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Original Investigation |

Comparative Risk for Angioedema Associated With the Use of Drugs That Target the Renin-Angiotensin-Aldosterone System FREE

Sengwee Toh, ScD; Marsha E. Reichman, PhD; Monika Houstoun, PharmD; Mary Ross Southworth, PharmD; Xiao Ding, PhD; Adrian F. Hernandez, MD; Mark Levenson, PhD; Lingling Li, PhD; Carolyn McCloskey, MD, MPH; Azadeh Shoaibi, MS, MHS; Eileen Wu, PharmD; Gwen Zornberg, MD, MS, ScD; Sean Hennessy, PharmD, PhD
[+] Author Affiliations

Author Affiliations: Department of Population Medicine, Harvard Medical School, and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (Drs Toh and Li); Office of Surveillance and Epidemiology (Drs Reichman, Houstoun, McCloskey, Wu, and Zornberg), Office of New Drugs (Dr Ross Southworth), Office of Translational Sciences (Drs Ding and Levenson), and Office of Medical Policy (Ms Shoaibi), Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland; Division of Cardiology, Duke University School of Medicine, Durham, North Carolina (Dr Hernandez); and Center for Clinical Epidemiology and Biostatistics and Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine, University of Pennsylvania, Philadelphia (Dr Hennessy).


Arch Intern Med. 2012;172(20):1582-1589. doi:10.1001/2013.jamainternmed.34.
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Background Although certain drugs that target the renin-angiotensin-aldosterone system are linked to an increased risk for angioedema, data on their absolute and comparative risks are limited. We assessed the risk for angioedema associated with the use of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and the direct renin inhibitor aliskiren.

Methods We conducted a retrospective, observational, inception cohort study of patients 18 years or older from 17 health plans participating in the Mini-Sentinel program who had initiated the use of an ACEI (n = 1 845 138), an ARB (n = 467 313), aliskiren (n = 4867), or a β-blocker (n = 1 592 278) between January 1, 2001, and December 31, 2010. We calculated the cumulative incidence and incidence rate of angioedema during a maximal 365-day follow-up period. Using β-blockers as a reference and a propensity score approach, we estimated the hazard ratios of angioedema separately for ACEIs, ARBs, and aliskiren, adjusting for age, sex, history of allergic reactions, diabetes mellitus, heart failure, or ischemic heart disease, and the use of prescription nonsteroidal anti-inflammatory drugs.

Results A total of 4511 angioedema events (3301 for ACEIs, 288 for ARBs, 7 for aliskiren, and 915 for β-blockers) were observed during the follow-up period. The cumulative incidences per 1000 persons were 1.79 (95% CI, 1.73-1.85) cases for ACEIs, 0.62 (95% CI, 0.55-0.69) cases for ARBs, 1.44 (95% CI, 0.58-2.96) cases for aliskiren, and 0.58 (95% CI, 0.54-0.61) cases for β-blockers. The incidence rates per 1000 person-years were 4.38 (95% CI, 4.24-4.54) cases for ACEIs, 1.66 (95% CI, 1.47-1.86) cases for ARBs, 4.67 (95% CI, 1.88-9.63) cases for aliskiren, and 1.67 (95% CI, 1.56-1.78) cases for β-blockers. Compared with the use of β-blockers, the adjusted hazard ratios were 3.04 (95% CI, 2.81-3.27) for ACEIs, 1.16 (95% CI, 1.00-1.34) for ARBs, and 2.85 (95% CI, 1.34-6.04) for aliskiren.

Conclusions Compared with β-blockers, ACEIs or aliskiren was associated with an approximately 3-fold higher risk for angioedema, although the number of exposed events for aliskiren was small. The risk for angioedema was lower with ARBs than with ACEIs or aliskiren.

Figures in this Article

Drugs that target the renin-angiotensin-aldosterone system, such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), are widely used in patients with hypertension or ischemic heart disease, especially those with other comorbidities such as congestive heart failure, diabetes mellitus, or chronic kidney disease.1,2 Angioedema, a serious and sometimes life-threatening adverse event that usually manifests as swelling of the lips, tongue, mouth, larynx, pharynx, or periorbital region, has been linked to the use of these medications, particularly ACEIs.36

However, limited information is available about the absolute and relative risks for angioedema associated with the use of these medications. Existing evidence is primarily based on investigations of specific cohorts (eg, predominantly male veterans or Medicaid beneficiaries), whose findings may not be generalizable to other populations, or based on investigations with few events, which provide unstable risk estimates.510

This study was designed to assess the risk for angioedema associated with the use of ACEIs (as a class), ARBs (as a class and as individual agents), and aliskiren (a first-in-class direct renin inhibitor approved by the US Food and Drug Administration [FDA] in 2007). The study was performed among a large, diverse, population-based cohort who received these drugs in real-world clinical settings.

