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Original Contribution |

Association of Perioperative β-Blockade With Mortality and Cardiovascular Morbidity Following Major Noncardiac Surgery FREE

Martin J. London, MD; Kwan Hur, PhD; Gregory G. Schwartz, MD, PhD; William G. Henderson, PhD, MPH
[+] Author Affiliations

Author Affiliations: Department of Anesthesia and Perioperative Care, US Department of Veterans Affairs Medical Center and University of California, San Francisco (Dr London); Center for Medication Safety, Pharmacy Benefits Management Services, US Department of Veterans Affairs Medical Center, Hines, Illinois (Dr Hur); Cardiology Section, US Department of Veterans Affairs Medical Center and University of Colorado, Denver (Dr Schwartz); Health Outcomes Program and Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora (Dr Henderson).


JAMA. 2013;309(16):1704-1713. doi:10.1001/jama.2013.4135.
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Published online

Importance The effectiveness of perioperative β-blockade in patients undergoing noncardiac surgery remains controversial.

Objective To determine the associations of early perioperative exposure to β-blockers with 30-day postoperative outcome in patients undergoing noncardiac surgery.

Design, Setting, and Patients A retrospective cohort analysis evaluating exposure to β-blockers on the day of or following major noncardiac surgery among a population-based sample of 136 745 patients who were 1:1 matched on propensity scores (37 805 matched pairs) treated at 104 VA medical centers from January 2005 through August 2010.

Main Outcomes and Measures All cause 30-day mortality and cardiac morbidity (cardiac arrest or Q-wave myocardial infarction).

Results Overall 55 138 patients (40.3%) were exposed to β-blockers. Exposure was higher in the 66.7% of 13 863 patients undergoing vascular surgery (95% CI, 65.9%-67.5%) than in the 37.4% of 122 882 patients undergoing nonvascular surgery (95% CI, 37.1%-37.6%; P < .001). Exposure increased as Revised Cardiac Risk Index factors increased, with 25.3% (95% CI, 24.9%-25.6%) of those with no risk vs 71.3% (95% CI, 69.5%-73.2%) of those with 4 risk factors or more exposed to β-blockers (P < .001). Death occurred among 1.1% (95% CI, 1.1%-1.2%) and cardiac morbidity occurred among 0.9% (95% CI, 0.8%-0.9%) of patients. In the propensity matched cohort, exposure was associated with lower mortality (relative risk [RR], 0.73; 95% CI, 0.65-0.83; P < .001; number need to treat [NNT], 241; 95% CI, 173-397). When stratified by cumulative numbers of Revised Cardiac Risk Index factors, β-blocker exposure was associated with significantly lower mortality among patients with 2 factors (RR, 0.63 [95% CI, 0.50-0.80]; P < .001; NNT, 105 [95% CI, 69-212]), 3 factors (RR, 0.54 [95% CI, 0.39-0.73]; P < .001; NNT, 41 [95% CI, 28-80]), or 4 factors or more (RR, 0.40 [95% CI, 0.25-0.73]; P < .001; NNT, 18 [95% CI, 12-34]). This association was limited to patients undergoing nonvascular surgery. β-Blocker exposure was also associated with a lower rate of nonfatal Q-wave infarction or cardiac arrest (RR, 0.67 [95% CI, 0.57-0.79]; P < .001; NNT, 339 [95% CI, 240-582]), again limited to patients undergoing nonvascular surgery.

Conclusions and Relevance Among propensity-matched patients undergoing noncardiac, nonvascular surgery, perioperative β-blocker exposure was associated with lower rates of 30-day all-cause mortality in patients with 2 or more Revised Cardiac Risk Index factors. Our findings support use of a cumulative number of Revised Cardiac Risk Index predictors in decision making regarding institution and continuation of perioperative β-blockade. A multicenter randomized trial involving patients at a low to intermediate risk by these factors would be of interest to validate these observational findings.

Figures in this Article

The effectiveness and safety of perioperative β-blockade for patients undergoing noncardiac surgery remains controversial.1 Class I recommendations in the current American Heart Association/American College of Cardiology Foundation (AHA/ACCF) Guidelines on Perioperative Evaluation and Care for Noncardiac Surgery remain limited to continuation of preexisting β-blockade.2

Given medical and ethical concerns regarding withdrawal of patients from preexisting β-blockade and the expense of conducting randomized clinical trials powered to determine effects on perioperative outcomes, observational analyses have been used to aid in clinical decision making regarding perioperative β-blockade. However, observational analyses have been largely limited by single-center data or by an inability to capture data from the actual perioperative period. Lindenauer et al,3 using a managed care administrative database, reported a large multicenter analysis of in-hospital perioperative β-blockade, relating exposure to β-blockers during the first 2 days of hospitalization to in-hospital mortality. Using propensity matching and subclassification on the revised Cardiac Risk Index (CRI) predictors, an association of perioperative β-blockade with a lower rate of mortality was demonstrated in patients with 2 or more predictors.4 In those with none or only 1 predictor, treatment was associated with no difference or a higher rate of mortality. However, this analysis was limited by inability to account for outpatient medication use, the type of β-blocker, and uncertainty regarding the date of surgery and has not been corroborated in another large clinical data set.

Recent evidence suggests that use of perioperative β-blockade may be declining.5 Contributing factors may include uncertainty about safety and recent data questioning the efficacy of long-term β blockade in stable outpatients.6,7 Thus, additional multicenter analyses of associations of perioperative β-blockade with outcome are timely and potentially relevant to clinicians and regulatory agencies promoting perioperative quality and safety efforts.

Using system-wide databases in the Veterans Health Administration (VHA), we conducted a propensity-matched analysis using a robust set of clinical predictor, process, medication, and outcome variables to model the association of perioperative β-blockade with all-cause 30-day mortality and cardiac morbidity in a contemporary cohort of patients undergoing major noncardiac surgery.

This study was approved by the VHA's Office of Patient Care Services and all responsible institutional review boards with waiver of informed consent. Race/ethnicity was coded to be white, black, or other using patient self-reported data obtained from the VA Surgical Quality Improvement Program (VASQIP). Perioperative risk, process and outcome data from patients undergoing noncardiac surgical procedures in 7 surgical subspecialties (vascular, general, neurosurgery, orthopedics, thoracic, urology, and otolaryngology) between October 1, 2005, and September 30, 2010, were obtained from the VASQIP database. Data collection methods, variable definitions, and interobserver agreement with abstracted chart data for the VASQIP program have been reported.8,9

Data were linked with outpatient and inpatient pharmacy records for β-blockers and other commonly prescribed cardiovascular medications captured by the VHA Pharmacy Benefits Management-Strategic Healthcare Group (PBM-SHG). Since 1998 the PBM-SHG has maintained a system-wide electronic database of all outpatient prescriptions filled at VHA pharmacies.10 In 2001, an inpatient database was instituted.11 We collected prescription data in 24 VA National Formulary Drug Classes from both of these databases (eTable 1).

