title: Prediction of Transfusions After Isolated Coronary Artery Bypass Grafting Surgical Procedures.
abstract: BACKGROUND
  Although blood transfusions are common and have been associated with adverse sequelae after cardiac surgical procedures, few contemporaneous models exist to support clinical decision making. This study developed a preoperative clinical decision support tool to predict perioperative red blood cell transfusions in the setting of isolated coronary artery bypass grafting.
  
  
  METHODS
  We performed a multicenter, observational study of 20,377 patients undergoing isolated coronary artery bypass grafting among patients at 39 hospitals participating in the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative's PERFusion measures and outcomes (PERForm) registry between 2011 and 2015. Candidates' preoperative risk factors were identified based on previous work and clinical input. The study population was randomly divided into a 70% development sample and a 30% validation sample. A generalized linear mixed-effect model was developed to predict perioperative red blood cell transfusion. The model's performance was assessed for calibration and discrimination. Sensitivity analysis was performed to assess the robustness of the model in different clinical subgroups.
  
  
  RESULTS
  Transfusions occurred in 36.8% of patients. The final regression model included 16 preoperative variables. The correlation between the observed and expected transfusions was 1.0. The risk prediction model discriminated well (receiver operator characteristic [ROC]development, 0.81; ROCvalidation, 0.82) and had satisfactory calibration (correlation between observed and expected rates was r = 1.00). The model performance was confirmed across medical centers and clinical subgroups.
  
  
  CONCLUSIONS
  Our risk prediction model uses 16 readily obtainable preoperative variables. This model, which provides a patient-specific estimate of the need for transfusion, offers clinicians a guide for decision making and evaluating the effectiveness of blood management strategies.
authors.affiliation: Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan. Electronic address: likosky@umich.edu.
authors.forename: Donald S
authors.initials: DS
authors.lastname: Likosky
authors.affiliation: Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan.
authors.forename: Theron A
authors.initials: TA
authors.lastname: Paugh
authors.affiliation: Heart and Vascular Institute, Henry Ford Macomb Hospitals, Clinton Township, Michigan.
authors.forename: Steven D
authors.initials: SD
authors.lastname: Harrington
authors.affiliation: Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan.
authors.forename: Xiaoting
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.
authors.forename: Mary A M
authors.initials: MA
authors.lastname: Rogers
authors.affiliation: Department of Surgery, Mayo Clinic, Rochester, Minnesota.
authors.forename: Timothy A
authors.initials: TA
authors.lastname: Dickinson
authors.affiliation: Department of Cardiac Surgery, Bronson Methodist Hospital, Kalamazoo, Michigan.
authors.forename: Alphonse
authors.initials: A
authors.lastname: DeLucia
authors.affiliation: Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan.
authors.forename: Barbara R
authors.initials: BR
authors.lastname: Benedetti
authors.affiliation: Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan.
authors.forename: Richard L
authors.initials: RL
authors.lastname: Prager
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Division of Cardiac Surgery, Department of Surgery, Henry Ford Hospital, Detroit, Michigan.
authors.forename: Gaetano
authors.initials: G
authors.lastname: Paone
mesh_terms: D000368:Aged; D001803:Blood Transfusion; D000066491:Clinical Decision-Making; D001026:Coronary Artery Bypass; D003324:Coronary Artery Disease / Q000150:complications / Q000401:mortality / Q000601:surgery*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020380:Needs Assessment; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment