Comparative effectiveness of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on major adverse cardiac events in patients with newly diagnosed type 2 diabetes: A nationwide study

International Journal of Cardiology 199 (2015) 283–289

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Comparative effectiveness of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on major adverse cardiac events in patients with newly diagnosed type 2 diabetes: A nationwide study☆ Chia-Jen Shih a,b,1, Hsi Chu a,c,d,1, Shuo-Ming Ou a,e,⁎, Yung-Tai Chen a,f,⁎⁎ a

School of Medicine, National Yang-Ming University, Taipei, Taiwan Department of Medicine, Taipei Veterans General Hospital, Yuanshan Branch, Yilan, Taiwan c Division of Respiratory Medicine, Department of Chest, Taipei City Hospital, Heping Fuyou Branch, Taipei, Taiwan d School of Jen-Teh Junior College of Medicine, Nursing and Management, Taiwan e Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan f Division of Nephrology, Department of Medicine, Taipei City Hospital, Heping Fuyou Branch, Taipei, Taiwan b

a r t i c l e

i n f o

Article history: Received 19 May 2015 Accepted 16 July 2015 Available online 22 July 2015 Keywords: Angiotensin-converting enzyme inhibitors Angiotensin II receptor blockers Diabetes Major adverse cardiac events Epidemiology

a b s t r a c t Background: Guidelines for hypertension management recommend either angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) as first-line therapies for diabetes population. No headto-head trial has been conducted to determine the priority of ACEI/ARB use for major adverse cardiac events (MACEs) in diabetes mellitus. Methods: Data on patients with newly diagnosed diabetes treated with ACEIs or ARBs were collected from Taiwan's National Health Insurance Research Database for the period 2000–2010. A total of 30,777 ARB users and 21,436 ACEI users were identified. One ARB user was matched to one ACEI user by propensity score. Intention-to-treat (ITT) and as-treated (AT) models were used. The primary outcomes were myocardial infarction, ischemic stroke, and all-cause mortality. The secondary outcomes were hospitalization for acute kidney injury and hyperkalemia. Findings: Compared with ACEI users (n = 21,436), ARB users (n = 30,777) showed no significant difference in the outcomes of myocardial infarction (hazard ratio [HR]: 0.92; 95% confidence interval [CI]: 0.80 to 1.07), ischemic stroke (HR: 0.95; 95% CI: 0.87 to 1.04), or all-cause mortality (HR: 0.95; 95% CI: 0.89 to 1.01) in the ITT analysis. The risks of hospitalization for acute kidney injury and hyperkalemia also did not differ between groups. ACEI and ARB use also had similar effects on MACEs and adverse effects in the AT analysis. Conclusions: This large cohort study supports the comparative effectiveness of ACEIs and ARBs in terms of MACE outcomes in patients with incident diabetes. © 2015 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Diabetes affected an estimated one in 10 US adults in 2012, and its prevalence is projected to double or triple by 2050 [1]. Its estimated global burden was 2.8% (171 million people) in 2000 and is expected to increase to 4.4% (366 million people) by 2030 [2]. Diabetes is a prevalent condition that is associated with adverse cardiovascular events. ☆ The Corresponding Authors have the right to grant on behalf of all authors and do grant on behalf of all authors a worldwide license to the Publishers and its licensees in perpetuity. ⁎ Correspondence to: S.-M. Ou, Department of Nephrology, Taipei Veterans General Hospital, Taipei 11217, Taiwan. ⁎⁎ Correspondence to: Y.-T. Chen, Department of Nephrology, Taipei City Hospital Heping Fuyou Branch, Taipei 112, Taiwan. E-mail addresses: [email protected] (S.-M. Ou), [email protected] (Y.-T. Chen). 1 These authors contributed equally to the paper.

http://dx.doi.org/10.1016/j.ijcard.2015.07.053 0167-5273/© 2015 Elsevier Ireland Ltd. All rights reserved.