DATA SOURCE

The Mini-Sentinel program is part of the Sentinel Initiative, a multifaceted effort by the FDA to develop a national system for monitoring the safety of medical products as mandated by the 2007 FDA Amendments Act.11,12 This study included 17 health plans (listed in the Additional Contributions section at the end of this article) contributing data to the Mini-Sentinel Distributed Database, which is composed of administrative claims and clinical information formatted into a common data model.13

DESIGN

A study protocol was developed before the analysis and has been previously published.14 We used an inception cohort design15 to identify patients 18 years or older with an outpatient dispensing of an oral formulation of the following medications as a single ingredient or as combination products with nonstudy drugs between January 1, 2001, and December 31, 2010: (1) an ACEI (benazepril, captopril, enalapril, fosinopril, lisinopril, moexipril, quinapril, perindopril, ramipril, or trandolapril), (2) an ARB (candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, or valsartan), (3) aliskiren, or (4) a β-blocker (acebutolol, atenolol, bisoprolol, carvedilol, labetalol, metoprolol, nebivolol, pindolol, propranolol, or timolol), used as a common reference group. We refer to the dispensing date of the first prescription of any of the study drug as the index date. To be eligible for the study, these patients must also have met each of the following criteria during the 183-day period preceding the index date: (1) continuous health plan enrollment with pharmacy and medical benefits, (2) no prescription for any other study drug, and (3) no diagnosis of angioedema in any care setting.

END POINTS

The primary outcome of interest was angioedema, identified by International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code 995.1, recorded in any position during an outpatient, inpatient, or emergency department encounter. The positive predictive value of this algorithm in administrative claims data ranges from 90%7,16 to 95%.8 The secondary outcome of interest was serious angioedema, defined as angioedema with airway obstruction requiring inpatient care. We identified serious angioedema events by an inpatient ICD-9-CM code 995.1, plus a code indicating intensive care unit admission, intubation, tracheostomy, or laryngoscopy occurring within 2 days of the date of hospital admission.8 The codes used to identify these events can be found in the published protocol.14

FOLLOW-UP PERIOD

The follow-up period began on the index date and ended at the earliest occurrence of the following: first angioedema diagnosis, death, disenrollment, 365 follow-up days, December 31, 2010, cessation of use of study drug, or initiation of another study drug of a different class (except for individual ARB analyses, for which censoring also occurred with initiation of a different ARB). Cessation of use occurred when the days' supplies were exhausted for longer than 14 days without a subsequent dispensing. We chose a maximal follow-up period of 365 days because we were interested in the immediate and intermediate risk for angioedema associated with the use of these drugs. Previous studies68,17 have shown that the risk for angioedema is greatest immediately after treatment initiation and gradually diminishes over time but remains higher compared with no use of these drugs.

STATISTICAL ANALYSIS

We compared the baseline characteristics among initiators of ACEIs, ARBs, and aliskiren separately with those among initiators of β-blockers using standardized differences, of which a value exceeding 0.1 is generally considered meaningful.18 For ACEIs, ARBs, individual ARBs, aliskiren, and β-blockers, we calculated the cumulative incidences and incidence rates of angioedema and serious angioedema, as well as their 95% CIs.

We estimated the site-adjusted hazard ratios (HRs) and 95% CIs separately for ACEIs, ARBs, individual ARBs, and aliskiren, with β-blockers as the common reference group, using the case-centered logistic regression approach developed by Fireman et al.19 This approach used site-specific aggregate-level data sets to fit a logistic regression model separately for each drug pair of interest. The aggregate data sets included 1 record per risk set, each anchored by an angioedema event. For example, in the ACEI and β-blocker analysis, each record included (1) a binary variable indicating whether the case was exposed to an ACEI and (2) the log odds of the site-specific proportion of ACEI-exposed patients in the risk set. The case-centered logistic regression model included the binary indicator variable as the dependent variable, the log odds as the independent variable (specified as an offset), and the data partner site as a stratification variable. Such a model maximizes the same likelihood as a stratified Cox proportional hazards regression model fit using individual-level data, and both yield the same parameter estimates.19 The major difference is that the case-centered approach does not require individual-level data to leave the data partners' firewalls, maintaining patient privacy and data security.20

We combined the case-centered approach with propensity scores (PS)21,22 to adjust for the following covariates ascertained during the 183-day period preceding the index date8,17,23,24: age (18-44, 45-54, 55-64, and ≥65 years), sex, and history of allergic reactions, diabetes mellitus, heart failure, or ischemic heart disease, as well as the use of prescription nonsteroidal anti-inflammatory drugs. Propensity scores (the probabilities of initiating a β-blocker) were estimated by a logistic regression model fit separately at each site for each drug pair that included these covariates as independent variables. To obtain PS-adjusted HRs, we fit a case-centered logistic regression model separately for each drug pair identical to the one aforementioned, except that the log odds were calculated only among at-risk individuals in the same PS quintile as the case. The adjusted analyses of individual ARBs used PS estimated from the entire drug class because they were more stable. Race/ethnicity has been shown to be an important determinant of the ACEI-angioedema relationship,7,8,17,2527 but this information was unknown or missing in approximately 70% of our population and was not adjusted for.

