title: Prediction of survival after coronary revascularization: modeling short-term, mid-term, and long-term survival.
abstract: BACKGROUND
  Many clinical prediction rules for short-term mortality after coronary revascularization have been developed, validated, and introduced into routine clinical practice. Few rules exist for predicting long-term survival in the modern era of coronary revascularization. We report on the development and validation of models for predicting long-term survival after coronary artery bypass graft surgery and percutaneous coronary intervention on the basis of recent experience.
  
  
  METHODS
  We linked 1987 through 2001 coronary artery bypass graft surgery and 1992 through 2001 percutaneous coronary intervention data from our northern New England registries on 35,000 patients with complete data on risk factors to the National Death Index, ascertaining 7,000 deaths. We partitioned time after revascularization into three periods on the basis of exploratory analysis using generalizations of Cox's semiparametric model to nonproportional hazards and nonlinear log-hazards. These periods were 0 to 3 months, 4 to 18 months, and 19 months and later. For each period, Cox's regression model was used to regress survival on risk factors yielding three models, which were then combined to make long-term predictions.
  
  
  RESULTS
  These models were incorporated into easy-to-use software that yields predicted survival for up to 8 years after revascularization. The Harrell concordance statistic ranged from 72% to 83% for these models.
  
  
  CONCLUSIONS
  We developed and internally validated models that accurately predict long-term survival after coronary artery bypass graft surgery and percutaneous coronary intervention as currently performed. These models using routine clinical data, can be solved with available software, and could be used to enhance informed, patient-centered clinical decision making on the choice of revascularization procedure.
authors.affiliation: Department of Medicine, Dartmouth Medical School, Hanover, New Hampshire, USA. mackenzie@hitchcock.org
authors.forename: Todd A
authors.initials: TA
authors.lastname: MacKenzie
authors.forename: David J
authors.initials: DJ
authors.lastname: Malenka
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Olmstead
authors.forename: Winthrop D
authors.initials: WD
authors.lastname: Piper
authors.forename: Craig
authors.initials: C
authors.lastname: Langner
authors.forename: Cathy S
authors.initials: CS
authors.lastname: Ross
authors.forename: Gerald T
authors.initials: GT
authors.lastname: O'Connor
mesh_terms: D000367:Age Factors; D000368:Aged; D015906:Angioplasty, Balloon, Coronary / Q000379:methods / Q000401:mortality; D002423:Cause of Death; D001026:Coronary Artery Bypass / Q000379:methods / Q000401:mortality; D003327:Coronary Disease / Q000000981:diagnostic imaging / Q000401:mortality / Q000601:surgery*; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009204:Myocardial Revascularization / Q000379:methods / Q000401:mortality*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011859:Radiography; D012042:Registries; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012737:Sex Factors; D016019:Survival Analysis; D013997:Time Factors