Abstract
Background
Perioperative use of angiotensin receptor blockers (ARBs) and angiotensin converting enzyme inhibitors (ACEi) in patients undergoing cardiac surgery remains controversial. The current practice of discontinuing renin-angiotensin-system inhibitors before surgery, may negate their beneficial effects in vulnerable populations including patients with metabolic syndrome who exhibit elevated renin-angiotensin system activity. We hypothesized that preoperative ARBs use is associated with reduced incidence of postoperative complications, compared to ACEi or no drug, in patients with metabolic syndrome undergoing coronary artery bypass grafting.
Methods
We used propensity matching to derive a cohort of 1,351 patients from 2,998 who underwent coronary artery bypass graftingbased on preoperative use of ARBs, ACEi, or no renin-angiotensin-system inhibitors. Our primary endpoint was a composite of adverse events occurring within 30 days after surgery: new onset atrial fibrillation/flutter, arrhythmia requiring cardioversion, perioperative myocardial infarction, acute renal failure, need for dialysis, cerebrovascular accidents, acute respiratory failure, or perioperative death.
Results
At least one adverse event occurred in 524 (38.8%) of matched cohort patients (1,184, 39.6% of all patients). Adjusting for EuroSCORE and metabolic syndrome in the matched cohort, preoperative use of ARBs was associated with lower incidence of adverse events in patients with metabolic syndrome compared to preoperative use of no renin-angiotensin-system inhibitors (OR 0.43;95%CI 0.19–0.99) or ACEi (OR 0.38;95%CI 0.16–0.88).
Conclusions
ARBs, but not ACEi, used preoperatively confer benefit within 30 days after cardiac surgery in patients with metabolic syndrome, suggesting potential efficacy differences of these drug classes in reducing cardiovascular morbidity and mortality in ambulatory versus surgical patients.
Perioperative management of angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs), in cardiac surgery patients remains controversial.[1] ACEi use is associated with increased incidence of hypotension and/or vasoplegic syndrome during general anesthesia which continues into the postoperative period.[2] ACEi may independently predict mortality, inotrope use, postoperative renal dysfunction and new onset postoperative atrial fibrillation after coronary bypass grafting (CABG) surgery.[3] However, more recent meta-analyses concluded that preoperative treatment with renin-angiotensin-system inhibitors (RASi) is associated with a reduced incidence of acute kidney injury (AKI),[4] perioperative myocardial injury,[5] and may provide perioperative mortality benefits in diabetic patients.[6] Yet, the practice continues of discontinuing perioperative RASi continues.[3,7]
ARBs and ACEi are often considered interchangeable. However, combining ARBs/ACEi to analyze RASi effects on incidence of postoperative adverse events after cardiac surgery, is a critical limitation because these drug classes have different mechanisms of inhibition. ACEi reduce circulating and local levels of angiotensin II (AngII) while increasing bradykinin levels, whereas ARBs can suppress inflammation and interrupt AngII-dependent and -independent receptor activation,[8] thereby blocking effects of AngII produced via non-ACE pathways[9] without increasing bradykinin levels. Thus, additional comparative analyses are essential.
Increasing evidence suggests a bidirectional pathogenic relationship between an overactive RAS and metabolic syndrome (MetS). RAS signaling, activated by several factors associated with MetS, contributes to inflammation, reactive oxygen species generation, and impaired insulin signaling.[10] Findings recently verified by a clinical trial showing that RASi reduces cardiovascular events in MetS patients.[11]
Therefore, our primary objective was to compare the effect of preoperative use of ARBs vs ACEi on incidence of adverse postoperative outcomes in the setting of CABG surgery, using no RASi therapy as comparator, stratified by presence of MetS.
Patients and Methods
We performed a retrospective analysis of patients in the Project of Ex-vivo Vein Graft Engineering via Transfection (PREVENT-IV) trial (ClinicalTrials.gov:NCT0042081) who underwent primary CABG surgery between August 2002 and October 2003 at 107 centers across the U.S. The PREVENT IV protocol was approved by institutional review boards of all participating sites, and all enrolled patients provided written informed consent.
We began with 3,014 PREVENT-IV participants, however, patients who received both ARB and ACEi preoperatively (n=26) were excluded to allow for independent assessment of class effects. In the final study population (n=2,988) 3 groups were identified according to their preoperative RASi use: ARBs (n = 193); ACEi (n = 1,055), and no RASi therapy (n = 1,740). Baseline characteristics of these groups are presented in Table 1. RASi were started/restarted postoperatively at the discretion of the treating physician. We identified a subpopulation of patients with diagnostic criteria of MetS as set forth by the National Cholesterol Education Program - Adult Treatment Panel III (NCEP-ATP III) (Table 2) [12].
