DESCRIPTION (provided by applicant): The Logical Analysis of Data (LAD) is an exciting new approach that can be used for systematically analyzing large databases for the purpose of developing and validating clinically useful risk prediction schemes. Unlike standard regression techniques, LAD does not primarily focus on individual risk factors and two-way interactions between them. Rather, LAD is designed to identify complex patterns of findings, or syndromes, that predict outcomes. This method has been applied to problems in economics, seismology, and oil exploration, and very recently, also to medicine. Cardiovascular risk stratification may be an appropriate application for the LAD as it relies on simultaneous and sequential collections of many different data elements. In preliminary work, we have shown that LAD works well for prediction of death among low-risk patients referred for stress electrocardiography without imaging. In this application there are three Specific Aims: 1. Apply LAD to develop and validate risk prediction instruments among patients undergoing different types of cardiac surgery. 2. Compare the predictive value of LAD predictive instruments with predictive instruments developed using standard statistical methods, including multiple time-phase parametric modeling. 3. Develop predictive instruments using relative risk forests, a new Monte Carlo method for estimating risk values in large survival data settings with large numbers of correlated variables. Relative risk forests are an adaptation of random forests introduced by Breiman. When possible these methods will be compared to LAD. Internal estimates for the generalization error, a measure of how well the method will generalize to other data settings, will be computed and will be used in the development of the predictive instrument. Relative risk forests will also be compared to several other non-deterministic methods, including boosting and spike and slab variable selection. All of these techniques can be used to develop complex models while maintaining good prediction error and are ideal for high dimensional problems where traditional methods breakdown. Although this project will focus on risk assessment among patients undergoing cardiac surgery, it is important to recognize that we are primarily interested in the value of LAD as a means of analyzing very large and complex data sets within a medical sphere. Hence, the applicability of this work goes beyond determination of risk of patients undergoing cardiac surgery.