We supplemented our data set with International Classification of Diseases, Ninth Revision, Clinical Modification discharge and treatment codes extracted from the VHA Outpatient Care and Patient Treatment Files for inpatient medical admissions administrative databases, within 1 year of each assessed surgical procedure12 (eTable 2). These data provided additional risk variables that we considered covariates in treatment allocation for β-blocker therapy.

Using these databases, we classified the 6 revised Cardiac Risk Index component variables: high-risk surgery, cerebrovascular disease, ischemic heart disease, congestive heart failure, diabetes mellitus, and renal insufficiency3,4 (eTable 3).

Our primary outcome was all-cause mortality and the secondary outcome a composite of Q-wave myocardial infarction (MI) or nonfatal cardiac arrest assessed at 30 days after surgery. We also categorized the incidence of new cerebrovascular accident, given its association with β-blocker administration in the Perioperative Ischemic Evaluation (POISE) study.6

We excluded patients not admitted on the day of, or hospitalized for the day after surgery (defined as postoperative day 0 and 1) and those dying on either of those days (landmark analysis). The data set was truncated at either end to allow comparable periods of preoperative pharmacy and postoperative outcome analysis. We randomly selected 1 surgical procedure per patient. The resulting cohort was then linked with the PBM-SHG and administrative databases using scrambled Social Security numbers. We imputed missing values for preoperative laboratory values and body mass index using multiple imputation.13,14 Imputed values were constrained within plausible ranges. The frequency of missing values for physiologic data ranged from 2.3% to 32.4% (average, 16.9%), whereas for the process variables, it was negligible (1-4 patients). Serum creatinine concentration (missing in 3% of patients) was not imputed (eTable 3). We eliminated 193 patients (0.14%) from the propensity matching process with missing categorical variables used in the regression model (described below).

β-Blocker exposure from the inpatient pharmacy database was defined as any prescription ordered on either postoperative day 0 or 1. Intraoperative or nonelectronically ordered medications are not captured by PBM-SHG and therefore were not considered. Dose and duration of therapy are not currently approved for research use from the inpatient database.

We extracted prescriptions from the outpatient pharmacy database within 90 days of hospital admission, coding indicator variables for availability of medication at several periods (90, 60, 30, and 7 days preoperatively). The proportion of days covered by β-blocker prescription (in the 90-day preoperative window), was used in supplementary analyses.15 We made no attempt to categorize users a priori as acute or long-term given the lack of data on the indications for the prescription. β-Blockers were coded into the following categories: atenolol, carvedilol, metoprolol tartrate, metoprolol succinate, and others. Combination antihypertensives were coded by the β-blocker component. Similar methods were used for drugs ordered after the 2-day exposure period until the time of hospital discharge for descriptive analyses.

Statistical Analysis

To control for treatment-selection bias, we performed a propensity-score matched analysis.16 To avoid immortal time bias, we performed a landmark analysis, eliminating patients who died or sustained 1 of the composite morbidity outcomes during the exposure period.17 A propensity score was estimated using a nonparsimonious logistic regression model (omitting any preoperative β-blocker use variables; eTable 4). To adjust for possible regional differences in β-blocker usage, we included a dummy variable for each of the VHA's 21 geographical regions in which the surgery was performed. β-Blocker exposure was regressed on risk or process variables that we considered related to β-blocker exposure or outcome with a prevalence in the data set of at least 1% (eTable 4).

Propensity-score matching (1:1) was conducted using previously described methods.18 Pairs of β-blocker exposed and nonexposed patients were matched using a greedy matching algorithm with a caliper width of 0.2 standard deviation of the log odds of the estimated propensity score.19 To accommodate subanalyses of the type of surgery (vascular vs nonvascular) and estimated cardiac risk (cumulative number of Revised Cardiac Risk Index predictors), patients were also matched on these factors. Covariate balance between matched pairs for continuous and dichotomous categorical variables was assessed using the standardized difference, with values of less than 10% indicating minimal imbalance.20 The use of interaction terms between covariates to minimize the standardized difference was explored iteratively. The McNemar test was used to compare the frequency of the primary and secondary outcomes between the matched groups.21 Relative risks (RRs) were computed using methods appropriate for paired data and are presented with their 95% CIs.21 Numbers needed to treat (NNT) were calculated as the inverse of the absolute risk reduction estimated in the propensity-score matched sample.

To evaluate associations of long-term and acute β-blocker use with outcome, we performed sensitivity analyses: first, an indicator variable for outpatient β-blocker use within a week of surgery was included in the propensity-score regression; second, the percent of time covered within the 90-day preadmission period and the type of β-blocker were included with appropriate interaction terms. Using our matched data set, we also compared primary and secondary outcome frequencies between patients with new prescriptions within 7 days or 30 days prior to surgery relative to those with existing prescriptions in the 60 or 90-day time periods.

To evaluate associations of the components of the revised Cardiac Risk Index with outcome, we regressed the 6 components, along with the propensity score and β-blocker exposure, on postoperative mortality using the full cohort. Model discrimination and calibration were assessed using the C index and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. We then examined mortality rates between exposed and nonexposed patients using paired t tests for each of the possible 64 combinations of Revised Cardiac Risk Index strata in the matched cohort.

To evaluate associations of potential β-blocker withdrawal, including differences between the 2 most commonly prescribed β-blockers (metoprolol tartrate or metoprolol succinate vs atenolol), we categorized potential patterns of drug use in patients receiving either of these drugs, considering continuation or withdrawal from outpatient therapy during the 2-day exposure period. The association of these patterns with our primary outcome, stratified by vascular vs nonvascular surgery, was evaluated using logistic regression. We also assessed associations between either drug class and the Revised Cardiac Risk Index predictors.

Continuous and categorical variables were compared using parametric or nonparametric methods as appropriate. The Mantel-Haenszel χ2 test was used to test for trends. All P values were 2-tailed and a value of .05 was considered significant. SAS software, version 9.2 (SAS Institute Inc) was used for the analysis.