TABLE 1.
Characteristics of the study population by preoperative RAS Inhibitor Use
| Full Cohort | Matched Analysis Cohort | |||||||
|---|---|---|---|---|---|---|---|---|
| Preoperative RAS Inhibitor Use | Preoperative RAS Inhibitor Use | |||||||
| No RASi (N = 1740) | ARBs (N = 193) | ACEi (N = 1055) | SMD | No RASi (N = 579) | ARBs (N = 193) | ACEi (N = 579) | SMD | |
| Demographics | ||||||||
| Age ≥75 years | 204 (11.7) | 27 (14.0) | 126 (11.9) | 0.045 | 73 (12.6) | 27 (14.0) | 63 (10.9) | 0.063 |
| Female Gender | 346 (19.9) | 56 (29.0) | 220 (20.9) | 0.142 | 156 (26.9) | 56 (29.0) | 151 (26.1) | 0.044 |
| Race | 0.162 | 0.170 | ||||||
| Caucasian | 1590 (91.4) | 174 (90.2) | 955 (90.5) | 524 (90.5) | 174 (90.2) | 523 (90.3) | ||
| Black | 76 (4.4) | 8 (4.1) | 53 (5.0) | 26(4.5) | 8 (4.1) | 28 (4.8) | ||
| Asian | 17 (1.0) | 6 (3.1) | 9 (0.9) | 7 (1.2) | 6 (3.1) | 6 (1.0) | ||
| Hispanic | 42 (2.4) | 3 (1.6) | 26 (2.5) | 17 (2.9) | 3 (1.6) | 17 (2.9) | ||
| Native American | 5 (0.3) | 2 (1.0) | 5 (0.5) | 1 (0.2) | 2 (1.0) | 3 (0.5) | ||
| Other | 10 (0.6) | 0 (0.0) | 7 (0.7) | 4 (0.7) | 0 (0.0) | 2 (0.3) | ||
| Clinical Risk Factors | ||||||||
| * Obesity: BMI >30 | 661 (38.0) | 88 (45.6) | 491 (46.5) | 0.116 | 253 (43.7) | 88 (45.6) | 257 (44.4) | 0.025 |
| * Hypertension | 1140 (65.5) | 179 (92.7) | 920 (87.2) | 0.475 | 542 (93.6) | 179 (92.7) | 538 (92.9) | 0.023 |
| *Hypercholesterolem ia | 1301 (74.8) | 153 (79.3) | 825 (78.2) | 0.071 | 474 (81.9) | 153 (79.3) | 477 (82.4) | 0.053 |
| * Diabetes | 521 (29.9) | 94 (48.7) | 510 (48.3) | 0.261 | 273 (47.2) | 94 (48.7) | 272 (47.0) | 0.023 |
| Metabolic Syndrome | 187 (10.7) | 41 (21.2) | 219 (20.8) | 0.193 | 121 (20.9) | 41 (21.2) | 120 (20.7) | 0.008 |
| Congestive heart failure | 128 (7.4) | 22 (11.4) | 138 (13.1) | 0.127 | 50 (8.6) | 22 (11.4) | 57 (9.8) | 0.061 |
| LV Dysfunction | 0.160 | 0.034 | ||||||
| Normal | 1136 (65.3) | 126 (65.3) | 584 (55.4) | 388 (67.0) | 126 (65.3) | 380 (65.6) | ||
| Moderate (LVEF 30%–50%) | 537 (30.9) | 58 (30.1) | 393 (37.3) | 169 (29.2) | 58 (30.1) | 174 (30.1) | ||
| Severe (LVEF< 30%) | 67 (3.9) | 9 (4.7) | 78 (7.4) | 22 (3.8) | 9 (4.7) | 25 (4.3) | ||
| Atrial flutter/fibrillation | 117 (6.7) | 16 (8.3) | 78 (7.4) | 0.040 | 43 (7.4) | 16 (8.3) | 42 (7.3) | 0.026 |
| Cigarette smoker | 0.094 | 0.059 | ||||||
| never | 527 (30.3) | 64 (33.2) | 345 (32.7) | 181 (31.3) | 64 (33.2) | 203 (35.1) | ||
| former | 802 (46.1) | 93 (48.2) | 469 (44.5) | 292 (50.4) | 93 (48.2) | 269 (46.5) | ||
| current | 411 (23.6) | 36 (18.7) | 241 (22.8) | 106 (18.3) | 36 (18.7) | 107 (18.5) | ||
| Prior MI | 352 (20.2) | 17 (8.8) | 235 (22.3) | 0.252 | 61 (10.5) | 17 (8.8) | 56 (9.7) | 0.039 |
| Prior PCI | 425 (24.4) | 47 (24.4) | 296 (28.1) | 0.056 | 156 (26.9) | 47 (24.4) | 172 (29.7) | 0.080 |
| Renal failure | 39 (2.2) | 7 (3.6) | 19 (1.8) | 0.075 | 14 (2.4) | 7 (3.6) | 16 (2.8) | 0.047 |
| History of dialysis | 11 (0.6) | 3 (1.6) | 7 (0.7) | 0.059 | 5 (0.9) | 3 (1.6) | 6 (1.0) | 0.042 |
| Cerebrovascular disease | 197 (11.3) | 30 (15.5) | 152 (14.4) | 0.083 | 81 (14.0) | 30 (15.5) | 92 (15.9) | 0.036 |
