MODELING THE ADDED PREDICTIVE VALUE OF A NOVEL CARDIOVASCULAR RISK MARKER WITH A SIMPLE MARKOV MODEL

Purpose: �� To evaluate the added value of a novel cardiovascular risk marker beyond traditional Framingham risk functions in the absence of longitudinal data by combining cross-sectional data and meta-analysis in a Markov model.  

Method: �� A Monte Carlo-Markov model was developed consisting of three health states: 1) alive and disease-free, 2) post-coronary heart disease (CHD) or stroke, and 3) death. Transition probabilities were calculated with the Framingham 30-year cardiovascular risk function, based on the traditional Framingham risk factors and took into account competing non-cardiovascular death risk. The Framingham risk function was updated with the CT coronary calcium score (CTCS) and three other novel risk markers using meta-analyses of the novel risk markers' independent hazard ratios and cross-sectional risk marker values of 3,736 asymptomatic National Health and Nutrition Examination Survey (NHANES) subjects. Events simulated with the Monte-Carlo Markov model with all 4 novel risk markers were used as a proxy for observed event rates.   �� For each NHANES subject, we calculated 10-year CHD risks using the Framingham risk function with and without CTCS. The original and updated predictions were used to classify and reclassify subjects into: <10% (low), 10-20% (intermediate) or ≥20% (high) risk categories. We subsequently simulated the 10- and 30-year CHD-free survival. We constructed reclassification tables and calculated the net reclassification improvement (NRI) and Harrell's C-statistic using simulated time-to-event data. To take into account parameter uncertainty, hazard ratios of novel risk markers were randomly sampled from lognormal distributions.  

Results: �� Of the 3,736 subjects (median age 53 years ICR 46 - 63, 48% male), 2,999 (80%) were at low, 525 (14%) at intermediate, and 212 (6%) at high CHD risk. Reclassification occurred most often in those at intermediate CHD risk: 205 (39%) moved to the low and 143 (27%) moved to the high CHD risk category with the updated risk function (Table). In these subjects, the simulated 10 (and 30)-year CHD risk was 5.6% (24.4%) and 30.8% (66.8%), respectively. The NRI was 0.20 (95%CI -0.07 - 0.42) and the C-statistic increased from 0.84 (95%CI 0.81-0.86) to 0.87 (95%CI 0.82-0.89) by adding CTCS to the Framingham risk function.  

Conclusion: �� In absence of longitudinal data, the added predictive value of a novel risk marker can be evaluated using a Monte Carlo-Markov model combining cross-sectional data and meta-analysis. �