The initial VASQIP cohort consisted of 334 130 patients. We excluded 197 385 patients following adjustment for surgery on the day of hospital admission (n = 106 097), length of stay criteria (n = 53 046; including 285 patients dying during the exposure period), exclusion of multiple surgeries (n = 16 904), truncation of the data set by the initial 90 and last 30 days (n = 10 119) and combinations of the aforementioned (n = 11 219). Thus, the final cohort consisted of 136 745 patients at 104 VHA hospitals. Selected demographic and clinical characteristics of the study cohort, before and after propensity-score matching, are presented in Table 1; all variables used in the modeling process are presented in eTable 5.

Table Graphic Jump LocationTable 1. Selected Study Cohort Characteristicsa
Analyses of β-Blocker Exposure

Overall, 45 347 patients (33.2%) had an active outpatient prescription for β-blockers within 7 days of surgery and 55 138 patients (40.3%) were potentially exposed to β-blockers on either postoperative day 0 or 1. Compared with nonexposed patients, exposed patients were older and had a higher burden of cardiovascular and related comorbidities; 75.4% had significantly greater use of β-blockers during the 90-day preoperative period (95% CI, 75.0%-75.8%) vs 13.6% of those who were not exposed (95% CI, 13.4%-13.8; P < .001; Table 1 and eTable 5). Of patients exposed on postoperative day 0 or 1, 66.1% had an active prescription covering the week before surgery, whereas only 2.4% of patients had a new prescription issued in that period (Table 2). Inpatient β-blocker exposure was higher in the 66.7% of 13 863 patients who underwent vascular surgery (95% CI, 65.9%-67.5%) than in the 37.4% of 122 882 patients who underwent nonvascular surgery (95% CI, 37.1%-37.6%; P < .001). The rate of use increased with increasing Revised Cardiac Risk Index variables: 25.3% for no factors (95% CI, 24.9%-25.6%) vs 71.3% for 4 or more factors (95% CI, 69.5%-73.2%; P < .001; eFigure 1A). Over the 5 years studied, there was a modest, but significant, trend toward lower β-blocker exposure: 43.5% (95% CI, 42.8%-44.2%) during fiscal-year 2006 and 36.2% (95% CI, 35.7%-36.8%) during fiscal year 2010 (P < .001; eFigure 1B). Metoprolol tartrate was the most commonly prescribed preoperative outpatient β-blocker (37.0% of exposed patients) followed by atenolol (26.9% of exposed patients). Metoprolol tartrate was the most commonly ordered inpatient β-blocker (62.5% of oral prescriptions, 67.2% of intravenous prescriptions) during the exposure period, followed by atenolol (24.4% of oral prescriptions, 0.2% of intravenous prescriptions; Table 2).

Primary and Secondary Outcomes

Overall, 1568 patients (1.1%) sustained the primary 30-day mortality outcome and 1196 patients (0.9%) the secondary cardiac morbidity outcome. Unadjusted primary outcomes occurred in 1.4 % (95% CI, 1.3%-1.5%) of exposed patients vs 1.0% (95% CI, 0.9%-1.1%) of unexposed patients. Primary and secondary outcomes occurred more frequently in patients exposed to β-blockers (mortality in those exposed, 1.4% [95% CI, 1.3%-1.5%] vs those unexposed, 1.0% [95% CI, 0.9%-1.1%], cardiac morbidity of those exposed, 1.2% [95% CI, 1.1%-1.3%] vs those unexposed, 0.7% [95% CI, 0.6%-0.7%]; P < .001 for both) and occurred more frequently in those exposed who underwent vascular surgery than those who underwent nonvascular surgery (vascular surgery mortality, 1.6% [95% CI, 1.4%-1.8%] vs nonvascular surgery, 1.1% [95% CI, 1.0%-1.2%]; vascular surgery cardiac morbidity, 1.6% [95% CI, 1.4%-1.8%] vs nonvascular surgery cardiac morbidity, 0.8% [95% CI, 0.7%-0.8%]; P <.001 for both); and occurred more frequently in patients with increasing number of Revised Cardiac Index Risk predictors (Table 3 and eFigure 2A and B.

Patients sustaining a secondary outcome were at markedly elevated risk of mortality compared with those who did not (571 deaths [47.7%] vs 997 deaths [0.7%]; odds ratio [OR], 123 [95% CI, 108-140]; P < .001). Stratification by β-blocker exposure revealed that exposed patients had a slightly lower, but still markedly elevated, risk of mortality following a secondary outcome (OR, 93 [95% CI, 78-112] vs OR, 161 [95% CI, 133-195]; P < .001 for both), with a similar pattern for patients undergoing vascular surgery or when analyzed by cumulative Revised Cardiac Risk Index factors.

Following 1:1 propensity matching, we obtained 37 805 matched pairs for the primary mortality model, a match rate of 68.5% for exposed patients. Following exclusion of an additional 317 patients from the original cohort who had sustained a secondary outcome during the exposure period and rematching, we obtained 37 662 matched pairs for the secondary cardiac morbidity analysis. The distribution of covariates, β-blocker usage parameters and outcomes between matched pairs in the primary outcome analysis are presented in Table 1 and eTable 5. The groups were well balanced with standardized differences of less than 10% for all continuous and dichotomous categorical variables (eTable 6).

In the matched cohort, a lower rate of mortality was observed in the exposed group (relative risk [RR], 0.73 [95% CI, 0.65-0.83]; P < .001; NNT, 241 [95% CI, 173-397]; Figure 1A and eTable 7). Stratification by revised Cardiac Risk Index variables revealed a lack of association with none or 1 revised Cardiac Risk Index factor. However, significant associations of β-blocker exposure with lower mortality were noted in patients with 2 Revised Cardiac Risk Index factors (RR, 0.63 [95% CI, 0.50-0.80]; P < .001; NNT, 105 [95% CI, 69-212], 3 factors (RR, 0.54 [95% CI, 0.39-0.73]; P < .001; NNT, 41 [95% CI, 28-80]), or 4 factors or more (RR, 0.40 [95% CI, 0.25-0.73]; P < .001; NNT, 18 [95% CI, 12-34]. When stratified by type of surgery, no significant associations were noted in patients undergoing vascular surgery, regardless of the Revised Cardiac Risk Index strata (Figure 1B and eTable 7). The distribution of study covariates between the matched patients in the risk strata with significant associations (eg, 2 or more Revised Cardiac Risk Index factors) revealed similar balance as in the overall cohort, albeit at a higher preoperative risk profile than in those patients with fewer than 2 risk factors (eTable 8).