| Prior carotid surgery | 51 (2.9) | 11 (5.7) | 36 (3.4) | 0.091 | 24 (4.1) | 11 (5.7) | 25 (4.3) | 0.048 |
| Prior stroke | 84 (4.8) | 12 (6.2) | 67 (6.4) | 0.044 | 37 (6.4) | 12 (6.2) | 37 (6.4) | 0.005 |
| Prior TIA | 67 (3.9) | 15 (7.8) | 55 (5.2) | 0.113 | 25(4.3) | 15 (7.8) | 35 (6.0) | 0.097 |
| COPD | 266 (15.3) | 31 (16.1) | 176 (16.7) | 0.025 | 82 (14.2) | 31 (16.1) | 83 (14.3) | 0.035 |
| Other procedures | 188 (10.8) | 20 (10.4) | 107 (10.1) | 0.014 | 71 (12.3) | 20 (10.4) | 61 (10.5) | 0.040 |
| CPB use | 1364 (78.4) | 157 (81.3) | 834 (79.1) | 0.049 | 446 (77.0) | 157 (81.3) | 457 (78.9) | 0.071 |
| CPB duration (min) | 103.41 (36.80) | 104.97 (32.13) | 104.92 (36.64) | 0.029 | 105.35 (38.68) | 104.97 (32.13) | 103.11 (36.37) | 0.041 |
| EuroSCORE | 4.77 (2.75) | 4.87 (2.80) | 4.91 (2.86) | 0.033 | 4.95 (2.89) | 4.87 (2.80) | 4.73 (2.69) | 0.051 |
Values represent n (%); or mean ± SD. ACEi = angiotensin converting enzyme inhibitor;
ARB = angiotensin receptor blocker; COPD = chronic obstructive pulmonary disease; CPB = cardiopulmonary bypass;
EuroSCORE = European System for Cardiac Operative Risk Evaluation; LVEF = left ventricular ejection fraction; MI = myocardial infarction; PCI = percutaneous intervention;
SMD = Standardized Mean Differences; TIA = transient ischemic attack;
Table 2.
Diagnostic Elements of Metabolic Syndrome
| Criteria | Lab Value | Drug | Diagnosis | ||
|---|---|---|---|---|---|
| Elevated Triglycerides | ≥ 150 mg/dL (1.7 mmol/L) | Or | Fibrate | Or | High Triglycerides |
| Low HDL | < 40 mg/dL (1.0 mmol/L) Men < 50 mg/dL (1.3 mmol/L) Women |
Or | STATIN | Or | Hypercholeterolemia |
| Hypertension | Systolic ≥ 130 mmHg and/or Diastolic ≥ 85 mmHg | Or | Antihypertensive | Or | Hypertension |
| Hyperglycemia | > 100 mg/dL (5.5 mmol/L) | Or | Glycemic Medication | Or | Diabetes |
| Obesity | BMI ≥ 30 |
(Patients diagnosed with metabolic syndrome met the following accepted criteria as defined by lab value, drug use, or diagnosis by the NCEP-ATPIII)
Propensity Matching and Modeling Methods
To study treatment effect in this observational dataset, we used propensity matching to balance patient factors across the treatment groups. Multinomial logistic regression was used to calculate a propensity for membership in each of the 3 treatment groups based on the following factors: metabolic syndrome, age > 75 years, sex, obesity, diabetes, hypertension, hypercholesterolemia, congestive heart failure, left ventricular (LV) dysfunction, history of atrial fibrillation, recent MI, renal failure and cerebrovascular disease. Using the Pharmacological Toolbox[13] we then matched 3 patients who received no RASi therapy and 3 patients who received ACEi to each ARB patient in three rounds of nearest-neighbor triplet matching with a caliper of 0.25. We compared the covariate balance before and after propensity matching using standardized mean differences (SMD) and the tableone package in R (v.3.1.1.,www.r-project.org). Factors with SMD<0.1 after propensity matching were deemed balanced.
Outcome Definition
The primary outcome was a composite of major perioperative adverse events (MPAE) occurring within 30 days of index surgery (Table 3). The criteria for all outcomes were in accordance with the STS National Adult Cardiac Database, except perioperative myocardial infarction, which was described based on an elevation in the plasma level of CK-MB >10 times the upper limit of normal within 24 hours after surgery and adjudicated by the PREVENT IV Clinical Events Committee. The secondary endpoint was incidence of postoperative inotropic requirements.
Table 3.
Outcome Distribution by RAS Groups in Matched Cohort
| No RASi (N = 579) | ARBs (N = 193) | ACEi (N = 579) | Total (N = 1351) | |
|---|---|---|---|---|
| Major Periop Adverse Outcome | 219 (37.82%) | 70 (36.27%) | 235 (40.59%) | 524 (38.79%) |
| Pt died on or before 30 days | 4 (0.69%) | 0 (0.00%) | 10 (1.73%) | 14 (1.04%) |
| Peri-index CABG MI | 60 (10.36%) | 18 (9.33%) | 58 (10.02%) | 136 (10.07%) |
| Re-intubation | 17 (2.94%) | 6 (3.11%) | 18 (3.11%) | 41 (3.03%) |
| Any AFIB/Flutter AE | 144 (24.87%) | 48 (24.87%) | 163 (28.15%) | 355 (26.28%) |
| Any Renal Failure AE | 40 (6.91%) | 12 (6.22%) | 47 (8.12%) | 99 (7.33%) |
| Any CVA/TIA AE | 10 (1.73%) | 4 (2.07%) | 11 (1.90%) | 25 (1.85%) |
| Cardioversion | 9 (1.55%) | 0 (0.00%) | 15 (2.59%) | 24 (1.78%) |
| Dialysis | 4 (0.69%) | 0 (0.00%) | 6 (1.04%) | 10 (0.74%) |
Statistical Analysis
Modeling Methods
To account for the data structure in the matched cohort, the univariate association of MPAE and postoperative inotrope use with the treatment groups was assessed in 3 separate generalized estimating equation (GEE) models (comparing ARBs vs no RASI, ARBs vs ACEi, and ACEi vs. no RASI, respectively) to account for the data structure in the matched cohort. For the primary MPAE outcome, we also performed multivariate GEE modeling adjusting the treatment group effect for EuroSCORE, MetS, and their interactions. If an interaction effect was non-significant it was dropped from the final MPAE outcome model. Modeling was performed in SAS v. 9.4 (SAS Institute Inc., Cary, NC), and p<0.05 was considered statistically significant.
Sensitivity Analyses
To verify that the association between preoperative ARB use and MPAE is not measuring the effects of RASi therapy started postoperatively and thereby introducing confounding class-specific association results, we identified patients who received de novo ACEi between surgery and hospital discharge. We performed a sensitivity analysis of the association of MPAE with preoperative ARBs vs no RASi, restricting the cohort to only those not receiving ACEi postoperatively (n=1100).
Results
Compared to ACEi therapy in the full cohort, preoperative ARBs use was higher in women and hypertensive patients, and lower in patients with LV dysfunction and those with a history of MI. Although RASi overall were used more often in patients with MetS (21.2% of the study cohort), CHF, and cerebrovascular disease, the incidence of these comorbidities was not different between patients receiving ARBs versus ACEi preoperatively (Table 1). Interestingly, 40.6% of the patients with a history of MI (20.2% with MI within 30 days of enrollment) and 65.5% of hypertensive patients received no RASi therapy preoperatively.
Preoperative RASi and Patient Outcomes
In the overall cohort, 1,184 (39.6%) of patients experienced an MPAE and 33 (1.1%) died within 30 days after surgery. In the matched cohort, 524 (38.8%) patients experienced a MPAE and 14 (1%) died within 30 days of surgery. Univariate GEE analyses of treatment groups showed no significant difference in incidence of MPAE in the ARBs (n=70,36.3) vs no RASi (n=219, 37.8%, p=0.69) or the ACEi (n=235,40.6%,p=276) groups (Table 4). No perioperative deaths occurred in the ARBs treated group compared to 10 (1.7%) in the ACEi group. No patients in the ARBs group required postoperative cardioversion compared to 2.6% in the ACEi group (p=0.03). New onset POAF between drug classes, was higher in the ACEi group compared to the group that received no RASi (28.2% vs 24.9%); however, no significant difference was found between drug classes.
