title: P-wave characteristics on routine preoperative electrocardiogram improve prediction of new-onset postoperative atrial fibrillation in cardiac surgery.
abstract: OBJECTIVE
  To test the hypothesis that including preoperative electrocardiogram (ECG) characteristics with clinical variables significantly improves the new-onset postoperative atrial fibrillation prediction model.
  
  
  METHODS
  Retrospective analysis.
  
  
  METHODS
  Single-center university hospital.
  
  
  METHODS
  Five hundred twenty-six patients, ≥ 18 years of age, who underwent coronary artery bypass grafting, aortic valve replacement, mitral valve replacement/repair, or a combination of valve surgery and coronary artery bypass grafting requiring cardiopulmonary bypass.
  
  
  METHODS
  Retrospective review of medical records.
  
  
  RESULTS
  Baseline characteristics and cardiopulmonary bypass times were collected. Digitally-measured timing and voltages from preoperative electrocardiograms were extracted. Postoperative atrial fibrillation was defined as atrial fibrillation requiring therapeutic intervention. Two hundred eight (39.5%) patients developed postoperative atrial fibrillation. Clinical predictors were age, ejection fraction<55%, history of atrial fibrillation, history of cerebral vascular event, and valvular surgery. Three ECG parameters associated with postoperative atrial fibrillation were observed: Premature atrial contraction, p-wave index, and p-frontal axis. Adding electrocardiogram variables to the prediction model with only clinical predictors significantly improved the area under the receiver operating characteristic curve, from 0.71 to 0.78 (p<0.01). Overall net reclassification improvement was 0.059 (p = 0.09). Among those who developed postoperative atrial fibrillation, the net reclassification improvement was 0.063 (p = 0.03).
  
  
  CONCLUSIONS
  Several p-wave characteristics are independently associated with postoperative atrial fibrillation. Addition of these parameters improves the postoperative atrial fibrillation prediction model.
authors.affiliation: Department of Anesthesia, Stanford University School of Medicine, Stanford, CA. Electronic address: wongjk2003@gmail.com.
authors.forename: Jim K
authors.initials: JK
authors.lastname: Wong
authors.affiliation: Department of Anesthesia, Cedars Sinai Medical Center, Los Angeles, CA.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Lobato
authors.affiliation: Department of Cardiology, Stanford University School of Medicine, Stanford, CA.
authors.forename: Andre
authors.initials: A
authors.lastname: Pinesett
authors.affiliation: Department of Anesthesia, Johns Hopkins University, Baltimore, MD.
authors.forename: Bryan G
authors.initials: BG
authors.lastname: Maxwell
authors.affiliation: Department of Anesthesia, Stanford University School of Medicine, Stanford, CA.
authors.forename: Christina T
authors.initials: CT
authors.lastname: Mora-Mangano
authors.affiliation: Department of Cardiology, Stanford University School of Medicine, Stanford, CA.
authors.forename: Marco V
authors.initials: MV
authors.lastname: Perez
mesh_terms: D000368:Aged; D001281:Atrial Fibrillation / Q000175:diagnosis / Q000453:epidemiology*; D006348:Cardiac Surgical Procedures; D002315:Cardiopulmonary Bypass; D004562:Electrocardiography / Q000379:methods / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D011300:Preoperative Care / Q000379:methods*; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors
keywords: cardiac surgery; electrocardiogram; p-wave characteristics; postoperative atrial fibrillation