Recent guidelines encourage use of renin–angiotensin–aldosterone system (RAAS) blockers for patients with diabetes and hypertension — this category includes angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs) [3–5]. The cardioprotective effects of ACEIs in patients with diabetes with and without pre-existing atherosclerosis have been well documented [6]. Cumulative data also support that ACEI has beneficial effects in terms of renal outcomes and major adverse cardiac events (MACEs) compared to placebo and nonRAAS active drugs, independent of blood pressure reduction [7–9]. However, the relative appropriateness of ACEIs and ARBs for patients with type 2 diabetes has not been determined. ARBs that bind directly to angiotensin II type 1 (AT1) receptors are assumed to offer more specific RAAS inhibition and have fewer systemic adverse effects than ACEIs, theoretically accompanied by better tolerance and easier achievement of clinical targets. ARBs were also shown to have renal protective effects, [9–11] but unclear if they have similar cardioprotective effects as ACEIs [12,13]. Moreover, randomized

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controlled trials (RCTs) have found no difference in the cardiovascular benefits of ACEIs and ARBs in patients with heart failure [14–16]. A subgroup analysis of the ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint (ONTARGET) trial [17] showed that ACEIs and ARBs have similar effects on MACEs in a high-risk type 2 diabetes subgroup (38% of the study cohort), but meta-analyses indirectly comparing drug effects show conflicting results [7,8,18–20]. In light of these controversial findings, we conducted a nationwide, propensity score-matched, population-based cohort study involving previously ACEI- and ARB-naïve patients with newly diagnosed type 2 diabetes receiving these drugs to estimate subsequent MACE outcomes in a comparison of treatment regimens prescribed by physicians. 2. Methods 2.1. Data source Data for this study were extracted from Taiwan's National Health Insurance Research Database (NHIRD), which covers about 99% of Taiwan's average 23 million residents from 1995 to 2012. The NHIRD has been described in detail previously [21,22]. For this study, we used the NHIRD's Longitudinal Cohort of Diabetes Patients dataset, which contains all medical claims data for 120,000 patients with incident diagnoses of diabetes per year during 2000–2010, representing the majority of this population in Taiwan. Because all claims data released by the NHIRD are de-identified and secondary, this study was exempted from full review by the Institutional Review Board of Taipei City Hospital. 2.2. Study population and design The diabetes cohort consisted of all adult patients (age ≥ 20 years) with incident diagnoses of diabetes recorded in the NHIRD between January 2000 and December 2010. Each diabetes case was defined based on the presence of one primary discharge diagnosis of diabetes mellitus (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 250.x), two ambulatory visits with a diagnosis of diabetes mellitus (ICD9-CM code 250.x), or use of any antidiabetic medication. The accuracy of diabetes diagnoses recorded in the NHIRD has been validated [23]. Patients prescribed any ACEI or ARB within 90 days after diabetes diagnosis was allocated to the ACEI and ARB cohorts, respectively. Single-pill combinations based on ACEIs or ARBs (e.g., combination of ACEIs/ARBs and diuretic) were also included in the corresponding cohort. The index date was defined as 91 days after the diagnosis of diabetes to avoid immortal time bias. Subjects with myocardial infarction or cerebrovascular disease before the index date were excluded to allow the identification of incident events. To minimize contamination bias, we further excluded patients who received ACEIs or ARBs before diabetes diagnosis or multiple ACEIs/ARBs before the index date. A propensity score analysis was performed to reduce confounding by indication generated by differences in medical history and concomitant medications between cohorts. This approach has been used previously for the same purpose (eMethod) [24]. Baseline demographic covariates included in the analysis were age, sex, year of index date, month of index date, monthly income, urbanization levels, hospital levels, Charlson Comorbidity Index (CCI) and adapted Diabetes Complications Severity Index (aDCSI) scores [25] for the 5-year period before the index date (Table 1). We collected data on use of medications such as antidiabetic and antihypertensive drugs to represent individuals' glycemic and blood pressure control. Data on subjects' use of concomitant drugs relevant to cardiovascular diseases (e.g. antiplatelet agents, steroid, nitrate, nonsteroidal antiinflammatory drugs, proton pump inhibitors, statin, and selective serotonin re-uptake inhibitors) were included. Other baseline comorbidities (defined by ICD-9-CM codes) that potentially related to cardiovascular events but were not included in the CCI score were also examined (Table 1). Our previous study provides details of ICD-9-CM codes for baseline comorbidities [21]. Finally, ACEI users were matched 1:1 with ARB users using propensity scores closest in value, based on nearest-neighbor matching without replacement and using calipers of width equal to 0.1 standard deviation of the logit of the propensity score. 2.3. ACEI/ARB exposure For each ACEI or ARB prescription, information on drug type, quantity, dose, dispensing date, and days of drug supply was collected. The period of exposure to each ACEI/ARB was defined to extend from the first day of prescription to the end of the last-dispensed drug supply. If a patient had filled a subsequent ACEI/ARB prescription within 90 days after the previous supply, he or she was considered to have received continuous therapy. The definition of 90-day grace period has been verified as a better threshold to predict discontinuation of RAAS blockers [26]. An interval of more than 90 days between prescription refills was categorized as discontinuation of medication. 2.4. Outcomes The primary outcomes were MACEs, including hospitalization with the principal diagnosis of ischemic stroke (ICD-9-CM code 433.x, 434.x, or 436) or myocardial infarction

(ICD-9-CM code 410.x), and all-cause mortality. The secondary outcomes were hospitalization with the principal diagnosis of acute kidney injury (ICD-9-CM code 584.x) or hyperkalemia (ICD-9-CM code 276.7). Individuals in both cohorts were followed until death, loss to follow up, or the end of the study period (31 December 2012). 2.5. Statistical analysis Descriptive statistics were used to characterize the study cohorts at baseline. Baseline characteristics were compared between groups using Pearson's chi-squared tests for categorical variables and independent t-tests and Mann–Whitney U-tests for parametric and nonparametric continuous variables, respectively. Standardized mean differences were used to compare characteristics between groups after propensity score matching. Propensity scores for the likelihood of ARB use were calculated by multivariate logistic regression, conditional on baseline covariates (eTable 1). Intention-to-treat (ITT) and as-treated analyses were performed. In ITT analyses, all ARB and ACEI users were followed within the cohort to which they were initially assigned until the occurrence of an event of interest or the end of the study period, regardless of any change in treatment status. In as-treated analyses, subjects were censored on the day of add-on or switching to the opposite medication (either an ACEI or ARB) or treatment discontinuation. The cumulative incidence of events was calculated using the Kaplan–Meier method and compared between groups using the log-rank test. Poisson distribution was used to compare the incidence rates of events between groups. The risk of MACEs in the two cohorts was compared by Cox regression model calculating hazard ratios (HRs). The ACEI cohort was selected as the reference group. The Schoenfeld residuals was used to test the assumption of our Cox proportional hazard model (for all-cause mortality: P = 0.358; for myocardial infarction: P = 0.494; for ischemic stroke: P = 0.402; for acute kidney injury: P = 0.925; for hyperkalemia: P = 0.462). Besides, the competing-risk regression based on Fine and Gray's model was also calculated [27]. Subgroup analyses were performed according to age, sex, CCI score, hypertension, chronic kidney disease, heart failure, coronary artery disease, and antihypertensive drug use. Interaction tests were performed using the likelihood ratio test. Since enrollees in the present study were accumulated over a 11-year period (Table 1), the follow-up durations in this population may differ significantly. To address the differential follow-up, we performed a sensitivity analyses with fixed duration by limiting the index year of follow-up to between 2000 and 2006 and at least 5 years of followup. Additionally, to test the impact of other confounding factors on our results, we performed sensitivity analyses in which (1) we used different maximum allowed medication gaps (30, 60, or 90 days) to be defined as continuous use of medication, (2) hospital-level variation in patterns of medication prescription was adjusted by Cox model with sandwich estimator of hospital-level or with shared-frailty model of the individual hospital identifier, and (3) a novel class of statistical method, marginal structural model with inverseprobability-of-treatment weights [28] was used to estimate the effect of selective discontinuation of ARBs on MACEs (eMethod). Microsoft SQL Server 2012 (Microsoft Corporation, Redmond, Washington, USA) was used for data linkage, processing, and sampling. Propensity scores were calculated using SAS version 9.3 (SAS Institute Inc., Cary, North Carolina, USA) and propensity score matching was performed by PSmatching macro [29]. All other statistical analyses were conducted using STATA statistical software and stcrreg command was used to implement competing-risks regression (version 13.0; StataCorp., College Station, Texas, USA). Statistical significance was defined as P b 0.05.

