title: Predicting 30-day major adverse cardiovascular events after primary percutaneous coronary intervention. The RISK-PCI score.
abstract: BACKGROUND
  Identification of patients at risk for major adverse cardiovascular events (MACE) might help selecting candidates for aggressive treatment or early discharge after primary percutaneous coronary intervention (pPCI).
  
  
  METHODS
  The RISK-PCI is an observational trial of 2096 consecutive patients who underwent pPCI between 2006 and 2009, randomly allocated to derivation and validation sets with a set ratio of 80% to 20%. Thirty-day MACE comprising death, nonfatal reinfarction and stroke was the primary end point. Multivariable logistic regression was used to determine the independent predictors of outcome. A sum of weighted points for specific predictors was calculated to define the final score.
  
  
  RESULTS
  The RISK-PCI score comprised 12 independent predictors of 30-day MACE, with a graded 125-fold increase in the primary end point with increasing risk score from ≤ 1 to ≥ 15. The model showed good discrimination and calibration for the prediction of 30-day MACE (c-statistic 0.83, goodness-of-fit p = 0.72) and 30-day death (c-statistic 0.87, goodness-of-fit p = 0.56). Bootstrapping with 1000 resample confirmed the stability of the model's performance. Patients were classified into risk classes, with the observed incidence of 30-day MACE of 1.9, 5.9, 13.3 and 39.4% in the low, intermediate, high and very high-risk classes, respectively. An 18-fold graded increase in the primary end point was observed between patients in a low risk class and those in a very high risk class.
  
  
  CONCLUSIONS
  We derived a novel risk model to predict 30-day MACE after pPCI, which might help clinician decide the most appropriate treatment in accordance with the patient's risk profile.
authors.affiliation: University of Belgrade School of Medicine, Belgrade, Serbia. igormrd@gmail.com
authors.forename: Igor
authors.initials: I
authors.lastname: Mrdovic
authors.forename: Lidija
authors.initials: L
authors.lastname: Savic
authors.forename: Gordana
authors.initials: G
authors.lastname: Krljanac
authors.forename: Milika
authors.initials: M
authors.lastname: Asanin
authors.forename: Jovan
authors.initials: J
authors.lastname: Perunicic
authors.forename: Ratko
authors.initials: R
authors.lastname: Lasica
authors.forename: Jelena
authors.initials: J
authors.lastname: Marinkovic
authors.forename: Nikola
authors.initials: N
authors.lastname: Kocev
authors.forename: Zorana
authors.initials: Z
authors.lastname: Vasiljevic
authors.forename: Miodrag
authors.initials: M
authors.lastname: Ostojic
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000401:mortality*; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects* / Q000401:mortality*; D011183:Postoperative Complications / Q000175:diagnosis* / Q000401:mortality*; D011237:Predictive Value of Tests; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome