, we found no differences in CV event rates when comparing patients without a history of CV events who were initiating treatment with either ateno-lol or metoprolol tartrate. These findings suggest that hy-pertension trial outcomes with atenolol may not relate to unfavorable characteristics of this particular drug. These results should be interpreted cautiously, since there have been no trials comparing these 2 -blockers directly.

Accepted for Publication: June 11, 2012.Published Online: August 27, 2012. doi:10.1001 /archinternmed.2012.4276Correspondence: Emily D. Parker, MPH, PhD, Health-Partners Institute for Education and Research, Box 1524, Mail Stop 21111R, Minneapolis, MN 55440-1524 ([email protected]).Author Contributions: Study concept and design: Parker, Margolis, and O’Connor. Acquisition of data: Margolis, Trower, Magid, Tavel, Shetterly, Swain, and O’Connor.Analysis and interpretation of data: Parker, Margolis, Ho, and O’Connor. Drafting of the manuscript: Parker, Mar-golis, Trower, Swain, and O’Connor. Critical revision of the manuscript for important intellectual content: Parker, Magid, Tavel, Shetterly, and Ho. Statistical analysis: Parker and Trower. Obtained funding: Magid and O’Connor. Study supervision: Margolis.Financial Disclosure: None reported.Funding/Support: This project was funded by grant NIH/ NHLBI/U19 HL091179 from the National Heart, Lung, and Blood Institute and subcontract to HealthPartners Institute for Education and Research.

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INVITED COMMENTARY

Observational Comparative Effectiveness Studiesof Drug TherapiesHigh-Quality Answers or Important Clinical Questions?

F or the prevention of cardiovascular disease (CVD), -blockers are among the most widely used therapies. Multiple clinical trials have

established their efficacy in preventing death after myocardial infarction (MI) and in treating congestive heart failure (CHF) due to systolic dysfunction.1,2 -Blockers have also long been used to treat hyperten-sion. Although low-dose diuretics are the recom-mended first-line agent for pharmacologic therapy for uncomplicated high blood pressure,3 several large trials funded by the pharmaceutical industry have used -blockers as the active-comparison control treatment,4

and the results of these trials suggest that other therapies are more effective than atenolol in pre-venting cardiovascular events, particularly stroke.5,6 Because no primary prevention trial among hyperten-sive patients has compared atenolol head to head with other -blockers, their comparative effectiveness in this setting remains unknown.

To address this question, Parker and colleagues7

conducted an observational study that compared the new use of atenolol and metoprolol tartrate, 2 widely used -blockers in the United States, for the prevention of MI, stroke, and CHF in patients with treated hyper-tension. This study was nested within the hypertension registry of the Cardiovascular Research Network (CVRN), which includes all adult patients with hyper-tension enrolled in 3 large integrated health care plans from 2000 to 2009. Most -blocker use was in combi-nation with other therapies, and half of the study popu-lation used diuretics within 6 months prior to starting a -blocker. For all outcomes, the relative risk estimates were null, and the 95% confidence intervals excluded a

greater than 2% increased risk associated with metopro-lol use compared with atenolol.

This study has several strengths. The validation of en-try criteria in the hypertension registry and the use of elec-tronic prescriptions records allowed for a new-user study design, which compares users of different treatments at a similar point in the natural history of hypertensive dis-ease and avoids some sources of bias that are common in studies that include prevalent users of medications.8 Because of the careful use of restriction to exclude per-sons with known prevalent CVD and even persons re-ferred to a cardiologist, who may be more likely than non-referred patients to have undocumented or suspected but undiagnosed CVD, the observed cardiovascular events likely reflect incident disease.

The authors used several analytic methods to mini-mize confounding bias. In one set of analyses, factors as-sociated with both the choice of -blocker and the risk of outcomes were adjusted for. In another, propensity scores were used to make comparisons among a subset of the study population with similar probabilities of treat-ment based on known risk factors. Furthermore, ateno-lol and metoprolol, which are both cardioselective 1-adrenergic receptor blockers, have similar pharmacologic properties and similar indications.9 The relative risk es-timates from the 2 analytic approaches were similar, and because of large sample sizes, the 95% confidence inter-vals were narrow.

The study by Parker and colleagues7 also shares the traditional and persistent weaknesses of observational studies, particularly those that rely on administrative data. Some potential confounding variables are not well cap-tured by administrative codes, and information on oth-

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