title: Simple bedside additive tool for prediction of in-hospital mortality after percutaneous coronary interventions.
abstract: BACKGROUND
  Risk-adjustment models for percutaneous coronary intervention (PCI) mortality have been recently reported, but application in bedside prediction of prognosis for individual patients remains untested.
  
  
  RESULTS
  Between July 1, 1997 and September 30, 1999, 10 796 consecutive procedures were performed in a consortium of 8 hospitals. Predictors of in-hospital mortality were identified by use of multivariate logistic regression analysis. The final model was validated by use of the bootstrap technique. Additional validation was performed on an independent data set of 5863 consecutive procedures performed between October 1, 1999, and August 30, 2000. An additive risk-prediction score was developed by rounding coefficients of the logistic regression model to the closest half-integer, and a visual bedside tool for the prediction of individual patient prognosis was developed. In this patient population, the in-hospital mortality rate was 1.6%. Multivariate regression analysis identified acute myocardial infarction, cardiogenic shock, history of cardiac arrest, renal insufficiency, low ejection fraction, peripheral vascular disease, lesion characteristics, female sex, and advanced age as independent predictors of death. The model had excellent discrimination (area under the receiver operating characteristic curve, 0.90) and was accurate for prediction of mortality among different subgroups. Near-perfect correlation existed between calculated scores and observed mortality, with higher scores associated with higher mortality.
  
  
  CONCLUSIONS
  Accurate predictions of individual patient risk of mortality associated with PCI can be achieved with a simple bedside tool. These predictions could be used during discussions of prognosis before and after PCI.
authors.affiliation: Division of Cardiology, University of Michigan, Blue Cross Blue Shield of Michigan Cardiovascular Consortium Coordinating Center, Ann Arbor, USA.
authors.forename: M
authors.initials: M
authors.lastname: Moscucci
authors.forename: E
authors.initials: E
authors.lastname: Kline-Rogers
authors.forename: D
authors.initials: D
authors.lastname: Share
authors.forename: M
authors.initials: M
authors.lastname: O'Donnell
authors.forename: A
authors.initials: A
authors.lastname: Maxwell-Eward
authors.forename: W L
authors.initials: WL
authors.lastname: Meengs
authors.forename: P
authors.initials: P
authors.lastname: Kraft
authors.forename: A C
authors.initials: AC
authors.lastname: DeFranco
authors.forename: J L
authors.initials: JL
authors.lastname: Chambers
authors.forename: K
authors.initials: K
authors.lastname: Patel
authors.forename: J G
authors.initials: JG
authors.lastname: McGinnity
authors.forename: K A
authors.initials: KA
authors.lastname: Eagle
mesh_terms: D000367:Age Factors; D015906:Angioplasty, Balloon, Coronary / Q000401:mortality*; D002318:Cardiovascular Diseases; D003327:Coronary Disease / Q000145:classification / Q000401:mortality / Q000628:therapy*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D012372:ROC Curve; D051437:Renal Insufficiency; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012737:Sex Factors