ADDITIONAL ANALYSES

For comparison, we performed a meta-analysis to pool the site-specific adjusted HRs obtained from a multivariable Cox proportional hazards regression model that adjusted for the same covariates in the PS model. The pooled HR was the weighted average of the site-specific HRs using the inverse of the site-specific variance as the weight.2830

We also performed an analysis that (1) used a 365-day look-back period to define new use and to exclude prior angioedema, (2) was restricted to angioedema events identified from inpatient or emergency department encounters, and (3) was limited to the cohort identified after the FDA approval date of aliskiren (March 5, 2007). Whenever possible, we stratified the analyses by age, sex, and follow-up period.

All the analyses were performed using commercially available software (SAS; SAS Institute, Inc), were developed and tested centrally by the Mini-Sentinel Operations Center, and were executed concurrently by all 17 data partners. None of the analyses required data partners to transfer individual-level data. The Mini-Sentinel program is a public health activity under the auspices of the FDA and is not under the purview of institutional review boards.31,32

A total of 1 845 138 ACEI initiators, 467 313 ARB initiators, 4867 aliskiren initiators, and 1 592 278 initiators of β-blockers were eligible for the study (Figure). Initiators of ACEIs, ARBs, or aliskiren were more likely than β-blocker initiators to be male and to have a previous diagnosis of diabetes mellitus but were less likely to have a prior diagnosis of ischemic heart disease (Table 1).

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Flowchart to create the study cohort, 2001-2010. ACEIs indicates angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers.

Table Graphic Jump LocationTable 1. Baseline Characteristics of Patients by Drug Class Use, 2001-2010

The mean follow-up durations were 149 days for ACEI initiators, 136 days for ARB initiators, 112 days for aliskiren initiators, and 126 days for β-blocker initiators. eTable 1 gives the numbers of patients censored for various reasons. During the follow-up period, we observed 3301 angioedema events associated with the use of ACEIs, 288 events with ARBs, 7 events with aliskiren, and 915 events with β-blockers (Table 2). The risk for angioedema (as measured by the cumulative incidence and incidence rate) was highest for ACEIs and was similar between ARBs and β-blockers (Table 2). The risk associated with the use of aliskiren seemed to be similar to that of the ACEIs but was based on only 7 exposed cases. There was moderate variation in risk across individual ARBs, with losartan having a greater risk than other ARBs. However, information was sparse for several ARBs, especially candesartan and eprosartan. The risk for serious angioedema was low across all drug classes but was also higher for ACEIs. Limited information was available on the risk for serious angioedema associated with the use of individual ARBs and aliskiren.

Table Graphic Jump LocationTable 2. Angioedema and Serious Angioedema Events by Study Drug Use During a Maximal Follow-up Period of 365 Days, 2001-2010

The HRs from the site-adjusted and PS-adjusted (which also adjusted for site) analyses were similar (Table 2). Compared with the use of β-blockers, the angioedema risk was approximately 3-fold higher for ACEIs and aliskiren and was 16% higher for ARBs. Within ARBs, the PS-adjusted HR was highest for losartan. For serious angioedema, the risk with ACEIs was 5 times the risk with β-blockers. There was no indication that ARB use increased the risk for serious angioedema compared with β-blocker use. Because there was only one case of serious angioedema among aliskiren initiators, the ability to assess this association was limited.

Results from the case-centered approach and meta-analysis were comparable (eTable 2), although the effect estimates varied moderately when the sample size was smaller. Using a 365-day look-back period, the numbers of eligible initiators and angioedema events were smaller, but the HRs were similar (eTable 3). Fifty-four percent (1782 of 3301) of angioedema events among ACEI initiators were identified from inpatient or emergency department encounters; these proportions were 29% (83 of 288) for ARBs, 29% (2 of 7) for aliskiren, and 31% (282 of 915) for β-blockers. Results were qualitatively similar when restricting the analysis to these angioedema events (eTable 4) or when restricting to the cohort identified after the FDA approval date of aliskiren (eTable 5).

