title: Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry.
abstract: OBJECTIVE
  We sought to create contemporary models for predicting mortality risk following percutaneous coronary intervention (PCI).
  
  
  BACKGROUND
  There is a need to identify PCI risk factors and accurately quantify procedural risks to facilitate comparative effectiveness research, provider comparisons, and informed patient decision making.
  
  
  METHODS
  Data from 181,775 procedures performed from January 2004 to March 2006 were used to develop risk models based on pre-procedural and/or angiographic factors using logistic regression. These models were independently evaluated in 2 validation cohorts: contemporary (n = 121,183, January 2004 to March 2006) and prospective (n = 285,440, March 2006 to March 2007).
  
  
  RESULTS
  Overall, PCI in-hospital mortality was 1.27%, ranging from 0.65% in elective PCI to 4.81% in ST-segment elevation myocardial infarction patients. Multiple pre-procedural clinical factors were significantly associated with in-hospital mortality. Angiographic variables provided only modest incremental information to pre-procedural risk assessments. The overall National Cardiovascular Data Registry (NCDR) model, as well as a simplified NCDR risk score (based on 8 key pre-procedure factors), had excellent discrimination (c-index: 0.93 and 0.91, respectively). Discrimination and calibration of both risk tools were retained among specific patient subgroups, in the validation samples, and when used to estimate 30-day mortality rates among Medicare patients.
  
  
  CONCLUSIONS
  Risks for early mortality following PCI can be accurately predicted in contemporary practice. Incorporation of such risk tools should facilitate research, clinical decisions, and policy applications.
authors.affiliation: Duke Clinical Research Institute, Durham, North Carolina 27715, USA. Peter016@mc.duke.edu
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.forename: David
authors.initials: D
authors.lastname: Dai
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: DeLong
authors.forename: J Matthew
authors.initials: JM
authors.lastname: Brennan
authors.forename: Mandeep
authors.initials: M
authors.lastname: Singh
authors.forename: Sunil V
authors.initials: SV
authors.lastname: Rao
authors.forename: Richard E
authors.initials: RE
authors.lastname: Shaw
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Roe
authors.forename: Kalon K L
authors.initials: KK
authors.lastname: Ho
authors.forename: Lloyd W
authors.initials: LW
authors.lastname: Klein
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Krone
authors.forename: William S
authors.initials: WS
authors.lastname: Weintraub
authors.forename: Ralph G
authors.initials: RG
authors.lastname: Brindis
authors.forename: John S
authors.initials: JS
authors.lastname: Rumsfeld
authors.forename: John A
authors.initials: JA
authors.lastname: Spertus
mesh_terms: D000368:Aged; D015906:Angioplasty, Balloon, Coronary / Q000401:mortality* / Q000706:statistics & numerical data; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000401:mortality* / Q000628:therapy; D011379:Prognosis; D012042:Registries / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D020379:Risk Adjustment / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012307:Risk Factors; D014481:United States / Q000453:epidemiology