title: The New York State risk score for predicting in-hospital/30-day mortality following percutaneous coronary intervention.
abstract: OBJECTIVE
  This study sought to develop a percutaneous coronary intervention (PCI) risk score for in-hospital/30-day mortality.
  
  
  BACKGROUND
  Risk scores are simplified linear scores that provide clinicians with quick estimates of patients' short-term mortality rates for informed consent and to determine the appropriate intervention. Earlier PCI risk scores were based on in-hospital mortality. However, for PCI, a substantial percentage of patients die within 30 days of the procedure after discharge.
  
  
  METHODS
  New York's Percutaneous Coronary Interventions Reporting System was used to develop an in-hospital/30-day logistic regression model for patients undergoing PCI in 2010, and this model was converted into a simple linear risk score that estimates mortality rates. The score was validated by applying it to 2009 New York PCI data. Subsequent analyses evaluated the ability of the score to predict complications and length of stay.
  
  
  RESULTS
  A total of 54,223 patients were used to develop the risk score. There are 11 risk factors that make up the score, with risk factor scores ranging from 1 to 9, and the highest total score is 34. The score was validated based on patients undergoing PCI in the previous year, and accurately predicted mortality for all patients as well as patients who recently suffered a myocardial infarction (MI).
  
  
  CONCLUSIONS
  The PCI risk score developed here enables clinicians to estimate in-hospital/30-day mortality very quickly and quite accurately. It accurately predicts mortality for patients undergoing PCI in the previous year and for MI patients, and is also moderately related to perioperative complications and length of stay.
authors.affiliation: University at Albany, State University of New York, Albany, New York 12144-3456, USA. elh03@health.state.ny.us
authors.forename: Edward L
authors.initials: EL
authors.lastname: Hannan
authors.forename: Louise Szypulski
authors.initials: LS
authors.lastname: Farrell
authors.forename: Gary
authors.initials: G
authors.lastname: Walford
authors.forename: Alice K
authors.initials: AK
authors.lastname: Jacobs
authors.forename: Peter B
authors.initials: PB
authors.lastname: Berger
authors.forename: David R
authors.initials: DR
authors.lastname: Holmes
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Stamato
authors.forename: Samin
authors.initials: S
authors.lastname: Sharma
authors.forename: Spencer B
authors.initials: SB
authors.lastname: King
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009518:New York / Q000453:epidemiology; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects / Q000401:mortality*; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome