title: Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994-1996. Northern New England Cardiovascular Disease Study Group.
abstract: OBJECTIVE
  Using recent data, we sought to identify risk factors associated with in-hospital mortality among patients undergoing percutaneous coronary interventions.
  
  
  BACKGROUND
  The ability to accurately predict the risk of an adverse outcome is important in clinical decision making and for risk adjustment when assessing quality of care. Most clinical prediction rules for percutaneous coronary intervention (PCI) were developed using data collected before the broader use of new interventional devices.
  
  
  METHODS
  Data were collected on 15,331 consecutive hospital admissions by six clinical centers. Logistic regression analysis was used to predict the risk of in-hospital mortality.
  
  
  RESULTS
  Variables associated with an increased risk of in-hospital mortality included older age, congestive heart failure, peripheral or cerebrovascular disease, increased creatinine levels, lowered ejection fraction, treatment of cardiogenic shock, treatment of an acute myocardial infarction, urgent priority, emergent priority, preprocedure insertion of an intraaortic balloon pump and PCI of a type C lesion. The receiver operating characteristic area for the predicted probability of death was 0.88, indicating a good ability to discriminate. The rule was well calibrated, predicting accurately at all levels of risk. Bootstrapping demonstrated that the estimate was stable and performed well among different patient subsets.
  
  
  CONCLUSIONS
  In the current era of interventional cardiology, accurate calculation of the risk of in-hospital mortality after a percutaneous coronary intervention is feasible and may be useful for patient counseling and for quality improvement purposes.
authors.affiliation: Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA.
authors.forename: G T
authors.initials: GT
authors.lastname: O'Connor
authors.forename: D J
authors.initials: DJ
authors.lastname: Malenka
authors.forename: H
authors.initials: H
authors.lastname: Quinton
authors.forename: J F
authors.initials: JF
authors.lastname: Robb
authors.forename: M A
authors.initials: MA
authors.lastname: Kellett
authors.forename: S
authors.initials: S
authors.lastname: Shubrooks
authors.forename: W A
authors.initials: WA
authors.lastname: Bradley
authors.forename: M J
authors.initials: MJ
authors.lastname: Hearne
authors.forename: M W
authors.initials: MW
authors.lastname: Watkins
authors.forename: D E
authors.initials: DE
authors.lastname: Wennberg
authors.forename: B
authors.initials: B
authors.lastname: Hettleman
authors.forename: D J
authors.initials: DJ
authors.lastname: O'Rourke
authors.forename: P D
authors.initials: PD
authors.lastname: McGrath
authors.forename: T
authors.initials: T
authors.lastname: Ryan
authors.forename: P
authors.initials: P
authors.lastname: VerLee
mesh_terms: D000368:Aged; D015906:Angioplasty, Balloon, Coronary / Q000401:mortality* / Q000706:statistics & numerical data; D003327:Coronary Disease / Q000401:mortality* / Q000628:therapy; D003625:Data Collection / Q000379:methods; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009511:New England / Q000453:epidemiology; D011379:Prognosis; D012372:ROC Curve; D012307:Risk Factors