TABLE 4.
GEE Regression models of postoperative outcomes in Matched Cohort
| Univariable analysis | All (n=1,351) | Preoperative RAS inhibitor use | Group comparison p-Values | |||||
|---|---|---|---|---|---|---|---|---|
| No RASi (n=579) | ARBs (n=193) | ACEi (n=579) | ARBs vs ACEi vs No RASi | ARBs Vs No RASi | ACEi Vs No RASi | ARBs Vs ACEi | ||
| Major Perioperative Adverse Events (composite) | 524 (38.8%) | 219 (37.8%) | 70 (36.3%) | 235 (40.6%) | 0.452 | 0.685 | 0.319 | 0.276 |
| Postoperative inotrope use | 806 (59.7%) | 340 (58.7%) | 125 (64.8%) | 341 (58.9%) | 0.271 | 0.124 | 0.952 | 0.155 |
Values represent n (%)
ARB = angiotensin receptor blockers;
ACEi = angiotensin receptor blockers;
RASi = renin angiotensin system inhibitors
The incidence of other postoperative adverse events (notably acute renal failure) as well as postoperative inotrope requirement were similar between treatment groups. Univariate analysis of the associate of treatment group with postoperative inotrope use found no significant difference in the rate of inotrope us in ARB treated (n=125,64.8%) compared to no RASi (n=340,58.7%,p=0.12) or ACEi (n=341,58.9%,p=0.16)
Adjusted Regression Models Including Interaction with Metabolic Syndrome
We used multivariate GEE to evaluate the interaction effect of RASi with presence of MetS on incidence of MPAE in the matched cohort. Multivariable logistic regression models of MPAE adjusted for EuroSCORE and MetS, showed statistically significant interactions of ARBs with MetS when compared either to no RASi or ACEi use. In patients with MetS, preoperative ARBs treatment was associated with lower incident MPAE when compared to no RASi (OR 0.44; 95%CI 0.19–0.99,p=0.049) or ACEi (OR 0.38; 95%CI 0.16–0.889,p=0.025), suggesting a potential differential effect of preoperative ARB use in these patients (Table 5). Conversely, no treatment association was seen in the cohort of patients without MetS. Interaction terms between either RASi drug class and EuroSCORE were not significant. Thus, any potential beneficial effects of ARBs may not extend to other at-risk subpopulations based on their comorbidity profiles.
TABLE 5.
Multivariable GEE analysis of postoperative outcomes in Matched Cohort
| Multivariable analysis | Preoperative RAS inhibitor treatment group comparisons | |||||
|---|---|---|---|---|---|---|
| ARBs vs no RASi | ACEi vs no RASi | ARBs vs ACEi | ||||
| p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | |
| Major Perioperative Adverse Events (composite outcome) | ||||||
| EuroSCORE | <.0001 | 1.131 (1.076, 1.190) | <0.0001 | 1.127 (1.079, 1.178) | <0.0001 | 1.120 (1.062, 1.181) |
| Metabolic syndrome with reference treatment | 0.494 | 1.158 (0.761, 1.763) | 0.498 | 1.156 (0.760, 1.761) | 0.464 | 1.167 (0.771, 1.766) |
| Treatment effect by metabolic syndrome | ||||||
| With metabolic syndrome | 0.049 | 0.439 (0.194, 0.996) | 0.573 | 1.166 (0.685, 1.985) | 0.025 | 0.381 (0.163, 0.888) |
| Without metabolic syndrome | 0.493 | 1.139 (0.785, 1.654) | 0.296 | 1.154 (0.882, 1.509) | 0.940 | 0.986 (0.682, 1.425) |
ARB = angiotensin receptor blockers; ACEi = angiotensin receptor blockers;
CI = confidence interval; OR = odds ratio; RASi = renin angiotensin system inhibitors
Sensitivity Analysis
The ORs for MPAE were consistent even after excluding patients who received de novo ACEi postoperatively (n=251 in matched sample). In this restricted cohort, preoperative ARBs were not significantly associated with incidence of MPAE when compared to no RASi therapy in a main effects model (OR 0.79; 95%CI 0.53–1.17). However, in a model that included the interaction between ARBs and MetS, the effect remained significant (p=0.04), and the relative odds reduction in patients with MetS was larger compared to the unrestricted cohort (adjusted OR 0.32; 95%CI 0.12–0.84). Taken together, these findings indicate that the association of preoperative ARBs with reduced MPAE in patients with MetS is unlikely to be a measure of the effect of introducing ACEi therapy postoperatively, but rather is specific to ARB use.
Comment
Although our study was not powered to define the mechanisms responsible for reduced MPAE in patients taking preoperative ARBs, we provide evidence of their physiologic plausibility. We hypothesize that patients with MetS are more dependent on medical control of risk factors, and also mount a greater inflammatory response to preioperative stress,[14] which can be attenuated by ARBs, resulting in reduced MPAE.
MetS significantly increases the risk for adverse perioperative outcomes, including acute renal failure, stroke, postoperative cognitive dysfunction,[15] postoperative atrial fibrillation and mortality.[16] In these patients, an overactive RAS strongly links to increased obesity and hyperlipidemia[12] with all RAS signaling components found in visceral adipocytes[17], contributing to the pathogenesis of obesity and hypertension. Further, there is increasing evidence for non-ACE-dependent AngII production in human tissues.[18] Plasma AngII levels increase significantly during cardiopulmonary bypass[19] and do so despite treatment with ACEi.[20] The increase in available AngII may play a causative role in the pathogenesis of adverse perioperative outcomes across several organ systems.[21]
Angiotensin receptor Type 1 (AT1) expression is upregulated in hyperlipidemia,[22] and is associated with constitutively active signaling independent of AngII.[23] AT1 receptors control inflammation by directly regulating IL-6 expression and monocyte-macrophage homing via enhanced expression of chemokines and chemotaxis, as well as blood pressure and fluid retention actions of AngII.[24] AT1 receptors are inhibited by ARBs, whereas the AT2 receptors are cardio-protective, anti-inflammatory and regulate apoptosis and are not inhibited by ARBs.[25]
The RAS system is manipulated by ACEi and ARBs, yet these 2 drugs act at distinctly different sites along the pathway. ACEi inhibit ACE at the point of conversion to the vasoactive peptide whereas ARBs selectively block the AT1 receptor. This highly selective blockade allows unopposed AT2 receptor signaling thereby attenuating any inflammatory response.[25] Therefore, the associated reduction in MPAE among patients who took ARBS preoperatively in this study, may be related to their anti-inflammatory action. Indeed, ARB usage has been shown to reduce inflammation.[26]
In several studies, ACEi and ARB patients have not been evaluated separately, nor has preoperative dual therapy been noted. Rather, these distinctly different drugs have been combined in their analyses.[3,27] We believe this represents a significant residual confounder as key pharmacological differences between the 2 classes of drugs associate with different clinical effects in the perioperative setting. [25]
Our findings suggest that the efficacy paradox between ACEi and ARBs that has emerged from large clinical trials of treatments for hypertension in the ambulatory population,[28] may not extend to the acute perioperative care of patients undergoing CABG, where ARBs appear to reduce the incidence of composite MPAE, particularly in patients with MetS.
Limitations
Our results are based on a post-hoc analysis of data collected during a prospective multi-center randomized controlled trial. We analyzed effects of preoperative ARBs or ACEi on major postoperative adverse events, but not the effects of perioperative or postoperative therapy. However our sensitivity analysis, which excluded patients with de novo ACEi therapy initiated postoperatively, showed similar results. Finally, we acknowledge that the preoperative ARBs cohort was relatively small.
Conclusions
In EuroSCORE-adjusted analyses, we found no significant association of preoperative ARBs use with major perioperative adverse events. However, in patients with MetS, preoperative ARB use was associated with a reduction (approximately 60%) in the odds of developing 30-day MPAE. We believe this is the first evidence of a class-specific beneficial effect of preoperative ARBs in patients with MetS, a frequent comorbidity in patients undergoing CABG. Further investigations are needed to clarify the specific mechanism that drives ARBs protection in patients with MetS, and to evaluate long-term effects of ARBs in these patients.
Abbreviations and Acronyms
- AKI
Acute kidney injury
- AngII
Angiotensin II
- ARB
Angiotensin Receptor Blocker
- ACEi
Angiotensin-Converting Enzyme inhibitor
- CVA
Cerebrovascular Accident
- CABG
Coronary Artery Bypass Graft
- EuroSCORE
European System for Cardiac Operative Risk Evaluation Score
- MPAE
Major Perioperative Adverse Events
- MetS
Metabolic Syndrome
- MI
Myocardial Infarction
- NCEP-ATPIII
National Cholesterol Education Program-Adult Treatment Panel III
- PREVENT IV
Project of Ex-vivo Vein Graft Engineering via Transfection
- POAF
Postoperative atrial fibrillation/flutter
- TIA
Transient Ischemic Attack
- RASi
Renin-Angiotensin System inhibitor
Footnotes
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References
- 1.Hillis LD, Smith PK, Anderson JL, Bittl JA, et al. 2011 ACCF/AHA Guideline for Coronary Artery Bypass Graft Surgery: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2011;124:e652–735. doi: 10.1161/CIR.0b013e31823c074e. [DOI] [PubMed] [Google Scholar]
- 2.Hasija S, Makhija N, Choudhury M, Hote M, Chauhan S, Kiran U. Prophylactic vasopressin in patients receiving the angiotensin-converting enzyme inhibitor ramipril undergoing coronary artery bypass graft surgery. 2010;24:230–8. doi: 10.1053/j.jvca.2009.08.001. [DOI] [PubMed] [Google Scholar]
- 3.Miceli A, Capoun R, Fino C, et al. Effects of Angiotensin-Converting Enzyme Inhibitor Therapy on Clinical Outcome in Patients Undergoing Coronary Artery Bypass Grafting. Journal of the American College of Cardiology. 2009;54:1778–84. doi: 10.1016/j.jacc.2009.07.008. [DOI] [PubMed] [Google Scholar]
- 4.Cheungpasitporn W, Thongprayoon C, Srivali N, et al. Preoperative renin-angiotensin system inhibitors use linked to reduced acute kidney injury: a systematic review and meta-analysis. Nephrology Dialysis Transplantation. 2015;30:978–88. doi: 10.1093/ndt/gfv023. [DOI] [PubMed] [Google Scholar]
- 5.Benedetto U, Melina G, Capuano F, et al. Preoperative angiotensin-converting enzyme inhibitors protect myocardium from ischemia during coronary artery bypass graft surgery. Journal of Cardiovascular Medicine. 2008;9:1098–103. doi: 10.2459/JCM.0b013e32830a6daf. [DOI] [PubMed] [Google Scholar]
- 6.Cheng X, Tong J, Hu Q, Chen S, Yin Y, Liu Z. Meta-analysis of the effects of preoperative renin-angiotensin system inhibitor therapy on major adverse cardiac events in patients undergoing cardiac surgery. Eur J Cardiothorac Surg. 2015;47:958–66. doi: 10.1093/ejcts/ezu330. [DOI] [PubMed] [Google Scholar]
- 7.Auron M, Harte B, Kumar A, Michota F. Renin-angiotensin system antagonists in the perioperative setting: clinical consequences and recommendations for practice. Postgrad Med J. 2011;87:472–81. doi: 10.1136/pgmj.2010.112987. [DOI] [PubMed] [Google Scholar]
- 8.Akazawa H, Yabumoto C, Yano M, Kudo-Sakamoto Y, Komuro I. ARB and Cardioprotection. 2012;27:155–60. doi: 10.1007/s10557-012-6392-2. [DOI] [PubMed] [Google Scholar]
- 9.Ahmad S, Simmons T, Varagic J, Moniwa N. Chymase-dependent generation of angiotensin II from angiotensin-(1–12) in human atrial tissue. PLoS ONE. 2011 doi: 10.1371/journal.pone.0028501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Frigolet ME, Torres N, Tovar AR. The renin-angiotensin system in adipose tissue and its metabolic consequences during obesity. J Nutr Biochem. 2013;24:2003–15. doi: 10.1016/j.jnutbio.2013.07.002. [DOI] [PubMed] [Google Scholar]
- 11.Zreikat HH, Harpe SE, Slattum PW, Mays DP, Essah PA, Cheang KI. Effect of Renin-Angiotensin system inhibition on cardiovascular events in older hypertensive patients with metabolic syndrome. Metab Clin Exp. 2014;63:392–9. doi: 10.1016/j.metabol.2013.11.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: Executive Summary. Crit Pathw Cardiol. 2005;4:198–203. doi: 10.1097/00132577-200512000-00018. [DOI] [PubMed] [Google Scholar]
- 13.Rassen JA, Doherty M, Huang W, Schneeweiss S. Pharmacoepidemiology toolbox. …. 2014 [Google Scholar]