The PS-adjusted HR for ACEIs was higher in patients 65 years or older than in patients of other age groups (P = .047, Wald test of homogeneity) and was higher in women than in men (P = .002), but the differences in magnitude were moderate (Table 3). Neither age nor sex seemed to modify the ARB-angioedema association. The PS-adjusted HR for ACEIs was greatest during the first 30 days of use (Table 4); the magnitude diminished but remained significantly higher compared with β-blockers during the remainder of the follow-up period. Sixty-six percent of all the angioedema events among ACEI initiators observed during the follow-up period occurred during the first 90 days compared with 65% for ARBs and 66% for β-blockers. Subgroup analyses were not performed for individual ARBs, aliskiren, or serious angioedema because of the few cases.

Table Graphic Jump LocationTable 3. Drug Class Use Results by Age and Sex Group During a Maximal Follow-up Period of 365 Days, 2001-2010
Table Graphic Jump LocationTable 4. Drug Class Use Results by Follow-up Period, 2001-2010

In this study, the risk for angioedema associated with the use of ACEIs or aliskiren was 3 times the risk with β-blockers, a drug class not thought to be linked to angioedema. However, results for aliskiren were based on only 7 exposed cases. The risk seemed to be 16% greater for ARBs compared with that for β-blockers, with a lower 95% CI bound of 1.00. Among individual ARBs, losartan appeared to be associated with the greatest risk, but information on several individual ARBs was limited. To our knowledge, this study is the largest of its kind and the first to examine the aliskiren-angioedema association using routinely collected clinical data.

Table 5 lists selected studies that examined associations between the use of the study drugs and angioedema. Miller et al8 found in US veterans that the angioedema incidence rates per 1000 person-years were 2 cases among ACEI initiators (n = 195 192) and 0.5 cases among β-blocker initiators (n = 94 020). Both of these incidence rates in our study were higher, but the cumulative incidence for ACEIs was similar (1.8 cases per 1000 ACEI-exposed persons; the cumulative incidence for β-blockers was unavailable in the study by Miller et al).

Table Graphic Jump LocationTable 5. Selected Published Studies on Associations Between the Use of Drugs That Target the Renin-Angiotensin-Aldosterone System and the Risk for Angioedema

Differences in the study population might have led to our higher incidence rates. For example, the proportion of women, whose study drug-associated angioedema risk was greater, exceeded 50% in our study compared with 3% in the study by Miller et al.8 However, it is unlikely that such differences would only influence the incidence rate and not the cumulative incidence. A more plausible explanation might be the difference in how the follow-up periods were constructed. Follow-up periods ceased completely in our study when patients stopped their treatment for at least 14 days. Miller et al seemed to have estimated the incidence rate using all exposed person-times (including person-times that accrued after resumption) during their maximal 21-month follow-up period, or they allowed a more generous gap between dispensings. This could explain why the mean follow-up period was 0.4 years in our study and 0.9 years in the study by Miller et al. Because the risk for angioedema gradually diminished over time,6,7,17 the study by Miller and colleagues8 might have included more follow-up times with a lower risk. Despite these potential differences, our PS-adjusted HR of 3.04 (95% CI, 2.81-3.27) for ACEIs was similar to the relative risk of 3.56 (95% CI, 2.82-4.44) obtained by Miller et al from a Poisson regression analysis using all other antihypertensive medications as a reference.

Angioedema is mediated by vasoactive mediators, such as bradykinins. It is generally believed that ACEIs precipitate angioedema by directly interfering with the degradation of bradykinin, potentiating its biological effect.5,6 Although some ACEI-induced cases may manifest only after a prolonged duration of therapy, sometimes exceeding 1 year since treatment initiation,7,8 the period immediately after treatment initiation is of greatest interest. Consistent with previous studies,68,17 we observed that the risk was greatest immediately following ACEI initiation. Miller et al8 found that 55% of angioedema events occurred within 90 days following ACEI initiation, while the percentage was 66% in our study.

Compared with what is known about ACEIs, the relationship between ARB use and angioedema is not as well understood. We found that the risk may be slightly elevated for the use of ARBs. In the study by Miller et al,8 the incidence rate of angioedema was 1 case per 1000 person-years among 9816 ARB initiators, or 2 times the rate among β-blocker initiators, but information on the adjusted HR was unavailable. Our results also suggest that the risk might vary across individual ARBs; these findings need to be examined further.

The association between aliskiren use and angioedema is not well quantified. In the premarket development program, there were reports of aliskiren-associated angioedema; therefore, its label contains a warning about this risk and is similar to ACEI class labeling. Postmarket reports of serious angioedema events associated with the use of aliskiren in which patients required intubation were also received. Results of pooled analyses among randomized trials comprising 4578 patients who received aliskiren monotherapy suggest that the risk for angioedema and urticaria as a combined outcome was similar or lower for aliskiren compared with that for ACEIs or ARBs.35,36 Unfortunately, the analyses did not examine angioedema separately, and individual trials were too small to provide reliable estimates. We observed that the risk for angioedema associated with the use of aliskiren is similar to that of ACEI use; further investigations are needed to better characterize the association.

Our results should be interpreted in the context of several limitations. African American race may be a risk factor for angioedema and a potential effect modifier for the effect of ACEI use on angioedema.7,8,17,2527 Race/ethnicity information was missing in approximately 70% of our cohort and was not adjusted for in our analysis. An analysis that included only those with nonmissing race/ethnicity may introduce bias if missingness depends on the risk for angioedema and treatment choice.37,38 If African Americans are less likely to receive ACEIs owing to this suspected risk, our HR would underestimate the actual relative risk (eTable 6).39,40 The estimated absolute risks might also not be directly generalizable to populations with a different race/ethnicity distribution than ours. Smoking was another variable unavailable to us that has also been suggested to be a confounder for the effect of ACEI use on angioedema.23,26,27

Some angioedema cases (especially those that were milder, resolved quickly, and did not require medical attention) might not have been captured in our databases. This might lead to an underestimation of the true risk for angioedema and might partly explain why randomized trials generally observed higher cumulative incidences associated with the use of ACEIs and ARBs than observational studies (Table 5). Because the ACEI-angioedema association is well recognized, underestimation of risk may be less severe for these drugs as patients and physicians may be more attentive to any clinical manifestation of angioedema. But, this could potentially lead to biased HRs when comparing ACEIs with β-blockers because there would be a differential case identification. However, the proportion of angioedema events diagnosed during an outpatient visit (rather than an inpatient or emergency department setting) was much lower in ACEI initiators compared with initiators of other study drugs, suggesting that milder cases were no more likely to be captured among ACEI initiators or that ACEI use might be associated with more severe cases.

Although our use of an as-treated approach captured angioedema events while patients were receiving treatment, censoring patients when they stopped treatment might introduce bias if treatment cessation depended on the risk for angioedema and varied by study drug.41,42 We attempted to account for this potential bias by extending the follow-up period for up to 14 days to capture events that might be diagnosed after treatment discontinuation.

The validity of our findings is strengthened by the consistent results from various analyses and by the high positive predictive value of the diagnosis code of angioedema. The large sample size and the demographic and geographic diversity of our population increase the generalizability of our findings.

In conclusion, this study characterized the relationships between the use of drugs targeting the renin-angiotensin-aldosterone system and the incidence of angioedema in a large, diverse cohort. The risk for angioedema associated with ACEI or aliskiren use was approximately 3 times the risk with β-blocker use, although results for aliskiren were based on only 7 exposed cases. The angioedema risk was lower with ARBs than with ACEIs or aliskiren.

Correspondence: Sengwee Toh, ScD, Department of Population Medicine, Harvard Medical School, and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, Sixth Floor, Boston, MA 02215 (darrentoh@post.harvard.edu).

Accepted for Publication: June 1, 2012.

Published Online: October 15, 2012. doi:10.1001/2013.jamainternmed.34

Author Contributions: Dr Toh had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Toh, Reichman, Houstoun, Ross Southworth, Ding, Levenson, McCloskey, Shoaibi, Zornberg, and Hennessy. Acquisition of data: Toh. Analysis and interpretation of data: Toh, Reichman, Houstoun, Ross Southworth, Ding, Hernandez, Li, Shoaibi, Wu, Zornberg, and Hennessy. Drafting of the manuscript: Toh. Critical revision of the manuscript for important intellectual content: Toh, Reichman, Houstoun, Ross Southworth, Ding, Hernandez, Levenson, Li, McCloskey, Shoaibi, Wu, Zornberg, and Hennessy. Statistical analysis: Toh, Reichman, Ding, Levenson, Li, Shoaibi, and Zornberg. Obtained funding: Toh and Reichman. Administrative, technical, and material support: Toh, Reichman, Houstoun, Ding, Wu, and Zornberg. Study supervision: Toh, Reichman, Houstoun, and Levenson.

Conflict of Interest Disclosures: Dr Hernandez has received research support or consulting fees from Amylin, AstraZeneca, Corthera, Johnson & Johnson, and sanofi-aventis. Dr Hennessy has received research support from AstraZeneca and Bristol-Myers Squibb and has consulted for Abbott and Novartis, unrelated to the products examined in this study.

Funding/Support: The Mini-Sentinel program is funded by the Food and Drug Administration through contract HHSF223200910006I from the Department of Health and Human Services.

Role of the Sponsor: The coauthors employed by the Food and Drug Administration participated in the design and conduct of the study; interpretation of the data; and preparation, review, and approval of the manuscript. They were not involved in the collection or management of the data.