Place holder to copy figure label and caption
Figure 1. Thirty-Day Mortality Propensity Model
Graphic Jump Location

Considering the secondary cardiac morbidity outcome, β-blocker exposure was associated with a significantly lower rate of cardiac complications (RR, 0.67 [95% CI, 0.57-0.79]; P < .001; NNT, 339 [95% CI, 240-582]; Figure 2A and eTable 9). Stratified by Revised Cardiac Risk Index factors, significant associations of β-blocker exposure to rates of cardiac complications were noted in patients with 2 or 3 Revised Cardiac Risk Index factors only (Figure 2A and eTable 9). In the vascular surgery subgroup, no significant associations were present (Figure 2B and eTable 9).

Place holder to copy figure label and caption
Figure 2. Thirty-Day Cardiac Morbidity Propensity Model
Graphic Jump Location

Substitution of the secondary Revised Cardiac Risk Index definition for diabetes based on treatment with oral hypoglycemic medications, insulin, or both (eTable 3) significantly increased the number of patients coded as having diabetes (23.7% [95% CI, 23.5%-24.0%]) relative to those with diabetes (8.9% [95% CI, 8.7%-9.0%]; P < .001) base on the primary definition. However, it resulted in a similar match rate in the propensity model (37 856 matched pairs) with no differences within any of the strata from the model using the primary definition (eTables 10 and 11).

Sensitivity and Exploratory Medication Analyses

Sensitivity analyses incorporating prehospital admission β-blocker use in the propensity model did not alter the results for mortality (eTable 1214). Post hoc comparisons of the primary and secondary outcomes between patients classified as either acute or chronic users again revealed results consistent with the primary analysis (eTable 15).

Based on absence of reordering of an outpatient preoperative prescription during the exposure period, we detected 8911 patients (6.5%) of the 136 745 patients who had potentially withdrawn from β-blockers. These patients were noted to be at higher risk of mortality than the 36 436 patients who continued treatment (OR, 1.60 [95% CI, 1.34-1.91]; P < .001) and higher than the 72 696 patients with no prior preoperative use or exposure (OR, 2.30 [95% CI, 1.93-2.70]; P < .001). However, when compared with 18 702 patients who were exposed to treatment with no prior use, no significant difference was noted (OR, 1.15 [95% CI 0.95-1.38]; P = .15).

Comparisons of mortality among patients receiving metoprolol (tartrate or succinate) vs atenolol prior to surgery and the effects of their withdrawal are presented in eTable 16. Mortality was significantly lower in the atenolol group (treated or withdrawn) than in the metoprolol group. However, similar ORs for mortality were noted for either drug in patients who stopped taking β-blockers. Patients in whom inpatient metoprolol was substituted for atenolol during the exposure period had a stronger association with mortality relative to the opposite pattern. However, the number of patients in the latter group was trivial (16-36 patients). Associations of withdrawal of metoprolol in patients who had undergone vascular surgery was similar (OR, 1.66 [95% CI, 1.01-2.73]; P < .05), although of lesser significance than patients who did not (OR, 1.78 [95% CI, 1.41-2.23]; P < .001). We were unable to detect a significant association of atenolol withdrawal (OR 1.34 [95% CI, 0.60-3.03]; P < .47) among the 1884 patients who had undergone vascular surgery compared with the 14 420 who had not (OR, 2.19 [95% CI, 1.50-3.19]; P < .001), possibly due to lower sample size.

The distribution of the components of the Revised Cardiac Risk Index for the full and matched cohorts, are presented in Table 1. Their combinations and the corresponding associated mortality rates are presented in eTable 17. Pairwise comparisons of mortality rates in each of the possible 64 combinations of the cumulative Revised Cardiac Risk Index predictors revealed only 7 significant combinations. Given the multiple comparisons involved, none of these would be considered significant (using a threshold of P < .001). Logistic regression models for mortality incorporating the Revised Cardiac Risk Index factors, adjusted for propensity score and β-blocker exposure, using either the entire or matched cohort, revealed similar ORs for each Revised Cardiac Risk Index component (eTable 18). Stratification of metoprolol or atenolol patients by the cerebrovascular, congestive heart failure, and ischemic heart disease components revealed significantly stronger associations of metoprolol over atenolol with each of these components (eTable 19).

Relations With Postoperative Stroke

In unadjusted analyses, postoperative stroke occurred in 415 patients (0.3%; Table 3). Incidence of stroke was significantly higher in patients who were exposed to β-blockers (0.45% [95% CI, 0.39%-0.50%]) than those who were not (0.21% [95% CI, 0.17%-0.24%]; P < .001). However, the overall stroke incidence among the full cohort did not differ significantly by exposure in the matched cohort (exposed, 0.35% [95% CI, 0.29%-0.41%] vs unexposed, 0.32% [95% CI, 0.26%-0.38%]; P = .45), nor among patients with the strongest association of exposure with outcome (those with a Revised Cardiac Risk Index score ≥ 2; exposed vs unexposed, 0.6%; P = .70; eTable 8). Although patients who had undergone vascular surgery had a higher incidence of stroke (1.05%; 95% CI, 0.88%-1.22%) than did patients who had not (0.22% [95% CI, 0.19%-0.25%]; P < .001), there were no significant differences in stroke incidence noted between patients exposed (1.0% [95% CI, 0.70%-1.32%]) vs those unexposed (1.1% [95% CI, 0.79%-1.44%]; P = .74) or among matched patients who had undergone vascular surgery (0.28% [95% CI, 0.22%-0.33%]) or those who had not (0.22% [95% CI, 0.17%-0.28%]; P = .22) of the matched cohort. We noted significant associations of drug group with stroke (atenolol vs metoprolol, OR, 0.63 [95% CI, 0.45-0.89]; P = .008; eTable 20).

We report associations of early perioperative β-blocker exposure with 30-day all-cause mortality and cardiac morbidity in patients undergoing noncardiac surgery in the VHA health care system. Our analyses suggest that such exposure is significantly associated with a lower rate of all-cause mortality, with the strongest association in those undergoing nonvascular surgery and in those with 2 or more Revised Cardiac Risk Index factors. We also noted a significant association with a lower rate of cardiac arrest and Q-wave MI. Although the latter complications occurred infrequently, they are highly predictive of subsequent mortality.22,23 The incidence of Q-wave MI in the current analysis is lower than the risk of MI reported in other studies in which elevation of cardiac biomarkers or less specific electrocardiographic patterns were used to define perioperative MI.24,25

We were unable to detect significant associations between β-blocker exposure and outcome in patients undergoing vascular surgery. Although considered at greatest risk of cardiovascular complications, prior analyses in this population have reported variable associations.1,26,27 However, a smaller sample size relative to the nonvascular cohort and the possibility that these patients received medication not captured by PBM-SHG (eg, those administered intraoperatively or ordered nonelectronically) or received a higher level of care (eg, ICU utilization), a factor not captured by VASQIP, must be considered.

The rate of β-blocker exposure over the period of this analysis significantly declined from 43.5% to 36.2%. This may be related to the findings of the POISE trial of increased stroke and death in treated patients, leading to more conservative guideline recommendations within this period.2,6 Similar findings have been recently reported by Canadian investigators during this timeframe.5

We were unable to demonstrate significant differences between our post hoc definitions of long-term use or acute institution of β-blockade. Limited evidence suggests that long-term use of β-blockade is associated with lower rates of adverse outcome than institution within 1 week of or immediately after surgery.28,29 Our data suggest that institution of β-blockade within 30 days is associated with similar outcome rates as longer periods. However, our analysis of associations within 7 days of surgery is limited by the very low incidence observed (2.4%).

We noted associations of increased rates of mortality with β-blocker withdrawal in the exposure period. This association, corresponding to approximately 2-fold increased risk, is similar to that reported by other investigators.3032

Controversy exists over the efficacy and safety of specific β-blockers used for perioperative β-blockade. Agents with greater β-1 specificity (eg, atenolol or bisoprolol) have been associated with improved outcome relative to more widely used formulations of metoprolol.33 Our exploratory analyses suggest stronger associations with mortality among patients treated with metoprolol than with atenolol. However, we also found that patients taking metoprolol were significantly more likely to have ischemic heart disease or congestive heart failure, consistent with existing AHA/ACCF guideline indications for therapy.34,35

Similar to other investigators, we noted significant interaction of perioperative β-blockade and the number of Revised Cardiac Risk Index predictors on outcome rates, with the difference in event rates between patients who were exposed and those were not exposed, being most prominent among patients with the largest number of Revised Cardiac Risk Index predictors.3,32 However, we were not able to demonstrate significant interaction of perioperative β-blockade with any specific combination of these variables. We used all 6 revised Cardiac Risk Index predictors, including history of congestive heart failure, a major risk factor for perioperative mortality.36 Although decompensated heart failure remains an absolute contraindication to perioperative β-blockade, long-term use for compensated heart failure has a class I indication for therapy and therefore continuation in the perioperative period.2,34

We were unable to demonstrate significant associations of perioperative β-blockade with the risk of postoperative stroke. This contrasts with an increased risk observed in the POISE trial.6 Our overall incidence (0.3%) was lower than that of the highly selected patients enrolled in that trial (0.7%). Although the overall stroke rates in our vascular cohort are similar to the treated patients in POISE (1% in both), no differences between our exposed and unexposed patients were noted. Analysis of existing randomized trials suggests that either timing of initiation and or dose of β-blocker are important covariates.37 Our data capture did not allow us to address dose parameters. Our finding of a higher incidence of stroke in patients treated with metoprolol may be related to the fact that metoprolol users had a significantly higher incidence of preexisting cerebrovascular disease relative to atenolol users, potentially predisposing them to perioperative injury.38

In conclusion, our results suggest that early perioperative β-blocker exposure is associated with significantly lower rates of 30-day mortality and cardiac morbidity in patients at elevated baseline cardiac risk undergoing nonvascular surgery. Although assessment of cumulative number of Revised Cardiac Risk Index predictors might be helpful to clinicians in deciding whether to use perioperative β-blockade, the current findings highlight a need for a randomized multicenter trial of perioperative β-blockade in low- to intermediate-risk patients scheduled for noncardiac surgery.

Corresponding Author: Martin J. London, MD, Department of Anesthesia and Perioperative Care, San Francisco VA Medical Center, Anesthesia (129), 4150 Clement St, San Francisco, CA 94121 (londonm@anesthesia.ucsf.edu).

Author Contributions: Dr London had full access to all of 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: All authors.

Acquisition of data: London, Henderson.

Analysis and interpretation of the data: All authors.

Drafting of the manuscript: London.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: London, Hur, Henderson.

Obtained funding: London.

Administrative, technical or material support: London.

Study Supervision: London.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Schwartz reported that institutional grants are pending from Roche, Resverlogix, Anthera, and Sanofi. Dr London reported receiving an unrestricted grant from the Anesthesia Patient Safety Foundation. No other conflicts of interest were reported.

Funding/Support: This study was supported by a grant from the Anesthesia Patient Safety Foundation.

Role of the Sponsor: The Anesthesia Patient Safety Foundation had no role in the design and conduct of the study, in the collection, management, analysis and data interpretation, or in the preparation, review or approval of the manuscript. The VASQIP database was provided by VASQIP who collected the data and reviewed and approved the manuscript prior to submission.

Disclaimer: The opinions expressed are those of the authors and not necessarily those of the Department of Veterans Affairs or the United States Government.

Additional Contributions: We thank Francesca Cunningham, PharmD, Director of the Department of Veterans Affairs Center for Medication Safety (VAMedSAFE) and Program Manager for Pharmacoepidemiologic and Outcomes Research for VHA-PBM who provided constructive input throughout the design and conduct of this analysis. Dr Cunningham received no financial compensation from this project. We also thank the VA Surgical Quality Data Use Group (SQDUG) for its role as scientific advisors and for the critical review of data use and analysis presented in this article.

This article was corrected for errors on April 23, 2013.

