Variation in transfusion rates within a single institution: exploring the effect of differing practice patterns on the likelihood of blood product transfusion in patients undergoing cardiac surgery

J Thorac Cardiovasc Surg. 2015 Jan;149(1):297-302. doi: 10.1016/j.jtcvs.2014.09.004. Epub 2014 Sep 16.

Abstract

Objectives: Rates of perioperative transfusion vary widely among patients undergoing cardiac surgery. Few studies have examined factors beyond the clinical characteristics of the patients that may be responsible for such variation. The purpose of this study was to determine whether differing practice patterns had an impact on variation in perioperative transfusion at a single center.

Methods: Patients who underwent cardiac surgery at a single center between 2004 and 2011 were considered. Comparisons were made between patients who had received a perioperative transfusion and those who had not from the clinical factors at baseline, intraoperative variables, and differing practice patterns, as defined by the surgeon, anesthesiologist, perfusionist, and the year in which the procedure was performed. The risk-adjusted effect of these factors on perioperative transfusion rates was determined using multivariable regression modeling techniques.

Results: The study population comprised 4823 patients, of whom 1929 (40.0%) received a perioperative transfusion. Significant variation in perioperative transfusion rates was noted between surgeons (from 32.4% to 51.5%, P < .0001), anesthesiologists (from 34.4% to 51.9%, P < .0001) and across year (from 28.2% in 2004 to 48.8% in 2008, P < .0001). After adjustment for baseline and intraoperative variables, surgeon, anesthesiologist, and year of procedure were each found to be independent predictors of perioperative transfusion.

Conclusions: Differing practice patterns contribute to significant variation in rates of perioperative transfusion within a single center. Strategies aimed at reducing overall transfusion rates must take into account such variability in practice patterns and account for nonclinical factors as well as known clinical predictors of blood transfusions.

MeSH terms

  • Aged
  • Blood Loss, Surgical / prevention & control*
  • Blood Transfusion / trends*
  • Cardiac Surgical Procedures / adverse effects
  • Cardiac Surgical Procedures / trends*
  • Chi-Square Distribution
  • Female
  • Humans
  • Logistic Models
  • Male
  • Multivariate Analysis
  • New Brunswick
  • Odds Ratio
  • Practice Patterns, Physicians' / trends*
  • Risk Factors
  • Time Factors
  • Transfusion Reaction
  • Treatment Outcome