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Comparing Zero Coronary Artery Calcium With Other Negative Risk Factors for Coronary Heart Disease

Comparing Zero Coronary Artery Calcium With Other Negative Risk Factors for Coronary Heart Disease. A Novel Methodology: Risk-Adjusted Negative Likelihood Ratios Multi-Ethnic Study of Atherosclerosis (MESA).

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Comparing Zero Coronary Artery Calcium With Other Negative Risk Factors for Coronary Heart Disease

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  1. Comparing Zero Coronary Artery Calcium With Other Negative Risk Factors for Coronary Heart Disease A Novel Methodology: Risk-Adjusted Negative Likelihood Ratios Multi-Ethnic Study of Atherosclerosis (MESA) Michael J. Blaha1, Bill McEvoy1, Ron Blankstein2, Matthew J. Budoff3, Chris Sibley4, Moyses Szklo5, Richard Kronmal6, Roger S. Blumenthal1, Khurram Nasir1, 7 ** Author affiliations in acknowledgements

  2. Negative Risk Factors • Most novel biomarkers marginally improve risk prediction at population level, adding little for individual patient • Theme of reporting: risk factor X adds slightly increases predicted risk  more testing, more treatment needed! • Less attention is paid to “negative” risk factors despite tremendous potential public health implications • “Imaging Hypothesis” – due to high sensitivity, NPV >> PPV, potential value as negative risk factors

  3. Tools for Comparing Risk Factors • Survival analysis - HR • ROC Analysis – c-statistic • Net reclassification improvement (NRI) • Do not communicate change in “risk” to the clinician decision-maker Specific Aim: Adapt a methodology for calculating and comparing risk-adjusted LRs and apply to “negative risk factors” • Do not emulate Bayesian decision making Likelihood Ratios (LRs) – “Bayes Factors” Directly communicate the change in risk before and after knowledge of a new test result

  4. Methods: Risk-Adjusted Likelihood Ratios (Gu and Pepe 2009) logit Ppost-test = logit Ppre-test + log LR * ** *** METHODS * X = Framingham Risk Factors + race/ethnicity ** Y = Negative Risk Factor, i.e. CAC=0 *** Calculate estimated LR for each MESA participant, for negative risk factor

  5. Multi-Ethnic Study of Atherosclerosis ** • Multicenter study of 6,814 individuals free of known cardiovascular disease • Follow-up for All CHD events over mean 7.1 years

  6. Post-Test Risk vs. Pre-Test Risk (Augmented logistic model) Zero CAC * Linear fit (Baseline logistic model)

  7. Patient 1 Intermediate Risk White Man Pre-Test Risk 10% 55 years old Total cholesterol 200 mg/dL HDL 35 mg/dL Moderate treated hypertension Logit ppost = logit ppre + log LR CAC=0, post-test risk ~4%** 0.35 ** 10-year risk extrapolated from 7.1 year risk

  8. Important Covariates Influencing Likelihood Ratio for CAC=0 Age Pre-Test Risk

  9. Limitations – Pre-Test risk estimate What is the correct tool for estimating pre-test risk? • Very poor calibration of FRS in MESA  Recalibrated 10-year “MESA FRS” for All CHD • Therefore LRs immediately useful for MESA FRS, not traditional FRS Rescale factor = (MESA FRS/Traditional FRS) = 0.67  All CHD vs. Hard CHD Rescale factor = 0.40

  10. EXAMPLE USING CAC=0 Intermediate Risk AA Woman 70 years old Smoker Total cholesterol 240 mg/dL HDL 50 mg/dL Mild treated hypertension • MESA Risk All CHD = 10% • FRS Hard CHD Risk = 14% Likelihood Ratio if CAC=0 • MESA All CHD = 0.30 Rescaled Likelihood Ratio • FRS All CHD = 0.20 • FRS Hard CHD = 0.12 Logit ppost = logit ppre + log LR Post-test All CHD risk ~3% Post-test Hard CHD risk ~1.8%

  11. iJACC paper, Lancet paper

  12. Conclusions and Implications • The risk-adjusted likelihood ratio is a powerful, clinically-usable tool for comparing incremental value of risk factors • Imaging tests, specifically CAC=0, are strongest negative risk factors for CHD • CAC=0, which is present in 50% of MESA, appears to have a LR consistently in “clinically helpful” range

  13. Acknowledgements We wish to thank all the volunteer research participants who made this study possible. This research was supported by contracts R01 HL071739, N01-HC-95159 through N01-HC-95165, and N01-HC-95169 from the National Heart, Lung, and Blood Institute. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. Author Affiliations: 1 Johns Hopkins Ciccarone Center for Prevention of Heart Disease, Baltimore, MD 2 Brigham and Women's Hosp Non-invasive CV Imaging Program, Boston, MA 3 Division of Cardiology, Harbor-UCLA Medical Center, Torrance, CA 4 National Institutes of Health, Bethesda, MD 5 Johns Hopkins University, School of Public Health, Baltimore, MD 6 University of Washington, Seattle, WA 7 Yale University School of Medicine, New Haven, CT 13

  14. Coronary Calcium Distribution across Age groups Prevalence of coronary calcium increases with age.

  15. Mortality Rate (per 1000 person-years) With Increasing Coronary Artery Calcium Scores & Traditional Risk Factors Nasir K, Blaha MJ, et al. Circulation Outcomes. 2011

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