title: Factors affecting transfusion of fresh frozen plasma, platelets, and red blood cells during elective coronary artery bypass graft surgery.
abstract: BACKGROUND
  The ability to predict the use of blood components during surgery will improve the blood bank's ability to provide efficient service.
  
  
  OBJECTIVE
  Develop prediction models using preoperative risk factors to assess blood component usage during elective coronary artery bypass graft surgery (CABG).
  
  
  METHODS
  Eighty-three preoperative, multidimensional risk variables were evaluated for patients undergoing elective CABG-only surgery.
  
  
  METHODS
  The study endpoints included transfusion of fresh frozen plasma (FFP), platelets, and red blood cells (RBC). Multivariate logistic regression models were built to assess the predictors related to each of these endpoints.
  
  
  METHODS
  Department of Veterans Affairs (VA) health care system.
  
  
  METHODS
  Records for 3034 patients undergoing elective CABG-only procedures; 1033 patients received a blood component transfusion during CABG.
  
  
  RESULTS
  Previous heart surgery and decreased ejection fraction were significant predictors of transfusion for all blood components. Platelet count was predictive of platelet transfusion and FFP utilization. Baseline hemoglobin was a predictive factor for more than 2 units of RBC. Some significant hospital variation was noted beyond that predicted by patient risk factors alone.
  
  
  CONCLUSIONS
  Prediction models based on preoperative variables may facilitate blood component management for patients undergoing elective CABG. Algorithms are available to predict transfusion resources to assist blood banks in improving responsiveness to clinical needs. Predictors for use of each blood component may be identified prior to elective CABG for VA patients.
authors.affiliation: Department of Pathology, University of Colorado Health Sciences Center School of Medicine, and Denver Veterans Affairs Medical Center, Denver, Colo 80220, USA.
authors.forename: Randal
authors.initials: R
authors.lastname: Covin
authors.forename: Maureen
authors.initials: M
authors.lastname: O'Brien
authors.forename: Gary
authors.initials: G
authors.lastname: Grunwald
authors.forename: Bradley
authors.initials: B
authors.lastname: Brimhall
authors.forename: Gulshan
authors.initials: G
authors.lastname: Sethi
authors.forename: Steven
authors.initials: S
authors.lastname: Walczak
authors.forename: William
authors.initials: W
authors.lastname: Reiquam
authors.forename: Chitra
authors.initials: C
authors.lastname: Rajagopalan
authors.forename: A Laurie
authors.initials: AL
authors.lastname: Shroyer
mesh_terms: D016063:Blood Loss, Surgical / Q000706:statistics & numerical data*; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D001026:Coronary Artery Bypass / Q000379:methods*; D017558:Elective Surgical Procedures / Q000379:methods*; D017707:Erythrocyte Transfusion / Q000706:statistics & numerical data*; D005260:Female; D006787:Hospitals, Veterans; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010949:Plasma; D017713:Platelet Transfusion / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D011300:Preoperative Care / Q000379:methods; D012307:Risk Factors