Wednesday, October 17, 2012
The Atrium (Hyatt Regency)
Poster Board # 32
INFORMS (INF), Health Services, and Policy Research (HSP)
Candidate for the Lee B. Lusted Student Prize Competition
Bart S. Ferket, MD1, Bob J.H. van Kempen, MSc1, Ewout W. Steyerberg, PhD2, Oscar H. Franco, MD, PhD, FESC, MFPH3, Wendy Max, PhD4, Kirsten E. Fleischmann, MD, MPH5 and M.G. Myriam Hunink, MD, PhD6, (1)Erasmus MC, Rotterdam, Netherlands, (2)Department of Public Health, AE 236, Rotterdam, Netherlands, (3)Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands, (4)University of California, San Francisco, San Francisco, CA, (5)UCSF Medical Center, San Francisco, CA, (6)Erasmus University Medical Center, Rotterdam, Netherlands
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. 