3. Results 3.1. Characteristics of the study population We identified 21,436 ARB users and 30,777 ACEI users with newly diagnosed diabetes between January 2000 and December 2010 who met the inclusion criteria (eFigure 1). Compared with ACEI users, ARB users were older and had higher CCI and aDCSI scores; the proportions of male subjects, hypertension, coronary artery disease, chronic kidney disease, peptic ulcer disease, dyslipidemia, valvular heart disease and cancer were higher in the ARB than in the ACEI cohort. After propensity score matching, ARB and ACEI users had similar baseline characteristics (Table 1, eTable 2 & eFigure 2–3). 3.2. Follow-up and adherence The mean follow-up period was 6.2 years. The drug persistence rate at 1 year, 3 years, 5 years and 10 years (72.2%, 45.5%, 35.7% and 13.0%, respectively) in ARB cohort was higher as compared to that in ACEI cohort (58.1%, 30.1%, 19.5%, and 6.0% respectively) (eTable 3). During the follow-up period, in ARB cohort, 1823 patients (5.9%) switched to ACEIs, 3155 (14.7%) have add-on ACEIs, and 18,435 patients (86.0%) ever discontinued original treatment; in ACEI cohort, 4699 patients (15.3%)

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Table 1 Baseline characteristics of diabetes patients. Characteristics

Before propensity score-matching

Propensity score-matched StD

ARB cohort

ACEI cohort

StD†

30,777 55.4 (12.3) 17,498 (56.8)

−0.018 0.032

16,570 55.2 (12.4) 9692 (58.5)

16,570 55.1 (12.4) 9697 (58.5)

0.010 −0.001

685 (3.2) 971 (4.5) 1389 (6.5) 1789 (8.3) 2243 (10.5) 1721 (8.0) 2014 (9.4) 2238 (10.4) 2388 (11.1) 2549 (11.9) 2698 (12.6) 751 (3.5)

3020 (9.8) 3970 (12.9) 3502 (11.4) 2984 (9.7) 3112 (10.1) 2758 (9.0) 2604 (8.5) 2323 (7.5) 2114 (6.9) 2165 (7.0) 1799 (5.8) 426 (1.4)

−0.271 −0.300 −0.172 −0.047 0.012 −0.033 0.033 0.101 0.150 0.166 0.235 0.138

685 (4.1) 971 (5.9) 1377 (8.3) 1661 (10.0) 1915 (11.6) 1553 (9.4) 1599 (9.6) 1625 (9.8) 1609 (9.7) 1629 (9.8) 1545 (9.3) 401 (2.4)

688 (4.2) 955 (5.8) 1410 (8.5) 1714 (10.3) 1969 (11.9) 1587 (9.6) 1594 (9.6) 1622 (9.8) 1574 (9.5) 1621 (9.8) 1455 (8.8) 381 (2.3)

−0.001 0.004 −0.007 −0.011 −0.010 −0.007 0.001 0.001 0.007 0.002 0.019 0.008

Monthly income, NT dollars Dependent b19,100 19,100 − 41,999 ≥42,000

5589 (26) 4510 (21) 8850 (41.2) 2487 (11.6)

8421 (27.3) 6059 (19.6) 13,650 (44.3) 2647 (8.6)

−0.030 0.033 −0.063 0.102

4375 (26.4) 3477 (21.0) 6985 (42.2) 1733 (10.5)

4345 (26.2) 3507 (21.2) 6999 (42.2) 1719 (10.4)

0.004 −0.004 −0.002 0.003

Urbanizationa Level 1 Level 2 Level 3 Level 4 (rural area)

8693 (40.6) 11,532 (53.8) 1037 (4.8) 174 (0.8)

12,369 (40.2) 16,724 (54.3) 1396 (4.5) 288 (0.9)

0.007 −0.011 0.014 −0.013

6750 (40.7) 8916 (53.8) 760 (4.6) 144 (0.9)

6847 (41.3) 8822 (53.2) 771 (4.7) 130 (0.8)

−0.012 0.011 −0.003 0.009

Hospital level of first prescription of ARB or ACEI Level I (Medical center) 5001 (23.3) Level II 6081 (28.4) Level III 6967 (32.5) Level IV (Local medical clinics) 3387 (15.8)

4174 (13.6) 6307 (20.5) 7562 (24.6) 12,734 (41.4)

−0.590 0.254 0.184 0.176

3376 (20.4) 3387 (20.4) 4549 (27.5) 5258 (31.7)

3261 (19.7) 3324 (20.1) 4668 (28.2) 5317 (32.1)

