title: A multivariable model for predicting the need for blood transfusion in patients undergoing first-time elective coronary bypass graft surgery.
abstract: BACKGROUND
  The incidence of blood transfusion in coronary artery bypass graft (CABG) surgery remains high. Preoperative identification of those at high risk for requiring blood will allow for the cost-effective use of some blood conservation modalities. Multivariable analysis techniques were used in this study to develop a prediction rule for such a purpose.
  
  
  METHODS
  Data were prospectively collected for all patients undergoing elective first-time CABG surgery from January 1997 to September 1998 at a tertiary-care teaching hospital (n = 1007). The prediction rule was developed on the first two-thirds of the sample by using logistic regression methods to examine the relationship of patient demographics, comorbidities, and preoperative Hb with perioperative blood transfusion. The remaining one-third of the sample was used to validate the rule.
  
  
  RESULTS
  The transfusion rate was 29.4 percent. The prediction rule included preoperative Hb (g/dL, OR 0.928, p<0.0001), weight (kg, OR 0.938, p<0.0001), age (years, OR 1.037, p<0.01), and sex (male/female, OR 0.493, p<0.01); receiver operating characteristic = 0.86. When externally validated, the rule had a sensitivity of 82.1 percent and a specificity of 63.6 percent (at a selected probability cutoff).
  
  
  CONCLUSIONS
  A simple and valid prediction rule is developed for predicting the risk of blood transfusion in patients undergoing first-time elective CABG surgery.
authors.affiliation: Department of Anesthesia, University Health Network, Toronto General Hospital, Centre for Research in Women's Health, Ontario, Canada. keyvan.karkouti@uhn.on.ca
authors.forename: K
authors.initials: K
authors.lastname: Karkouti
authors.forename: M M
authors.initials: MM
authors.lastname: Cohen
authors.forename: S A
authors.initials: SA
authors.lastname: McCluskey
authors.forename: G D
authors.initials: GD
authors.lastname: Sher
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D001803:Blood Transfusion / Q000191:economics / Q000706:statistics & numerical data*; D001026:Coronary Artery Bypass / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D017558:Elective Surgical Procedures; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors