Comparison of models for predicting outcomes in patients with coronary artery disease focusing on microsimulation

Physicians have difficulty to subjectively estimate the cardiovascular risk of their patients. Using an estimate of global cardiovascular risk could be more relevant to guide decisions than using binary representation (presence or absence) of risk factors data. The main aim of the paper is to compare different models of predicting the progress of a coronary artery diseases (CAD) to help the decision making of physician.

There are different standard models for predicting risk factors such as models based on logistic regression model, Cox regression model, dynamic logistic regression model, and simulation models such as Markov model and microsimulation model. Each model has its own application which can or cannot use by physicians to make a decision on treatment of each patient.

There are five main common models for predicting of outcomes, including models based on 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). The advantages and disadvantages of these models have been discussed and summarized.

Given the complex medical decisions that physicians 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, physicians may potentially benefit from a clinical decision support system that accounts for all these considerations. The microsimulation model could provide cardiologists, researchers, and medical students a user-friendly software, which can be used as an intelligent interventional simulator.