Bangalore S, Wetterslev J, Pranesh S, Sawhney S, Gluud C, Messerli FH. Perioperative beta blockers in patients having non-cardiac surgery: a meta-analysis.  Lancet. 2008;372(9654):1962-1976
PubMed   |  Link to Article
Fleisher LA, Beckman JA, Brown KA,  et al.  2009 ACCF/AHA focused update on perioperative beta blockade incorporated into the ACC/AHA 2007 guidelines on perioperative cardiovascular evaluation and care for noncardiac surgery: a report of the American college of cardiology foundation/American heart association task force on practice guidelines.  Circulation. 2009;120(21):e169-e276
PubMed   |  Link to Article
Lindenauer PK, Pekow P, Wang K, Mamidi DK, Gutierrez B, Benjamin EM. Perioperative beta-blocker therapy and mortality after major noncardiac surgery.  N Engl J Med. 2005;353(4):349-361
PubMed   |  Link to Article
Lee TH, Marcantonio ER, Mangione CM,  et al.  Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery.  Circulation. 1999;100(10):1043-1049
PubMed   |  Link to Article
Wijeysundera DN, Mamdani M, Laupacis A,  et al.  Clinical evidence, practice guidelines, and β-blocker utilization before major noncardiac surgery.  Circ Cardiovasc Qual Outcomes. 2012;5(4):558-565
PubMed   |  Link to Article
Devereaux PJ, Yang H, Yusuf S,  et al; POISE Study Group.  Effects of extended-release metoprolol succinate in patients undergoing non-cardiac surgery (POISE trial): a randomised controlled trial.  Lancet. 2008;371(9627):1839-1847
PubMed   |  Link to Article
Bangalore S, Steg G, Deedwania P,  et al; REACH Registry Investigators.  β-Blocker use and clinical outcomes in stable outpatients with and without coronary artery disease.  JAMA. 2012;308(13):1340-1349
PubMed   |  Link to Article
Khuri SF, Daley J, Henderson W,  et al; National VA Surgical Quality Improvement Program.  The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care.  Ann Surg. 1998;228(4):491-507
PubMed   |  Link to Article
Davis CL, Pierce JR, Henderson W,  et al.  Assessment of the reliability of data collected for the Department of Veterans Affairs national surgical quality improvement program.  J Am Coll Surg. 2007;204(4):550-560
PubMed   |  Link to Article
Sales MM, Cunningham FE, Glassman PA, Valentino MA, Good CB. Pharmacy benefits management in the Veterans Health Administration: 1995 to 2003.  Am J Manag Care. 2005;11(2):104-112
PubMed
Smith MW, Joseph GJ. Pharmacy data in the VA health care system.  Med Care Res Rev. 2003;60(3):(suppl)  92S-123S
PubMed   |  Link to Article
Maynard C, Chapko MK. Data resources in the Department of Veterans Affairs.  Diabetes Care. 2004;27:(suppl 2)  B22-B26
PubMed   |  Link to Article
Wu WC, Schifftner TL, Henderson WG,  et al.  Preoperative hematocrit levels and postoperative outcomes in older patients undergoing noncardiac surgery.  JAMA. 2007;297(22):2481-2488
PubMed   |  Link to Article
Hamilton BH, Ko CY, Richards K, Hall BL. Missing data in the American College of Surgeons National Surgical Quality Improvement Program are not missing at random: implications and potential impact on quality assessments.  J Am Coll Surg. 2010;210(2):125-139
PubMed   |  Link to Article
Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications.  J Clin Epidemiol. 1997;50(1):105-116
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
Suissa S. Immortal time bias in pharmaco-epidemiology.  Am J Epidemiol. 2008;167(4):492-499
PubMed   |  Link to Article
Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement.  J Thorac Cardiovasc Surg. 2007;134(5):1128-1135
PubMed   |  Link to Article
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.  Pharm Stat. 2011;10(2):150-161
PubMed   |  Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
PubMed   |  Link to Article
Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data.  Stat Med. 2004;23(1):65-75
PubMed   |  Link to Article
Hamel MB, Henderson WG, Khuri SF, Daley J. Surgical outcomes for patients aged 80 and older: morbidity and mortality from major noncardiac surgery.  J Am Geriatr Soc. 2005;53(3):424-429
PubMed   |  Link to Article
Davenport DL, Ferraris VA, Hosokawa P, Henderson WG, Khuri SF, Mentzer RM Jr. Multivariable predictors of postoperative cardiac adverse events after general and vascular surgery: results from the patient safety in surgery study.  J Am Coll Surg. 2007;204(6):1199-1210
PubMed   |  Link to Article
Levy M, Heels-Ansdell D, Hiralal R,  et al.  Prognostic value of troponin and creatine kinase muscle and brain isoenzyme measurement after noncardiac surgery: a systematic review and meta-analysis.  Anesthesiology. 2011;114(4):796-806
PubMed   |  Link to Article
Devereaux PJ, Chan MT, Alonso-Coello P,  et al; Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators.  Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery.  JAMA. 2012;307(21):2295-2304
PubMed   |  Link to Article
Poldermans D, Bax JJ, Schouten O,  et al; Dutch Echocardiographic Cardiac Risk Evaluation Applying Stress Echo Study Group.  Should major vascular surgery be delayed because of preoperative cardiac testing in intermediate-risk patients receiving beta-blocker therapy with tight heart rate control?  J Am Coll Cardiol. 2006;48(5):964-969
PubMed   |  Link to Article
Bertges DJ, Goodney PP, Zhao Y,  et al; Vascular Study Group of New England.  The Vascular Study Group of New England Cardiac Risk Index (VSG-CRI) predicts cardiac complications more accurately than the Revised Cardiac Risk Index in vascular surgery patients.  J Vasc Surg. 2010;52(3):674-683, 683, e1-e683, e3
PubMed   |  Link to Article
Flu WJ, van Kuijk JP, Chonchol M,  et al.  Timing of pre-operative Beta-blocker treatment in vascular surgery patients: influence on post-operative outcome.  J Am Coll Cardiol. 2010;56(23):1922-1929
PubMed   |  Link to Article
Ellenberger C, Tait G, Beattie WS. Chronic β blockade is associated with a better outcome after elective noncardiac surgery than acute β blockade: a single-center propensity-matched cohort study.  Anesthesiology. 2011;114(4):817-823
PubMed   |  Link to Article
van Klei WA, Bryson GL, Yang H, Forster AJ. Effect of beta-blocker prescription on the incidence of postoperative myocardial infarction after hip and knee arthroplasty.  Anesthesiology. 2009;111(4):717-724
PubMed   |  Link to Article
Wallace AW, Au S, Cason BA. Association of the pattern of use of perioperative β-blockade and postoperative mortality.  Anesthesiology. 2010;113(4):794-805
PubMed   |  Link to Article
Kwon S, Thompson R, Florence M,  et al; Surgical Care and Outcomes Assessment Program (SCOAP) Collaborative.  β-Blocker continuation after noncardiac surgery: a report from the surgical care and outcomes assessment program.  Arch Surg. 2012;147(5):467-473
PubMed   |  Link to Article
Badgett RG, Lawrence VA, Cohn SL. Variations in pharmacology of beta-blockers may contribute to heterogeneous results in trials of perioperative beta-blockade.  Anesthesiology. 2010;113(3):585-592
PubMed
Jessup M, Abraham WT, Casey DE,  et al.  2009 focused update: ACCF/AHA Guidelines for the Diagnosis and Management of Heart Failure in Adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation.  Circulation. 2009;119(14):1977-2016
PubMed   |  Link to Article
Smith SC Jr, Benjamin EJ, Bonow RO,  et al; World Heart Federation and the Preventive Cardiovascular Nurses Association.  AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and other Atherosclerotic Vascular Disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation.  Circulation. 2011;124(22):2458-2473
PubMed   |  Link to Article
Hammill BG, Curtis LH, Bennett-Guerrero E,  et al.  Impact of heart failure on patients undergoing major noncardiac surgery.  Anesthesiology. 2008;108(4):559-567
PubMed   |  Link to Article
Poldermans D, Schouten O, van Lier F,  et al.  Perioperative strokes and beta-blockade.  Anesthesiology. 2009;111(5):940-945
PubMed   |  Link to Article
Bateman BT, Schumacher HC, Wang S, Shaefi S, Berman MF. Perioperative acute ischemic stroke in noncardiac and nonvascular surgery: incidence, risk factors, and outcomes.  Anesthesiology. 2009;110(2):231-238
PubMed

Figures

Place holder to copy figure label and caption
Figure 1. Thirty-Day Mortality Propensity Model
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. Thirty-Day Cardiac Morbidity Propensity Model
Graphic Jump Location

References

Bangalore S, Wetterslev J, Pranesh S, Sawhney S, Gluud C, Messerli FH. Perioperative beta blockers in patients having non-cardiac surgery: a meta-analysis.  Lancet. 2008;372(9654):1962-1976
PubMed   |  Link to Article
Fleisher LA, Beckman JA, Brown KA,  et al.  2009 ACCF/AHA focused update on perioperative beta blockade incorporated into the ACC/AHA 2007 guidelines on perioperative cardiovascular evaluation and care for noncardiac surgery: a report of the American college of cardiology foundation/American heart association task force on practice guidelines.  Circulation. 2009;120(21):e169-e276
PubMed   |  Link to Article
Lindenauer PK, Pekow P, Wang K, Mamidi DK, Gutierrez B, Benjamin EM. Perioperative beta-blocker therapy and mortality after major noncardiac surgery.  N Engl J Med. 2005;353(4):349-361
PubMed   |  Link to Article
Lee TH, Marcantonio ER, Mangione CM,  et al.  Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery.  Circulation. 1999;100(10):1043-1049
PubMed   |  Link to Article
Wijeysundera DN, Mamdani M, Laupacis A,  et al.  Clinical evidence, practice guidelines, and β-blocker utilization before major noncardiac surgery.  Circ Cardiovasc Qual Outcomes. 2012;5(4):558-565
PubMed   |  Link to Article
Devereaux PJ, Yang H, Yusuf S,  et al; POISE Study Group.  Effects of extended-release metoprolol succinate in patients undergoing non-cardiac surgery (POISE trial): a randomised controlled trial.  Lancet. 2008;371(9627):1839-1847
PubMed   |  Link to Article
Bangalore S, Steg G, Deedwania P,  et al; REACH Registry Investigators.  β-Blocker use and clinical outcomes in stable outpatients with and without coronary artery disease.  JAMA. 2012;308(13):1340-1349
PubMed   |  Link to Article
Khuri SF, Daley J, Henderson W,  et al; National VA Surgical Quality Improvement Program.  The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care.  Ann Surg. 1998;228(4):491-507
PubMed   |  Link to Article
Davis CL, Pierce JR, Henderson W,  et al.  Assessment of the reliability of data collected for the Department of Veterans Affairs national surgical quality improvement program.  J Am Coll Surg. 2007;204(4):550-560
PubMed   |  Link to Article
Sales MM, Cunningham FE, Glassman PA, Valentino MA, Good CB. Pharmacy benefits management in the Veterans Health Administration: 1995 to 2003.  Am J Manag Care. 2005;11(2):104-112
PubMed
Smith MW, Joseph GJ. Pharmacy data in the VA health care system.  Med Care Res Rev. 2003;60(3):(suppl)  92S-123S
PubMed   |  Link to Article
Maynard C, Chapko MK. Data resources in the Department of Veterans Affairs.  Diabetes Care. 2004;27:(suppl 2)  B22-B26
PubMed   |  Link to Article
Wu WC, Schifftner TL, Henderson WG,  et al.  Preoperative hematocrit levels and postoperative outcomes in older patients undergoing noncardiac surgery.  JAMA. 2007;297(22):2481-2488
PubMed   |  Link to Article
Hamilton BH, Ko CY, Richards K, Hall BL. Missing data in the American College of Surgeons National Surgical Quality Improvement Program are not missing at random: implications and potential impact on quality assessments.  J Am Coll Surg. 2010;210(2):125-139
PubMed   |  Link to Article
Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications.  J Clin Epidemiol. 1997;50(1):105-116
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
Suissa S. Immortal time bias in pharmaco-epidemiology.  Am J Epidemiol. 2008;167(4):492-499
PubMed   |  Link to Article
Austin PC. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: a systematic review and suggestions for improvement.  J Thorac Cardiovasc Surg. 2007;134(5):1128-1135
PubMed   |  Link to Article
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.  Pharm Stat. 2011;10(2):150-161
PubMed   |  Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
PubMed   |  Link to Article
Agresti A, Min Y. Effects and non-effects of paired identical observations in comparing proportions with binary matched-pairs data.  Stat Med. 2004;23(1):65-75
PubMed   |  Link to Article
Hamel MB, Henderson WG, Khuri SF, Daley J. Surgical outcomes for patients aged 80 and older: morbidity and mortality from major noncardiac surgery.  J Am Geriatr Soc. 2005;53(3):424-429
PubMed   |  Link to Article
Davenport DL, Ferraris VA, Hosokawa P, Henderson WG, Khuri SF, Mentzer RM Jr. Multivariable predictors of postoperative cardiac adverse events after general and vascular surgery: results from the patient safety in surgery study.  J Am Coll Surg. 2007;204(6):1199-1210
PubMed   |  Link to Article
Levy M, Heels-Ansdell D, Hiralal R,  et al.  Prognostic value of troponin and creatine kinase muscle and brain isoenzyme measurement after noncardiac surgery: a systematic review and meta-analysis.  Anesthesiology. 2011;114(4):796-806
PubMed   |  Link to Article
Devereaux PJ, Chan MT, Alonso-Coello P,  et al; Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators.  Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery.  JAMA. 2012;307(21):2295-2304
PubMed   |  Link to Article
Poldermans D, Bax JJ, Schouten O,  et al; Dutch Echocardiographic Cardiac Risk Evaluation Applying Stress Echo Study Group.  Should major vascular surgery be delayed because of preoperative cardiac testing in intermediate-risk patients receiving beta-blocker therapy with tight heart rate control?  J Am Coll Cardiol. 2006;48(5):964-969
PubMed   |  Link to Article
Bertges DJ, Goodney PP, Zhao Y,  et al; Vascular Study Group of New England.  The Vascular Study Group of New England Cardiac Risk Index (VSG-CRI) predicts cardiac complications more accurately than the Revised Cardiac Risk Index in vascular surgery patients.  J Vasc Surg. 2010;52(3):674-683, 683, e1-e683, e3
PubMed   |  Link to Article
Flu WJ, van Kuijk JP, Chonchol M,  et al.  Timing of pre-operative Beta-blocker treatment in vascular surgery patients: influence on post-operative outcome.  J Am Coll Cardiol. 2010;56(23):1922-1929
PubMed   |  Link to Article
Ellenberger C, Tait G, Beattie WS. Chronic β blockade is associated with a better outcome after elective noncardiac surgery than acute β blockade: a single-center propensity-matched cohort study.  Anesthesiology. 2011;114(4):817-823
PubMed   |  Link to Article
van Klei WA, Bryson GL, Yang H, Forster AJ. Effect of beta-blocker prescription on the incidence of postoperative myocardial infarction after hip and knee arthroplasty.  Anesthesiology. 2009;111(4):717-724
PubMed   |  Link to Article
Wallace AW, Au S, Cason BA. Association of the pattern of use of perioperative β-blockade and postoperative mortality.  Anesthesiology. 2010;113(4):794-805
PubMed   |  Link to Article
Kwon S, Thompson R, Florence M,  et al; Surgical Care and Outcomes Assessment Program (SCOAP) Collaborative.  β-Blocker continuation after noncardiac surgery: a report from the surgical care and outcomes assessment program.  Arch Surg. 2012;147(5):467-473
PubMed   |  Link to Article
Badgett RG, Lawrence VA, Cohn SL. Variations in pharmacology of beta-blockers may contribute to heterogeneous results in trials of perioperative beta-blockade.  Anesthesiology. 2010;113(3):585-592
PubMed
Jessup M, Abraham WT, Casey DE,  et al.  2009 focused update: ACCF/AHA Guidelines for the Diagnosis and Management of Heart Failure in Adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation.  Circulation. 2009;119(14):1977-2016
PubMed   |  Link to Article
Smith SC Jr, Benjamin EJ, Bonow RO,  et al; World Heart Federation and the Preventive Cardiovascular Nurses Association.  AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and other Atherosclerotic Vascular Disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation.  Circulation. 2011;124(22):2458-2473
PubMed   |  Link to Article
Hammill BG, Curtis LH, Bennett-Guerrero E,  et al.  Impact of heart failure on patients undergoing major noncardiac surgery.  Anesthesiology. 2008;108(4):559-567
PubMed   |  Link to Article
Poldermans D, Schouten O, van Lier F,  et al.  Perioperative strokes and beta-blockade.  Anesthesiology. 2009;111(5):940-945
PubMed   |  Link to Article
Bateman BT, Schumacher HC, Wang S, Shaefi S, Berman MF. Perioperative acute ischemic stroke in noncardiac and nonvascular surgery: incidence, risk factors, and outcomes.  Anesthesiology. 2009;110(2):231-238
PubMed
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Supplemental Content

London MJ, Hur K, Schwartz GG, Henderson WG. Association of Perioperative ß-Blockade With Mortality and Cardiovascular Morbidity Following Major Noncardiac Surgery. JAMA. DOI:10.1001/jama.2013.4135.

eTable 1. VA National Formulary Drug Classes extracted from PBM-SHG database

eTable 2. ICD-9 variables extracted from OPC/PTF administrative databases

eTable 3. Coding of the Revised Cardiac Risk Index

eTable 4. Variables used in the Propensity Model

eTable 5. Clinical Characteristics of the Study Cohorts (all variables)

eTable 6. Standardized differences of the Variables in the Propensity Model

eTable 7 . Mortality Matched pairs analysis stratified by type of surgery and RCRI primary model No Preoperative ß-Blocker Variables

eTable 8 . Matched study cohort characteristics RCRI greater than or equal to 2

eTable 9. Morbidity Matched pairs analysis stratified by type of surgery and RCRI primary model No Preoperative ß-Blocker Variables

eTable 10. Mortality Matched pairs analysis stratified by type of surgery and RCRI primary model No Preoperative ß-Blocker Variables; Secondary Diabetes Definition

eTable 11. Morbidity Matched pairs analysis stratified by type of surgery and RCRI primary model No Preoperative ß-Blocker Variables; Secondary Diabetes Definition

eTable 12. Distribution of beta blocker usage parameters by propensity models

eTable 13. Mortality Matched pairs analysis stratified by type of surgery and RCRI 7 day ß-Blocker indicator model

eTable 14. Mortality Matched pairs analysis stratified by type of surgery and RCRI 90 day ß-Blocker variable model

eTable 15. Morbidity and Mortality comparisons for patients classified posthoc as chronic or acute beta blocker users

eTable 16. Comparisons of mortality between metoprolol and atenolol users

eTable 17. Pairwise comparisons mortality among strata of cumulative RCRI predictors

eTable 18. RCRI predictors logistic regression model

eTable 19. Associations of Metoprolol or Atenolol use with CVD, CHF and IHD RCRI predictors

eTable 20. Associations of Metoprolol or Atenolol use with Postoperative Stroke

eFigure 1A and B. ß-Blocker exposure by cumulative RCRI variables and fiscal year

eFigure 2A and B. 30-day Study Outcomes by cumulative RCRI predictors and fiscal year

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