title: Total arterial coronary revascularization and factors influencing in-hospital mortality.
abstract: OBJECTIVE
  To determine if arterial conduit selection or more efficient arterial revascularization techniques influence in-hospital mortality.
  
  
  METHODS
  Data from patients undergoing coronary artery bypass surgery at Royal Melbourne Hospital, Australia, between 1 January 1996 and 30 June 1998 (n = 1681) was collected prospectively. Logistic regression analysis was performed.
  
  
  RESULTS
  Independent preoperative predictors of increased in-hospital mortality included renal failure, redo coronary artery surgery and intra-aortic balloon pump use. In-hospital mortality for total arterial revascularization 0.7%, radial artery use 0.9%, pedicled arterial revascularization 0.2%, composite arterial conduit 0.4%, and the exclusive Y graft operation 0.3%. These were all associated with reduced in-hospital mortality. Mortality when vein graft was used was 2.9%. Most patients received total arterial revascularization, which was considered the primary surgical strategy.
  
  
  CONCLUSIONS
  Total arterial revascularization, radial artery use and complex arterial reconstructions were associated with reduced in-hospital mortality. Preoperative renal failure, intra-aortic balloon pump use and redo coronary surgery predicted greater in-hospital mortality.
authors.affiliation: Department of Cardiothoracic Surgery, The Royal Melbourne Hospital, Parkville, Victoria, Australia. alistair.royse@nwhcn.org.au
authors.forename: A G
authors.initials: AG
authors.lastname: Royse
authors.forename: C F
authors.initials: CF
authors.lastname: Royse
authors.forename: J
authors.initials: J
authors.lastname: Tatoulis
mesh_terms: D017677:Age Distribution; D000368:Aged; D001315:Australia / Q000453:epidemiology; D001026:Coronary Artery Bypass / Q000401:mortality* / Q000706:statistics & numerical data; D003625:Data Collection; D005260:Female; D006084:Graft Rejection; D006085:Graft Survival; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012307:Risk Factors; D017678:Sex Distribution; D016019:Survival Analysis