Why Cardiac Biomarkers Don't Help Predict Heart Disease

Associate Professor, Department of Medicine, Yale School of Medicine; Director, Clinical and Translational Research Accelerator, Yale School of Medicine, New Haven, Connecticut, your weekly dose of commentary on a new medical study. I'm Dr F. Perry Wilson of the Yale School of Medicine.

A C-statistic of 0.5, or 50%, would mean the Perry Factor gives you no better results than a coin flip; it's chance. A C-statistic of 1 is perfect prediction. So, what will the C-statistic be, given the incredibly strong association of the Perry Factor with outcomes? 0.9? 0.95?I constructed my fake Perry Factor dataset quite carefully to illustrate this point. Let me show you what happened. What you see here is a breakdown of the patients in my fake study.

C-statistic as high as 0.82 when applied to Black women, a low of 0.71 when applied to Black men. Non-Black individuals are in the middle Of course, our old friend the pooled cohort risk equation is still here — in the background — requiring just that one blood test and measurement of blood pressure. Let's talk about predictive power.By adding troponin, BNP, and CRP to the equation, the new C-statistic is 0.819. Barely any change.

Source: Healthcare Press (healthcarepress.net)

Cardiovascular Risk CV Risk Factors Cardiovascular Risk Factors Cardiovascular Risk Management Heart Failure Biomarker Biological Marker Multi-Biomarker Disease Activity MBDA Multibiomarker Disease Activity Myocardial Infarction Statistics Heart Deep Venous Thrombosis DVT Deep Vein Thrombosis DVT - Deep Venous Thrombosis

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