0.017 0.009 −0.016 −0.008

Outpatient Visits of Metabolism & Endocrinology, in the past one year 0–5 visits 17,895 (83.4) 6–10 visits 3155 (14.7) 11–15 visits 322 (1.5) N15 visits 64 (0.2)

27,050 (87.8) 3257 (10.5) 376 (1.2) 94 (0.3)

−0.126 0.125 0.024 −0.001

14,050 (84.8) 2239 (13.5) 231 (1.4) 50 (0.3)

14,050 (84.8) 2248 (13.6) 227 (1.4) 45 (0.3)

0.000 −0.002 0.002 0.006

Charlson Comorbidity Index Scoreb 1 2 3 ≥4

11,952 (38.8) 7823 (25.4) 5607 (18.2) 5395 (17.5)

−0.025 −0.003 0.003 0.031

6245 (37.7) 4197 (25.3) 3044 (18.4) 3084 (18.6)

6288 (37.9) 4122 (24.9) 3077 (18.6) 3083 (18.6)

−0.005 0.010 −0.005 0.000

Adapted Diabetes Complications Severity Index scorec 0 11,316 (52.8) 1 5527 (25.8) 2 2375 (11.1) ≥3 2218 (10.3)

16,847 (54.7) 7893 (25.6) 3271 (10.6) 2766 (9.0)

0.003 0.015 0.046 −0.039

8806 (53.1) 4263 (25.7) 1789 (10.8) 1712 (10.3)

8747 (52.8) 4295 (25.9) 1873 (11.3) 1655 (10.0)

0.007 −0.004 −0.016 0.011

Drugs for diabetes Acarbose inhibits enzymes Sulfonylurea Insulin Metformin Thiazolidinediones Glinide Dipeptidyl peptidase-4 inhibitor

835 (3.8) 5274 (24.6) 299 (1.3) 8450 (39.4) 1062 (4.9) 819 (3.8) 215 (1.0)

786 (2.5) 8053 (26.1) 324 (1.0) 11,323 (36.7) 657 (2.1) 766 (2.4) 68 (0.2)

0.076 −0.036 0.031 0.054 0.153 0.076 0.100

537 (3.2) 4178 (25.2) 214 (1.3) 6450 (38.9) 596 (3.6) 567 (3.4) 83 (0.5)

580 (3.5) 4173 (25.2) 226 (1.4) 6469 (39.0) 576 (3.5) 589 (3.6) 66 (0.4)

−0.002 0.001 −0.006 −0.002 0.007 −0.007 0.015

Anti-hypertensive drug Alpha-blocker Beta-blocker Calcium channel blocker Diuretics Other anti-hypertensive drug

400 (1.8) 3028 (14.1) 6056 (28.2) 2191 (10.2) 197 (0.9)

547 (1.7) 4181 (13.5) 7535 (24.4) 3476 (11.2) 495 (1.6)

−0.062 −0.035 0.086 0.016 0.007

306 (1.8) 2308 (13.9) 4500 (27.2) 1819 (11.0) 176 (1.1)

299 (1.8) 2299 (13.9) 4476 (27.0) 1814 (10.9) 167 (1.0)

0.003 0.002 0.003 0.001 0.005

Other concomitant medications Antiplatelet agent‡ Steroid Nitrate NSAID PPI

2840 (13.2) 954 (4.4) 588 (2.7) 2956 (13.7) 476 (2.2)

3554 (11.5) 1639 (5.3) 863 (2.8) 5061 (16.4) 413 (1.3)

0.052 −0.041 −0.004 −0.074 0.066

2155 (13.0) 790 (4.8) 480 (2.9) 2380 (14.4) 313 (1.9)

2209 (13.3) 787 (4.7) 482 (2.9) 2408 (14.5) 296 (1.8)

−0.010 0.001 −0.001 −0.005 0.008

ARB cohort

ACEI cohort

Patient (no.) Mean age (SD), year Male

21,436 55.2 (12.4) 12,528 (58.4)

Year of index date 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

8066 (37.6) 5421 (25.2) 3933 (18.3) 4016 (18.7)

(continued on next page)

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Table 1 (continued) Characteristics

Before propensity score-matching

Propensity score-matched

ARB cohort

ACEI cohort

StD

ARB cohort

ACEI cohort

StD†

Statin SSRI

3624 (16.9) 129 (0.6)

3787 (12.3) 190 (0.6)