Additional Contributions: Aarthi Iyer, MPH, and Jillian Lauer, BS, provided administrative support; Nicolas Beaulieu, MA, James Marshall, MS, Lisa Trebino, MS, Erick Moyneur, MA, and Eric Gravel, MA, contributed programming assistance; and Bruce Fireman, MA, guided application of the case-centered logistic regression approach. The following data partners participated in the study: Aetna, HealthCore Inc, the HMO Research Network (Group Health Research Institute, Harvard Pilgrim Health Care Institute, HealthPartners Research Foundation, Henry Ford Health System, Lovelace Clinic Foundation, Marshfield Clinic Research Foundation, and Meyers Primary Care Institute), Humana, the Kaiser Permanente Center for Effectiveness and Safety Research (Kaiser Permanente Colorado, Kaiser Permanente Georgia, Kaiser Permanente Hawaii, Kaiser Permanente Mid-Atlantic, Kaiser Permanente Northern California, and Kaiser Permanente Northwest), and Vanderbilt University/Tennessee Medicaid.

This article was corrected for errors on November 26, 2012.

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Fireman B, Lee J, Lewis N, Bembom O, van der Laan M, Baxter R. Influenza vaccination and mortality: differentiating vaccine effects from bias.  Am J Epidemiol. 2009;170(5):650-656
PubMed   |  Link to Article
Fireman B, Toh S, Butler MG,  et al.  A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  282-290
PubMed   |  Link to Article
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70:41-55
Link to Article
Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score.  J Am Stat Assoc. 1984;79(387):516-524
Link to Article
Piller LB, Ford CE, Davis BR,  et al; ALLHAT Collaborative Research Group.  Incidence and predictors of angioedema in elderly hypertensive patients at high risk for cardiovascular disease: a report from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).  J Clin Hypertens (Greenwich). 2006;8(9):649-658
Link to Article
Greaves M, Lawlor F. Angioedema: manifestations and management.  J Am Acad Dermatol. 1991;25(1, pt 2):155-165
Link to Article
Gibbs CR, Lip GY, Beevers DG. Angioedema due to ACE inhibitors: increased risk in patients of African origin.  Br J Clin Pharmacol. 1999;48(6):861-865
PubMed   |  Link to Article
Morimoto T, Gandhi TK, Fiskio JM,  et al.  An evaluation of risk factors for adverse drug events associated with angiotensin-converting enzyme inhibitors.  J Eval Clin Pract. 2004;10(4):499-509
PubMed   |  Link to Article
Byrd JB, Adam A, Brown NJ. Angiotensin-converting enzyme inhibitor–associated angioedema.  Immunol Allergy Clin North Am. 2006;26(4):725-737
PubMed   |  Link to Article
DerSimonian R, Laird N. Meta-analysis in clinical trials.  Control Clin Trials. 1986;7(3):177-188
PubMed   |  Link to Article
Deeks JJ, Higgins JPT, Altman DG. Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.0.1. Oxford, England: Cochrane Collaboration; 2008:chap 9
Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis.  Trials. 2007;8:16http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920534/?tool=pubmed. Accessed July 31, 2012
PubMed   |  Link to Article
Forrow S, Campion DM, Herrinton LJ,  et al.  The organizational structure and governing principles of the Food and Drug Administration's Mini-Sentinel pilot program.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  12-17
PubMed   |  Link to Article
McGraw D, Rosati K, Evans B. A policy framework for public health uses of electronic health data.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  18-22
PubMed   |  Link to Article
Pfeffer MA, McMurray JJ, Velazquez EJ,  et al; Valsartan in Acute Myocardial Infarction Trial Investigators.  Valsartan, captopril, or both in myocardial infarction complicated by heart failure, left ventricular dysfunction, or both.  N Engl J Med. 2003;349(20):1893-1906
PubMed   |  Link to Article
Yusuf S, Teo KK, Pogue J,  et al; ONTARGET Investigators.  Telmisartan, ramipril, or both in patients at high risk for vascular events.  N Engl J Med. 2008;358(15):1547-1559
PubMed   |  Link to Article
White WB, Bresalier R, Kaplan AP,  et al.  Safety and tolerability of the direct renin inhibitor aliskiren in combination with angiotensin receptor blockers and thiazide diuretics: a pooled analysis of clinical experience of 12,942 patients.  J Clin Hypertens (Greenwich). 2011;13(7):506-516
PubMed   |  Link to Article
White WB, Bresalier R, Kaplan AP,  et al.  Safety and tolerability of the direct renin inhibitor aliskiren: a pooled analysis of clinical experience in more than 12,000 patients with hypertension.  J Clin Hypertens (Greenwich). 2010;12(10):765-775
PubMed   |  Link to Article
Toh S, García Rodríguez LA, Hernán MA. Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 2)  13-20
PubMed   |  Link to Article
Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analyses.  Am J Epidemiol. 1995;142(12):1255-1264
PubMed
Rothman KJ, ed, Greenland S, ed, Lash TL, edModern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008
Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics.  Pharmacoepidemiol Drug Saf. 2006;15(5):291-303
PubMed   |  Link to Article
Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias.  Epidemiology. 2004;15(5):615-625
PubMed   |  Link to Article
Toh S, Hernán MA. Causal inference from longitudinal studies with baseline randomization.  Int J Biostat. 2008;4(1):Article 22

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure. Flowchart to create the study cohort, 2001-2010. ACEIs indicates angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of Patients by Drug Class Use, 2001-2010
Table Graphic Jump LocationTable 2. Angioedema and Serious Angioedema Events by Study Drug Use During a Maximal Follow-up Period of 365 Days, 2001-2010
Table Graphic Jump LocationTable 3. Drug Class Use Results by Age and Sex Group During a Maximal Follow-up Period of 365 Days, 2001-2010
Table Graphic Jump LocationTable 4. Drug Class Use Results by Follow-up Period, 2001-2010
Table Graphic Jump LocationTable 5. Selected Published Studies on Associations Between the Use of Drugs That Target the Renin-Angiotensin-Aldosterone System and the Risk for Angioedema

References

Chobanian AV, Bakris GL, Black HR,  et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National Heart, Lung, and Blood Institute; National High Blood Pressure Education Program Coordinating Committee.  Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.  Hypertension. 2003;42(6):1206-1252
PubMed   |  Link to Article
Zaman MA, Oparil S, Calhoun DA. Drugs targeting the renin-angiotensin-aldosterone system.  Nat Rev Drug Discov. 2002;1(8):621-636
PubMed   |  Link to Article
Slater EE, Merrill DD, Guess HA,  et al.  Clinical profile of angioedema associated with angiotensin converting-enzyme inhibition.  JAMA. 1988;260(7):967-970
PubMed   |  Link to Article
Sabroe RA, Black AK. Angiotensin-converting enzyme (ACE) inhibitors and angio-oedema.  Br J Dermatol. 1997;136(2):153-158
PubMed   |  Link to Article
Israili ZH, Hall WD. Cough and angioneurotic edema associated with angiotensin-converting enzyme inhibitor therapy: a review of the literature and pathophysiology.  Ann Intern Med. 1992;117(3):234-242
PubMed
Vleeming W, van Amsterdam JG, Stricker BH, de Wildt DJ. ACE inhibitor–induced angioedema: incidence, prevention and management.  Drug Saf. 1998;18(3):171-188
PubMed   |  Link to Article
Brown NJ, Ray WA, Snowden M, Griffin MR. Black Americans have an increased rate of angiotensin converting enzyme inhibitor–associated angioedema.  Clin Pharmacol Ther. 1996;60(1):8-13
PubMed   |  Link to Article
Miller DR, Oliveria SA, Berlowitz DR, Fincke BG, Stang P, Lillienfeld DE. Angioedema incidence in US veterans initiating angiotensin-converting enzyme inhibitors.  Hypertension. 2008;51(6):1624-1630
PubMed   |  Link to Article
Matchar DB, McCrory DC, Orlando LA,  et al.  Systematic review: comparative effectiveness of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers for treating essential hypertension.  Ann Intern Med. 2008;148(1):16-29
PubMed
Powers BJ, Coeytaux RR, Dolor RJ,  et al.  Updated report on comparative effectiveness of ACE inhibitors, ARBs, and direct renin inhibitors for patients with essential hypertension: much more data, little new information.  J Gen Intern Med. 2012;27(6):716-729
Link to Article
Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the Sentinel System: a national resource for evidence development.  N Engl J Med. 2011;364(6):498-499
PubMed   |  Link to Article
Platt R, Carnahan RM, Brown JS,  et al.  The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  1-8
PubMed
Curtis LH, Weiner MG, Boudreau DM,  et al.  Design considerations, architecture, and use of the Mini-Sentinel distributed data system.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  23-31
PubMed   |  Link to Article
Toh D, Reichman ME, Houstoun M,  et al.  Protocol for signal refinement of angioedema events in association with use of drugs that act on the renin-angiotensin-aldosterone system. http://www.mini-sentinel.org/work_products/Assessments/Mini-Sentinel_Angioedema-and-RAAS_Protocol.pdf. Accessed January 17, 2012
Ray WA. Evaluating medication effects outside of clinical trials: new-user designs.  Am J Epidemiol. 2003;158(9):915-920
PubMed   |  Link to Article
Brown NJ, Snowden M, Griffin MR. Recurrent angiotensin-converting enzyme inhibitor–associated angioedema.  JAMA. 1997;278(3):232-233
PubMed   |  Link to Article
Kostis JB, Kim HJ, Rusnak J,  et al.  Incidence and characteristics of angioedema associated with enalapril.  Arch Intern Med. 2005;165(14):1637-1642
PubMed   |  Link to Article
Mamdani M, Sykora K, Li P,  et al.  Reader's guide to critical appraisal of cohort studies, 2: assessing potential for confounding.  BMJ. 2005;330(7497):960-962
