Comparison of Models for Predicting Outcomes in Patients with Coronary Artery Disease Focusing on Microsimulation

Every day the cardiologist faces the complex clinical decisionmaking challenges. Each cardiac disease can be treated in multiple ways according to multiple interrelated factors dependent on patient characteristics, severity, and progression of disease, and patient and physician preference. There are different standard models for predicting risk factors such as models based on the logistic regression model, Cox regression model, dynamic logistic regression model, and simulation models such as Markov model and microsimulation model. The main aim of the paper is to compare different models of predicting the progress of a coronary artery disease, in order to help cardiologists to make a good decision, and also answer the above-mentioned questions. The microsimulation model should provide cardiologists, researchers, and medical students a user-friendly software package, which can be used as an intelligent interventional simulator. There are five main common models for predicting of outcomes, including models based on the logistic regression model (for short-term outcomes), Cox regression model (for intermediate-term outcomes), dynamic logistic regression model, and simulation models such as Markov and microsimulation models (for long-term outcomes). Given the complex medical decisions cardiologists face in everyday practice, the multiple interrelated factors that play a role in choosing the optimal treatment, and the continuously accumulating new evidence on determinants of outcome and treatment options for CAD, the cardiologist may potentially benefit from a clinical decision support system that accounts for all these considerations and allows for evidence-based objective selection of the optimal treatment for the patient that is sitting in his/her office.