0.131 −0.002

2599 (15.7) 101 (0.6)

2617 (15.8) 102 (0.6)

−0.003 −0.001

Comorbidities Coronary artery disease Hypertension Heart failure Peripheral vascular disease Peptic ulcer disease Chronic kidney disease Liver disease Atrial fibrillation Dyslipidemia Valvular heart disease Cancer Autoimmune disease Propensity score (SD)

4315 (20.1) 19,152 (89.3) 1161 (5.4) 444 (2.0) 5717 (26.6) 1845 (8.6) 4951 (23.0) 218 (1.0) 10,296 (48) 960 (4.4) 1272 (5.9) 394 (1.8) 0.51 (0.17)

5974 (19.4) 26,962 (87.6) 1694 (5.5) 652 (2.1) 7696 (25) 2442 (7.9) 7301 (23.7) 333 (1.0) 13,189 (42.8) 1082 (3.5) 1553 (5.0) 526 (1.7) 0.34 (0.19)

0.018 0.055 −0.004 −0.003 0.038 0.024 −0.015 −0.006 0.104 0.049 0.039 0.010 0.923

3299 (19.9) 14,623 (88.2) 945 (5.7) 344 (2.1) 4314 (26.0) 1387 (8.4) 3878 (23.4) 173 (1.0) 7726 (46.6) 702 (4.2) 943 (5.7) 298 (1.8) 0.46 (0.17)

3291 (19.9) 14,591 (88.1) 937 (5.7) 339 (2.0) 4358 (26.3) 1393 (8.4) 3867 (23.3) 181 (1.1) 7708 (46.5) 691 (4.2) 944 (5.7) 294 (1.8) 0.46 (0.18)

0.001 0.006 0.002 0.002 −0.006 −0.001 0.002 −0.005 0.002 0.003 0.000 0.002 0.004

* All data were descripted as number (%), except mean age and propensity score. Abbreviations: SD, standard deviation; StD, Standardized difference; NT$, new Taiwan dollars; ACEI, angiotensin-converting-enzyme inhibitors; ARB, Angiotensin II receptor blockers; NSAIDs, Non-steroidal anti-inflammatory drugs; SSRI, Selective serotonin re-uptake inhibitors. a Urbanization levels in Taiwan are divided into four strata according to the Taiwan National Health Research Institute publications. Level 1 designates the most urbanized areas, and level 4 designates the least urbanized areas. b Charlson Comorbidity Index score is used to determine overall systemic health. With each increased level of CCI score, there are stepwise increases in the cumulative mortality. c Adapted Diabetes Complications Severity Index is a 13-point scale from 7 complication categories: retinopathy, nephropathy, neuropathy, cerebrovascular, cardiovascular, peripheral vascular disease, and metabolic, ranging from each complication. Each complication produced a numeric score ranging from 0 to 2 (0 = no abnormality, 1 = some abnormality, 2 = severe abnormality). † Imbalance defined as absolute value greater than 0.020. ‡ Including aspirin, clopidogrel, ticlopidine and cilostazol.

have add-on ARBs, 1829 patients (5.9%) switched to ARBs, and 27,115 patients (88.1%) ever discontinued original treatment (eTable 4).

3.4. Sensitivity analysis Our results remained unchanged in sensitivity analyses of subpopulation limiting the follow-up duration at least 5 years (eTable 6), different maximal allowed medication gaps (eTable 7), or different hospitallevel variation in patterns of ACEI/ARB prescription (eTable 8–9). Even in marginal structural models with inverse-probability-of-treatment weights, ACEI and ARB users had similar risks of MACEs (eTable 10). Finally, in subgroup analyses stratified by age, sex, CCI score, hypertension, chronic kidney disease, heart failure, coronary artery disease, and antihypertensive drug use, the effects of ARB and ACEI use on the risk of MACEs remained consistent (eTable 11–13 & eFigure 5).

3.3. Long-term risks of major adverse cardiac events and adverse effects In ITT analyses, ACEI and ARB users had similar risks of myocardial infarction (HR: 0.92; 95% confidence interval [CI]: 0.80 to 1.07), ischemic stroke (HR: 0.95; 95% CI: 0.87 to 1.04) and all-cause mortality (HR: 0.95; 95% CI: 0.89 to 1.01) after propensity score matching (Table 2). MACE risk was also similar among both cohorts before propensity score matching (eTable 5 & 6). The two cohorts had similar risks of hospitalization for acute kidney injury (HR: 0.91; 95% CI: 0.82 to 1.02) and hyperkalemia (HR: 1.02; 95% CI: 0.84 to 1.22). The cumulative incidence of MACEs and adverse effects in ACEI and ARB users were illustrated in Figs. 1–3 and eFigure 4, respectively. As-treated analysis showed similar impacts of ACEI and ARB use on MACEs and adverse effects (Table 2).

