title: Microsimulation for clinical decision-making in individual patients with established coronary artery disease: a concept.
abstract: 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.
  
  
  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.
authors.affiliation: Clinical Epidemiology Unit, Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands. l.battes@erasmusmc.nl
authors.forename: Linda
authors.initials: L
authors.lastname: Battes
authors.forename: Isabella
authors.initials: I
authors.lastname: Kardys
authors.forename: Rogier
authors.initials: R
authors.lastname: Barendse
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: Masoud
authors.initials: M
authors.lastname: Amiri
authors.forename: Marinus J C
authors.initials: MJ
authors.lastname: Eijkemans
authors.forename: Jaap W
authors.initials: JW
authors.lastname: Deckers
authors.forename: Douwe
authors.initials: D
authors.lastname: Postmus
authors.forename: Johanna J M
authors.initials: JJ
authors.lastname: Takkenberg
authors.forename: Ken
authors.initials: K
authors.lastname: Redekop
authors.forename: Eric
authors.initials: E
authors.lastname: Boersma
mesh_terms: D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000150:complications*; D003657:Decision Making; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000209:etiology* / Q000401:mortality*; D011379:Prognosis; D012044:Regression Analysis; D012307:Risk Factors; D015996:Survival Rate