Microsimulation for Clinical Decision-Making in Individual Patients With Established Coronary Artery Disease

BACKGROUND: In cardiovascular disease, numerous evidence-based prognostic models have been created, usually based on regression analyses of isolated patient datasets. They tend to focus on one outcome event, based on just one baseline evaluation of the patient, and fail to take the disease process in its dynamic nature into account. We present so-called microsimulation as an attractive alternative for clinical decision-making in individual patients. We aim to further familiarize clinicians with the concept of microsimulation and to inform them about the modeling process. METHODS AND RESULTS: We describe the modeling process, advantages and disadvantages of microsimulation. We illustrate the concept using a hypothetical 60-year-old patient, with several cardiac risk factors, who is hospitalized for myocardial infarction. By using microsimulation, we calculate this patient's probability of death. In our example, this particular patient's estimated life expectancy turns out to be 8.9 years. While calculating this life expectancy, we were able to account for multiple outcome events and changing patient characteristics. CONCLUSIONS: Microsimulation takes into account the dynamic nature of coronary artery disease by estimating most likely outcomes regarding a broad range of clinical events. Moreover, microsimulation can be used to evaluate treatment effects by estimating the event-free life expectancy with and without treatment. Hence, microsimulation has several advantages compared to modeling techniques such as regression.