Data analysis: The participants' demographics, preoperative characteristics and postoperative information will be summarized using descriptive statistics: expressed as mean (standard deviation) or median (minimum, maximum, interquartile range) for continuous variables and number (percent) for categorical variables. We will initially use restricted cubic spline plots to evaluate the nature of the association of platelet inhibition (continuous variable) and the risk of MACE (logit transformation). Based upon these results, we will determine if percent inhibition can be treated as a continuous variable, or whether it can be categorized along specific cut-offs. We will also compare these results against a receiver-operating-characteristic curve analysis. Once we have determined the appropriate approach for describing percent platelet inhibition, univariate analyses will be performed with chi-square or Fishers' exact test for categorical data, and t-test and Mann-Whitney U test for continuous data. A p < 0.05 will be considered statistically significant. Random effect multivariable logistic regression will be used to assess the adjusted association of platelet inhibition with MACE during the perioperative period.46 This regression model will employ a random intercept for each center, so as to minimize the bias due to any confounding by center.47 Regression models will be constructed to selectively include the potentially confounding perioperative variables that are associated with a higher risk of MACE (as identified from the literature and clinical experience).7-12 In addition to platelet inhibition (preoperative value or change), four covariates will be included in the model: type of stent; time between PCI and NCS; mono-, dual (aspirin or aspirin and clopidogrel) or no therapy until the day of surgery; and urgent surgery. Since the recommended range of platelet inhibition (20-50%)was never validated during the perioperative period we will describe it as a continuous variable (0-100%).