ITERATIVE CALIBRATION OF MONTE-CARLO MARKOV MODELS USED FOR EVALUATING THE IMPACT OF UPDATING TRADITIONAL CARDIOVASCULAR RISK PREDICTIONS WITH NOVEL RISK MARKERS

Purpose: �� To present and validate an iterative method to calibrate Monte-Carlo Markov models used for evaluating the impact of updating traditional cardiovascular disease (CVD) risk predictions with novel risk markers.

Method: �� We developed a Monte Carlo-Markov model with three health states: 1) alive and CVD-free, 2) post-CVD, and 3) death. One-year transition probabilities were based on the Framingham 30-year cardiovascular risk function, which include traditional factors and takes into account competing non-cardiovascular death. We updated the Framingham risk function with the CT coronary calcium score, ankle-brachial index, high-sensitivity C-reactive protein and carotid intima-media thickness, by extending the original linear predictor with the respective adjusted beta-coefficients from meta-analyses. Individual risk profiles, containing information on the traditional and 4 novel risk factors were taken from 3,736 asymptomatic subjects of the National Health and Nutrition Examination Survey (NHANES). �� We assumed that the average CVD risk based on the traditional risk factors alone would not change as a result of the addition of the novel risk factors. Using a cycle length of 1 year, we calculated the uncalibrated 1-yr CVD risk by using the hazard of year 1 and the extended linear predictor. We then added a fixed term to the extended linear predictor such that the average 1-yr CVD risk of all 3,736 individuals equalled the 1-yr CVD risk based on the original Framingham risk function. Using the calibrated first year probability of CVD, we simulated the 3,736 individuals and tracked which individuals experienced a CVD event or competing death. The remainder of these individuals were used to recalibrate the 1-yr CVD risk of year 2. This was repeated for 30 years. �� Finally, we compared the 30-year CVD risk simulated by the calibrated Monte-Carlo Markov model for the 3,736 individuals with the predicted 30-year CVD risk based on the original Framingham risk function. �

Result: �� The average CVD risk at year 1-to-30 of the 3,736 subjects (median age 53 years ICR 46 - 63, 48% male) simulated by the calibrated Monte-Carlo Markov model matched the predicted CVD risks based on the original Framingham risk function (figure 1).

Conclusion: �� We presented a valid method to calibrate Monte-Carlo Markov models which can be used to evaluate the impact of updating traditional CVD risk functions with novel risk markers.