Research ArticleCardiovascular Disease

Computationally Generated Cardiac Biomarkers for Risk Stratification After Acute Coronary Syndrome

Science Translational Medicine  28 Sep 2011:
Vol. 3, Issue 102, pp. 102ra95
DOI: 10.1126/scitranslmed.3002557

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Better Biomarkers for Predicting Coronary Artery Disease-Related Death

The symptoms of a heart attack—chest pain, sweating, etc.—can actually signal several serious coronary events, collectively termed acute coronary syndrome. Some of these put patients at high risk for death and must be treated aggressively; others are of relatively lower risk. Our ability to correctly assign patients to one of these groups quickly and reliably is inadequate for optimal care. Syed and his colleagues have extracted three biomarkers, by computational methods, from the continuous EKG readings obtained from these patients in the hospital. When added to existing predictors, the three derived computational biomarkers improve the classification of acute coronary syndrome patients by 7 to 13%.

The biomarkers derived from the EKG do not correspond readily to features easily recognizable by an observer in the tracings of the EKG. But they are clearly visible to a computer using time-series algorithms on those same tracings. The first, morphologic variability, quantifies energy differences between consecutive heart beats. The second, symbolic mismatch, quantifies the difference in the EKG of a particular patient from others with the same clinical course. The third, heart rate motifs, determines patterns of heart rate in the EKG that reveal autonomic functioning.

The power of this approach is that these biomarkers can be extracted from clinical data that are already continuously collected from the patient. It can be done in line, in real time with no overt change in the patients’ clinical experience or the cost of care. This could lead to treatments that ultimately extend the lives of tens of thousands of patients.