- 14.Marso SP, Murphy JW, House JA, Safley DM, Harris WS. Metabolic syndrome-mediated inflammation following elective percutaneous coronary intervention. Diabetes and Vascular Disease Research. 2005;2:31–6. doi: 10.3132/dvdr.2005.003. [DOI] [PubMed] [Google Scholar]
- 15.Hudetz JA, Patterson KM, Iqbal Z, Gandhi SD, Pagel PS. Metabolic syndrome exacerbates short-term postoperative cognitive dysfunction in patients undergoing cardiac surgery: results of a pilot study. J Cardiothorac Vasc Anesth. 2011;25:282–7. doi: 10.1053/j.jvca.2010.06.008. [DOI] [PubMed] [Google Scholar]
- 16.Echahidi N, Mohty D, Pibarot P, et al. Obesity and metabolic syndrome are independent risk factors for atrial fibrillation after coronary artery bypass graft surgery. Circulation. 2007;116:I213–9. doi: 10.1161/CIRCULATIONAHA.106.681304. [DOI] [PubMed] [Google Scholar]
- 17.Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. The Journal of Clinical Endocrinology and Metabolism. 2004;89:2548–56. doi: 10.1210/jc.2004-0395. [DOI] [PubMed] [Google Scholar]
- 18.Uehara Y, Miura S-I, Yahiro E, Saku K. Non-ACE pathway-induced angiotensin II production. Curr Pharm Des. 2013;19:3054–9. doi: 10.2174/1381612811319170012. [DOI] [PubMed] [Google Scholar]
- 19.Taylor KM, Bain WH, Russell M, Brannan JJ, Morton IJ. Peripheral vascular resistance and angiotensin II levels during pulsatile and no-pulsatile cardiopulmonary bypass. Thorax. 1979;34:594–8. doi: 10.1136/thx.34.5.594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Oh YJ, Lee JH, Nam SB, Shim JK, Song JH, Kwak YL. Effects of chronic angiotensin II receptor antagonist and angiotensin-converting enzyme inhibitor treatments on neurohormonal levels and haemodynamics during cardiopulmonary bypass. British Journal of Anaesthesia. 2006;97:792–8. doi: 10.1093/bja/ael268. [DOI] [PubMed] [Google Scholar]
- 21.Putnam K, Shoemaker R, Yiannikouris F, Cassis LA. The renin-angiotensin system: a target of and contributor to dyslipidemias, altered glucose homeostasis, and hypertension of the metabolic syndrome. Am J Physiol Heart Circ Physiol. 2012;302:H1219–30. doi: 10.1152/ajpheart.00796.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Nickenig G, Harrison DG. The AT(1)-type angiotensin receptor in oxidative stress and atherogenesis - Part II: AT(1) receptor regulation. Circulation. 2002;105:530–6. doi: 10.1161/hc0402.102619. [DOI] [PubMed] [Google Scholar]
- 23.Yasuda N, Akazawa H, Qin Y, Zou Y, Komuro I. A novel mechanism of mechanical stress-induced angiotensin II type 1–receptor activation without the involvement of angiotensin II. Naunyn-Schmied Arch Pharmacol. 2007;377:393–9. doi: 10.1007/s00210-007-0215-1. [DOI] [PubMed] [Google Scholar]
- 24.Benigni A, Cassis P, Remuzzi G. Angiotensin II revisited: new roles in inflammation, immunology and aging. EMBO Molecular Medicine. 2010;2:247–57. doi: 10.1002/emmm.201000080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Mehta PK, Griendling KK. Angiotensin II cell signaling: physiological and pathological effects in the cardiovascular system. Am J Physiol, Cell Physiol. 2007;292:C82–97. doi: 10.1152/ajpcell.00287.2006. [DOI] [PubMed] [Google Scholar]
- 26.Clancy P, Koblar SA, Golledge J. Angiotensin receptor 1 blockade reduces secretion of inflammation associated cytokines from cultured human carotid atheroma and vascular cells in association with reduced extracellular signal regulated kinase expression and activation. Atherosclerosis. 2014;236:108–15. doi: 10.1016/j.atherosclerosis.2014.06.011. [DOI] [PubMed] [Google Scholar]
- 27.Drenger B, Fontes ML, Miao Y, et al. Patterns of use of perioperative angiotensin-converting enzyme inhibitors in coronary artery bypass graft surgery with cardiopulmonary bypass: effects on in-hospital morbidity and mortality. Circulation. 2012;126:261–9. doi: 10.1161/CIRCULATIONAHA.111.059527. [DOI] [PubMed] [Google Scholar]
- 28.Ferrari R, Rosano GM. Not just numbers, but years of science: putting the ACE inhibitor-ARB meta-analyses into context. International Journal of Cardiology. 2013;166:286–8. doi: 10.1016/j.ijcard.2013.01.027. [DOI] [PubMed] [Google Scholar]