title: Simple risk algorithm to predict serious bleeding in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: RISK-PCI bleeding score.
abstract: BACKGROUND
  Bleeding is a potentially catastrophic complication after primary percutaneous coronary intervention (PPCI). It occurs most frequently within the first 30 days following the intervention. The aim of this study was to generate a simple and accurate risk model for the prediction of bleeding after PPCI.
  
  
  RESULTS
  The training set included 2,096 patients enrolled in the RISK-PCI trial. The model was validated using a database of 961 patients enrolled in the ART-PCI trial. Bleeding was defined as type ≥3a bleeding according to the Bleeding Academic Research Consortium definition. Multivariate logistic regression was used to evaluate the predictors of outcome. A sum of weighted points for specific predictors was calculated to determine the final score. The model included 5 independent predictors of 30-day bleeding: gender (female); history of peptic ulcer; creatinine clearance at admission (<60 ml/min); hemoglobin at presentation (<125 g/dl); and Killip class >1 heart failure at admission. The model showed good discrimination and calibration for the prediction of bleeding in the derivation set (C-statistic, 0.79; goodness of fit, P=0.12) and in the validation set (C-statistic, 0.76; goodness of fit, P=0.37). Patients were classified into 3 risk classes and the observed incidence of 30-day bleeding of 1.0%, 3.5% and 10.7% corresponded to the low-, intermediate- and high-risk classes, respectively.
  
  
  CONCLUSIONS
  A simple risk model was developed that has a reasonably good capacity for the prediction of 30-day bleeding after PPCI.
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: Ratko
authors.initials: R
authors.lastname: Lasica
authors.forename: Nemanja
authors.initials: N
authors.lastname: Djuricic
authors.forename: Natasa
authors.initials: N
authors.lastname: Brdar
authors.forename: Jelena
authors.initials: J
authors.lastname: Marinkovic
authors.forename: Nikola
authors.initials: N
authors.lastname: Kocev
authors.forename: Jovan
authors.initials: J
authors.lastname: Perunicic
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000453:epidemiology / Q000503:physiopathology / Q000628:therapy*; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects*; D019106:Postoperative Hemorrhage / Q000453:epidemiology* / Q000209:etiology / Q000503:physiopathology; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012737:Sex Factors; D013997:Time Factors