Why Cardiac Biomarkers Don’t Help Predict Heart Disease

Why does this happen? Why do these promising biomarkers, clearly associated with bad outcomes, fail to improve our ability to predict the future? I already gave one example, which has to do with how the markers are distributed in the population. But even more relevant here is that the new markers will only improve prediction insofar as they are not already represented in the old predictive model.

Of course, BNP, for example, wasn’t in the old model. But smoking was. Diabetes was. Blood pressure was. All of that data might actually tell you something about the patient’s BNP through their mutual correlation. And improvement in prediction requires new information.

This is actually why I consider this a really successful study. We need to do studies like this to help us find what those new sources of information might be. It doesn’t seem like these biomarkers will help us in our effort to risk-stratify people. So, we move on to other domains. Perhaps social determinants of health would improve risk prediction. Perhaps insurance status. Perhaps environmental exposures. Perhaps markers of stress.

We will never get to a C-statistic of 1. Perfect prediction is the domain of palm readers and astrophysicists. But better prediction is always possible through data. The big question, of course, is which data?

Dr. Wilson is associate professor of medicine and public health and director of the Clinical and Translational Research Accelerator at Yale University, New Haven, Conn. He has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.