title: Fast-track failure after cardiac surgery: development of a prediction model.
abstract: OBJECTIVE
  Risk factors for unsuccessful fast-tracking of cardiac surgery patients have not been collectively defined in the literature. The aim of this study was to determine risk factors for fast-track failure and incorporate them into a predictive fast-track failure score.
  
  
  METHODS
  Prospective observational study.
  
  
  METHODS
  Cardiothoracic Department of St Mary's Hospital, London.
  
  
  METHODS
  Data were collected from April 2003 to April 2005 including 1,084 patients undergoing heart surgery who were admitted into the fast-track unit.
  
  
  METHODS
  Multifactorial logistic regression was used to develop a propensity score for estimating the likelihood of fast-track failure.
  
  
  RESULTS
  One hundred and sixty-nine patients failed fast-track management (15.6%). Independent predictors for fast-track failure were impaired left ventricular function with or without recent acute coronary syndrome (odds ratios 2.89 and 1.65 respectively), re-do operation (one, two, or more vs. none, odds ratio 1.75, 7.98), extracardiac arteriopathy (odds ratio 2.63), preoperative intra-aortic balloon pump (odds ratio 3.09), raised serum creatinine in micromol/L (120-150, >150 vs. <120, odds ratio 1.57, 11.24), and nonelective (odds ratio 3.43) and complex surgery (odds ratio 2.70). Model validation showed very good discrimination (area under the curve = 0.815) and calibration (ĉ statistic = 8.527, p = .129).
  
  
  CONCLUSIONS
  The fast-track failure score incorporates several preoperative factors and has been successfully internally validated; after undergoing external validation and possible recalibration it may be used as a tool to facilitate planning and flow of cardiac surgery patients, based on the predicted probability of failure. Application of this score may limit fast-track failure rates and help to reduce morbidity and cost.
authors.affiliation: Imperial College London, Department of Surgical Oncology and Technology, St Mary's Hospital, London, UK.
authors.forename: Vasilis A
authors.initials: VA
authors.lastname: Constantinides
authors.forename: Paris P
authors.initials: PP
authors.lastname: Tekkis
authors.forename: Azeem
authors.initials: A
authors.lastname: Fazil
authors.forename: Kelly
authors.initials: K
authors.lastname: Kaur
authors.forename: Richard
authors.initials: R
authors.lastname: Leonard
authors.forename: Mike
authors.initials: M
authors.lastname: Platt
authors.forename: Roberto
authors.initials: R
authors.lastname: Casula
authors.forename: Rex
authors.initials: R
authors.lastname: Stanbridge
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
authors.forename: Thanos
authors.initials: T
authors.lastname: Athanasiou
mesh_terms: D000368:Aged; D006348:Cardiac Surgical Procedures / Q000379:methods*; D019091:Critical Pathways / Q000706:statistics & numerical data*; D003710:Demography; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D011182:Postoperative Care / Q000379:methods; D011237:Predictive Value of Tests; D011300:Preoperative Care / Q000379:methods; D011446:Prospective Studies; D012307:Risk Factors; D017211:Treatment Failure