Correspondence  |   May 2011
Perioperative β-Blockade Protocol Unproven
Author Affiliations & Notes
  • Arthur W. Wallace, M.D., Ph.D.
  • *University of California, San Francisco, San Francisco, California. ; ;
Article Information
Correspondence   |   May 2011
Perioperative β-Blockade Protocol Unproven
Anesthesiology 5 2011, Vol.114, 1248-1249. doi:10.1097/ALN.0b013e318213c466
Anesthesiology 5 2011, Vol.114, 1248-1249. doi:10.1097/ALN.0b013e318213c466
In Reply:
van Klei et al.  in their letter to the editor asked questions about statistical techniques used in our publication, “Association of the Pattern of Use of Perioperative β-Blockade and Postoperative Mortality,”1 to which we respond. The logistic regression model described in table 5 of the article describes the overall regression model in the high-risk patient group based on Perioperative Cardiac Risk Reduction Therapy criteria, including presence of coronary artery disease, peripheral vascular disease, or two risk factors (diabetes, hypertension, age older than 60 yr, smoking, or hyperlipidemia). As described in the Methods section, all risk factors such as preexisting medical conditions (age, sex, presence of known coronary artery disease, presence of known vascular disease, diabetes, hypertension, smoking, hypercholesterolemia, class of operation, medication use, revised cardiac risk index) were included in the model. However, only those chronic health risk factors that were significant and predictive of increased mortality were reported in table 5. Age is included as a significant risk factor. At the request of van Klei et al.  , we offer an expansion of table 5 from our article,1 including nonsignificant risk factors; see table 1for this information.
Table 1.  Logistic Regression Model for Perioperative β-Blockade for 30-Day and 1-yr Mortality
Image not available
Table 1.  Logistic Regression Model for Perioperative β-Blockade for 30-Day and 1-yr Mortality
We were asked to clarify our methods of propensity matching, and we are pleased to do so. We used the risk factors of the original logistic regression model, including all nonsignificant risk factors, to develop propensity scores to predict the outcome: pattern of perioperative β-blockade =none  . Members of the other three groups were matched to the none  group. An exact matching strategy was used, based on 12-digit propensity scores and allowing for more than one match per patient in the none  group. Differences in outcomes-based β-blocker use pattern were determined by logistic regression.
As we discussed in the Limitations section of our manuscript, and as van Klei et al.  emphasize, changes in practice patterns over time may have affected outcomes in our study, including unmeasured factors such as use of clonidine, statins, aspirin, minimally invasive surgery, off-pump coronary artery bypass graft, accuracy of administrative records, and other practice pattern shifts. We have performed a sensitivity analysis that forces “time” (number of years since 1996) into the multivariable analysis as a proxy for change in practice patterns (which, of course, includes changes in perioperative β-blockade practices). Inclusion of “time” in this manner has no effect on the analysis of the beneficial effects of perioperative β-blockade addition  or the deleterious effects of withdrawal  . When “time” is included in this manner, the effect of continuous  perioperative β-blockade becomes nonsignificant. However, this apparent effect is probably spurious, because continuous  perioperative β-blockade covaries with time and has increased significantly over the period of study. Because of this interaction, “time” is not included in our model.
The effect of hemoglobin on mortality is complex, with studies associating hematocrit values greater than 26% with an increased mortality;2 hematocrit values were not included in the study. We agree that it would have been interesting to know whether hemoglobin or other laboratory results affected outcomes in this study. Hemoglobin and hematocrit values were not extracted from the medical records in our study and are not available to be included in the analysis. This limitation was discussed in the text of the article.
Dr. David R. Vaughn in his letter concludes that the evidence for a particular protocol for perioperative β-blockade cannot be supported and should not be mandated. Knowledge in the field of perioperative medicine is still evolving. However, there are approximately 100 clinical studies in the medical literature that demonstrate the reduction in mortality with β-blockers in various clinical conditions such as postmyocardial infarction,3,4 congestive heart failure,5 and during perioperative care.6–8 Furthermore, β-blocker withdrawal has been known to be detrimental since 1973 in patients with coronary artery disease. Perioperative administration of β-blockers to patients who had preexisting β-blocker prescriptions before surgery has been a level 1 standard of care since 1996.9 Despite this knowledge, perioperative withdrawal of β-blocker therapy is surprisingly common.1 
In the current study, withdrawal of β-blockers is associated with an increase in 30-day (odds ratio 3.93, 95% CI 2.57–6.01, P  < 0.0001) and 1-yr mortality (odds ratio 1.96, 95% CI 1.49–2.58, P  < 0.0001).1 How long should we wait for the average medical center to eliminate perioperative β-blockade withdrawal, a practice that increases the risk of patient death by 400%? Performance measures are designed to speed the adoption of level 1 standards of care. Instituting a performance measure to eliminate inappropriate withdrawal of perioperative β-blockade is good medicine.
Dr. Vaughn also points out that we used definitions of Continuous  or Addition  patterns of β-blockade that might not be universally agreed upon. Specifically, our definitions for Addition  or Continuous  β-blockade required as little as one dose of β-blocker medication after surgery. These definitions, as stated in the text, were designed to set clear inclusion criteria for group membership and not to suggest that use of one dose of β-blockade is an appropriate mode of therapy. Increased duration of therapy increases efficacy. The definitions used enabled us to use intention-to-treat analysis to determine efficacy. The simple fact that we were able to demonstrate efficacy with these minimal definitions of perioperative β-blocker therapy clearly demonstrates profound efficacy.
Appropriate perioperative β-blockade in at-risk patients reduces the risk of perioperative mortality by almost 50%, at what appears to be an extremely low cost to the health care system. We agree with Dr. Vaughn's suggestion that the existing guidelines and performance measures need continuous refinement. Systems to ensure appropriate perioperative β-blockade, including pharmacy reconciliation, practice guidelines, and clinical reminders both to start and maintain therapy, must be implemented. But we must ask, how long should a patient wait for the average doctor to adhere to a level 1 standard of care that significantly reduces mortality?
*University of California, San Francisco, San Francisco, California. ; ;
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Table 1.  Logistic Regression Model for Perioperative β-Blockade for 30-Day and 1-yr Mortality
Image not available
Table 1.  Logistic Regression Model for Perioperative β-Blockade for 30-Day and 1-yr Mortality