Modeling for decision making is an important tool to analyze the potential outcomes of various treatment options based on the best available evidence. Many times clinical studies have not addressed the exact research question being asked, but there is related data available from other studies. Models can be constructed from this data and used as a justification for establishing new randomized clinical trials and gives the clinician an early indication of treatment outcomes. Models have a short lifespan because they are based on the latest clinical evidence in related fields. Two types of models will be presented – Decision Trees and Markov Modeling.
Decision Tree Example - There have been a large number of publications comparing this Off-Pump Coronary Artery Bypass Surgery (OPCAB) with the "gold standard" On-Pump surgery. Prospective randomized clinical trials that have compared these two procedures have always used standard high prime systems. This model specifically looks at a comparison of the costs associated with outcomes of On Pump surgery using low prime circuits with retrograde autologous prime (RAP) to Off-Pump Surgery. A breakeven point can be determined within a range of outcomes for each procedure.
Markov Model Example - The purpose of this model was to look at the costs associated with ECMO and ventricular assist in the postcardiotomy patient. Markov models are used in decision analysis to accurately represent complex processes that involve transition in and out of various states of health. The two different types of states are transition states and absorbing states. Patients are able to move in and out of the transition states freely, but once they enter an absorbing state such as death, they remain there. The model uses the probabilities of being in each state over time to determine the totals costs associated with each treatment modality. This model can be used to answer the question, "When is the most cost effective time to institute ventricular assist in a patient with postcardiotomy failure".
