title: Predictors and impact of atrial fibrillation after isolated coronary artery bypass grafting.
abstract: OBJECTIVE
  Although an extensive number of studies have attempted to identify predictors of new-onset atrial fibrillation (AFIB) after coronary artery bypass grafting (CABG), a strong predictive model does not exist. Prior studies have included patients recruited from multiple centers with variant AFIB prevalence rates and those who underwent CABG in combination with other surgical procedures. Also, most studies have focused on pre- and perioperative characteristics, with less attention given to the initial postoperative period. The purpose of this study was to comprehensively examine pre-, peri-, and postoperative characteristics that might predict new-onset AFIB in a large sample of patients undergoing isolated CABG in a single medical center, utilizing data readily available to clinicians in electronic data repositories. In addition, length of stay and selected postoperative complications and disposition were compared in patients with AFIB and no AFIB.
  
  
  METHODS
  Retrospective, comparative survey.
  
  
  METHODS
  University-affiliated tertiary care hospital.
  
  
  METHODS
  Patients with new-onset AFIB who underwent isolated standard CABG or minimally invasive direct vision coronary artery bypass were identified from an electronic clinical data repository.
  
  
  METHODS
  None.
  
  
  RESULTS
  The prevalence of AFIB in the total sample (n = 814) was 31.9%. Predictors of AFIB included age (p =.0004), number of vessels bypassed (p =.013), vessel location (diagonal [p <.003] or posterior descending artery [p <.001]), and net fluid balance on the operative day (p =.015). Forward stepwise regression analysis produced a model that correctly predicted AFIB in only 24% of cases, with age (14%) and body surface area (9%) providing the most prediction. The incidence of embolic stroke was higher in AFIB (n = 8) vs. no AFIB (n = 4) patients, but stroke preceded AFIB onset in seven of eight cases. Subjects with AFIB had a longer stay (p =.0004), more intensive care unit readmissions (p =.0004), and required more assistance at hospital discharge (p =.017).
  
  
  CONCLUSIONS
  Despite attempts to examine comprehensively predictors of new-onset AFIB, we were unable to identify a robust predictive model. Our findings, in combination with prior work, imply that it may not be feasible to predict the development of new-onset AFIB after CABG using data readily available to the bedside clinician. In this sample, stroke was uncommon and, when it occurred, preceded AFIB in all but one case. As anticipated, AFIB increased length of stay, and patients with this complication required more assistance at discharge.
authors.affiliation: Department of Acute/Tertiary Care, School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Marilyn
authors.initials: M
authors.lastname: Hravnak
authors.forename: Leslie A
authors.initials: LA
authors.lastname: Hoffman
authors.forename: Melissa I
authors.initials: MI
authors.lastname: Saul
authors.forename: Thomas G
authors.initials: TG
authors.lastname: Zullo
authors.forename: Gayle R
authors.initials: GR
authors.lastname: Whitman
authors.forename: Bartley P
authors.initials: BP
authors.lastname: Griffith
mesh_terms: D000368:Aged; D001281:Atrial Fibrillation / Q000453:epidemiology* / Q000517:prevention & control; D001026:Coronary Artery Bypass; D005260:Female; D005544:Forecasting; D006801:Humans; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania / Q000453:epidemiology; D011183:Postoperative Complications / Q000453:epidemiology* / Q000517:prevention & control; D015995:Prevalence; D012044:Regression Analysis; D012189:Retrospective Studies; D012306:Risk; D020521:Stroke / Q000453:epidemiology