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.

Conclusion: 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.