4. Discussion To our knowledge, this observational cohort study is the first to directly compare the treatment effects of naïve ACEI and ARB use on

Table 2 Incidence and risk of myocardial infarction, ischemic stroke, all-cause mortality, and hospitalization for acute kidney injury and hyperkalemia among patients using ARB and ACEI. ARB cohort

ACEI cohort (as reference) a

Crude

No. of event

Person–years

Incidence rate

3.33 8.68 19.99 5.44 2.12

380 942 2230 624 221

105,455 103,413 106,483 104,939 105,889

3.27 7.18 3.38 3.08 12.54

110 241 101 93 41

29,465 29,242 29,583 29,384 29,510

No. of event

Person–years

Incidence rate

Intention-to-treat analysis Myocardial infarction Ischemic Stroke All-cause mortality Hospitalization for acute kidney injury Hospitalization for hyperkalemia

351 897 2126 570 225

105,372 103,364 106,360 104,829 105,918

As-treated analysis Myocardial infarction Ischemic Stroke All-cause mortality Hospitalization for acute kidney injury Hospitalization for hyperkalemia

148 323 154 139 57

45,293 44,968 45,555 45,097 45,447

Abbreviations: CI, confidence interval; ARB, angiotensin II receptor blockers; ACEI, angiotensin-converting-enzyme inhibitors. a Per 103 person–years.

a

Hazard ratio (95% CI)

P value

3.60 9.11 20.94 5.95 2.09

0.92 (0.80–1.07) 0.95 (0.87–1.04) 0.95 (0.89–1.01) 0.91 (0.82–1.02) 1.02 (0.84–1.22)

0.280 0.296 0.182 0.117 0.867

3.73 8.24 3.41 3.17 13.89

0.84 (0.65–1.07) 0.85 (0.72–1.06) 1.07 (0.83–1.38) 0.90 (0.69–1.17) 0.86 (0.57–1.29)

0.157 0.057 0.579 0.432 0.462

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Fig. 1. Cumulative incidence of myocardial infarction in ACEI and ARB users with type 2 diabetes.

MACEs in patients with newly diagnosed type 2 diabetes. We found no significant difference in MACE outcomes or adverse effects, such as hospitalization for acute kidney injury or hyperkalemia, between ACEI and ARB users. In either patients receiving ACEI/ARB monotherapy or those receiving combined therapy with other non-RAAS antihypertensive drugs, ARBs were not associated with better effects compared with ACEIs. The comparative effectiveness of ACEIs and ARBs was consistently demonstrated in both sexes and across age groups and categories of baseline comorbidity. Systematic reviews of RCTs examining the relative effectiveness of ACEIs/ARBs and non-RAAS blocker comparators in patients with diabetes have associated ACEIs and ARBs with improved renal outcomes [9,30]. However, effects on hard endpoints, such as myocardial infarction and mortality, were substantial only for ACEIs. The Randomized Olmesartan and Diabetes Microalbuminuria Prevention trial [31] found that olmesartan treatment was associated with an unexpectedly greater incidence of cardiovascular death compared with placebo. However, based on data from large retrospective cohort studies [32–35], the US Food and Drug Administration concluded that no clear evidence supported a clinically significantly greater cardiovascular risk associated with olmesartan than with other ARBs or ACEIs. Six RCTs have examined the relative effectiveness of ACEIs or ARBs in populations of people with diabetes [17,36–40]. However, five of these

Fig. 2. Cumulative incidence of ischemic stroke in ACEI and ARB users with type 2 diabetes.

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Fig. 3. Cumulative incidence of all-cause mortality in ACEI and ARB users with type 2 diabetes.

trials that enrolled subjects with type 2 diabetes and early nephropathy were limited by small samples and short-term follow-up periods [36–40]. The primary endpoints were exclusively renal outcomes (e.g., change in glomerular filtration rate, albuminuria), which did not differ between ACEI and ARB treatment groups. The sixth study, the ONTARGET trial [17], compared ACEI and ARB treatments in subjects with vascular disease or high-risk diabetes without heart failure and produced findings similar to those of the present study. However, that study did not examine a homogenous diabetes population and was not randomized according to the presence and severity of diabetes. A meta-analysis of 63 trials conducted in 2013 demonstrated no superiority of ACEI or ARB treatment in patients with diabetes [8]. By contrast, another recent meta-analysis of 35 RCTs found that ACEIs appeared to be associated with reduced occurrence of MACEs compared with ARBs in patients with diabetes mellitus [7]; however, this study did not include the most important trial examining this issue, the ONTARGET trial [17]. The results of meta-analyses should be interpreted cautiously due to biases stemming from heterogeneity in study populations' background characteristics, variation in follow-up periods, and the examination of prespecified endpoints, which lead to the indirect comparison of individual trials [41]. Given these limitations and the paucity of head-to-head clinical trials, our study design may thus provide an ideal opportunity to resolve the controversy surrounding the preference for ACEI or ARB treatment in patients with type 2 diabetes using MACE endpoints. The strengths of the present study include the examination of a study cohort comprising nearly all patients with diabetes aged ≥ 20 years in Taiwan during 2000–2010, which minimized selection bias. Second, as RAAS blockers are target medications for patients with high cardiovascular risk, we excluded diabetic patients with prior histories of MACE and enrolled subjects from the time of diabetes diagnosis, to minimize the burden of cardiovascular diseases and reduce confounding by indication. In addition, we examined de novo ACEI/ARB use after diabetes diagnosis to prevent bias due to background exposure to these drugs. However, several limitations of this study should be addressed when interpreting the results. First, the retrospective observational cohort design made it difficult to account for unmeasured confounders, although we used propensity score analysis to control for baseline confounding characteristics in both groups. Second, ACEI or ARB prescription was based on the discretion of physicians in charge and was intended to achieve target blood pressure in almost 90% of study subjects with underlying hypertension. The NHIRD does not contain exact blood pressure data, but we included antihypertensive medication type in

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propensity score matching as a proxy for blood pressure control. However, some subjects' receipt of combined antihypertensive drug therapy for blood pressure control may have confounded our results. Subgroup analysis of ACEI/ARB monotherapy, from which patients receiving more than one antihypertensive drug were eliminated, produced results similar to the main analysis, further confirming our findings. Third, ACEI users may experience more adverse effects (e.g., cough) than did ARB users due to increased bradykinin levels, leading some subjects to discontinue ACEI treatment; the ITT analysis may thus have overestimated the actual effects of ACEIs, although this method better reflected real-world practice [42]. The as-treated analysis, which reduced dropout bias, reproduced the comparable effectiveness of ACEs and ARBs. Fourth, study subjects comprised of incident diabetes patients per year between 2000 and 2010, which indicated varying follow-up period (ranging from 2 year to 13 years). However, a sensitivity analysis with fixed duration of follow-up still supported our finding. Additionally, laboratory data such as plasma glucose or HbA1c levels were not present in this type of claim database. Nonetheless, we used anti-diabetes medication types and aDCSI scores, at least in part to reflect glycemic control. Finally, data on some individual characteristics, such as body mass index, smoking and/or alcohol habits, and family history of premature coronary artery disease, were also not available.

[4] [5]

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[8]

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5. Conclusion This study showed that ARBs and ACEIs had comparable effects on MACE outcomes in patients with newly diagnosed type 2 diabetes. Thus, ARBs are an equally effective alternative for patients with diabetes who are intolerant to ACEIs. Otherwise, with consideration of drug cost, ACEIs should be the first-line treatment strategy for this population. The choice between ACEIs and ARBs should thus be based on physicians' discretion or diabetic patients' preferences and tolerance of possible adverse effects.

[13]

[14]

[15]

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Grant support [17]

None. Potential conflicts of interest The authors declare that there are no conflicts of interest. Acknowledgments This study was based in part on data from the NHIRD provided by Bureau of National Health Insurance (BNHI) of the Department of Health and managed by the National Health Research Institute. The conclusions presented in this study are those of the authors and do not necessarily reflect the views of the BNHI, the Department of Health, or the National Health Research Institute.

[18]

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Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2015.07.053.

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