PubMed   |  Link to Article
Fireman B, Lee J, Lewis N, Bembom O, van der Laan M, Baxter R. Influenza vaccination and mortality: differentiating vaccine effects from bias.  Am J Epidemiol. 2009;170(5):650-656
PubMed   |  Link to Article
Fireman B, Toh S, Butler MG,  et al.  A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  282-290
PubMed   |  Link to Article
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70:41-55
Link to Article
Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score.  J Am Stat Assoc. 1984;79(387):516-524
Link to Article
Piller LB, Ford CE, Davis BR,  et al; ALLHAT Collaborative Research Group.  Incidence and predictors of angioedema in elderly hypertensive patients at high risk for cardiovascular disease: a report from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).  J Clin Hypertens (Greenwich). 2006;8(9):649-658
Link to Article
Greaves M, Lawlor F. Angioedema: manifestations and management.  J Am Acad Dermatol. 1991;25(1, pt 2):155-165
Link to Article
Gibbs CR, Lip GY, Beevers DG. Angioedema due to ACE inhibitors: increased risk in patients of African origin.  Br J Clin Pharmacol. 1999;48(6):861-865
PubMed   |  Link to Article
Morimoto T, Gandhi TK, Fiskio JM,  et al.  An evaluation of risk factors for adverse drug events associated with angiotensin-converting enzyme inhibitors.  J Eval Clin Pract. 2004;10(4):499-509
PubMed   |  Link to Article
Byrd JB, Adam A, Brown NJ. Angiotensin-converting enzyme inhibitor–associated angioedema.  Immunol Allergy Clin North Am. 2006;26(4):725-737
PubMed   |  Link to Article
DerSimonian R, Laird N. Meta-analysis in clinical trials.  Control Clin Trials. 1986;7(3):177-188
PubMed   |  Link to Article
Deeks JJ, Higgins JPT, Altman DG. Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 5.0.1. Oxford, England: Cochrane Collaboration; 2008:chap 9
Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis.  Trials. 2007;8:16http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920534/?tool=pubmed. Accessed July 31, 2012
PubMed   |  Link to Article
Forrow S, Campion DM, Herrinton LJ,  et al.  The organizational structure and governing principles of the Food and Drug Administration's Mini-Sentinel pilot program.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  12-17
PubMed   |  Link to Article
McGraw D, Rosati K, Evans B. A policy framework for public health uses of electronic health data.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 1)  18-22
PubMed   |  Link to Article
Pfeffer MA, McMurray JJ, Velazquez EJ,  et al; Valsartan in Acute Myocardial Infarction Trial Investigators.  Valsartan, captopril, or both in myocardial infarction complicated by heart failure, left ventricular dysfunction, or both.  N Engl J Med. 2003;349(20):1893-1906
PubMed   |  Link to Article
Yusuf S, Teo KK, Pogue J,  et al; ONTARGET Investigators.  Telmisartan, ramipril, or both in patients at high risk for vascular events.  N Engl J Med. 2008;358(15):1547-1559
PubMed   |  Link to Article
White WB, Bresalier R, Kaplan AP,  et al.  Safety and tolerability of the direct renin inhibitor aliskiren in combination with angiotensin receptor blockers and thiazide diuretics: a pooled analysis of clinical experience of 12,942 patients.  J Clin Hypertens (Greenwich). 2011;13(7):506-516
PubMed   |  Link to Article
White WB, Bresalier R, Kaplan AP,  et al.  Safety and tolerability of the direct renin inhibitor aliskiren: a pooled analysis of clinical experience in more than 12,000 patients with hypertension.  J Clin Hypertens (Greenwich). 2010;12(10):765-775
PubMed   |  Link to Article
Toh S, García Rodríguez LA, Hernán MA. Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics.  Pharmacoepidemiol Drug Saf. 2012;21:(suppl 2)  13-20
PubMed   |  Link to Article
Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analyses.  Am J Epidemiol. 1995;142(12):1255-1264
PubMed
Rothman KJ, ed, Greenland S, ed, Lash TL, edModern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008
Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics.  Pharmacoepidemiol Drug Saf. 2006;15(5):291-303
PubMed   |  Link to Article
Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias.  Epidemiology. 2004;15(5):615-625
PubMed   |  Link to Article
Toh S, Hernán MA. Causal inference from longitudinal studies with baseline randomization.  Int J Biostat. 2008;4(1):Article 22

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