Computationally Generated Cardiac Biomarkers for Risk Stratification After Acute Coronary Syndrome

Computational biomarkers extracted by machine learning from electrocardiograms improve identification of high-risk patients after coronary events. 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. The existing tools for estimating the risk of death in patients after they experience acute coronary syndrome are commonly based on echocardiography and clinical risk scores (for example, the TIMI risk score). These identify a small group of high-risk patients who account for only a minority of the deaths that occur in patients after acute coronary syndrome. Here, we investigated the use of three computationally generated cardiac biomarkers for risk stratification in this population: morphologic variability (MV), symbolic mismatch (SM), and heart rate motifs (HRM). We derived these biomarkers from time-series analyses of continuous electrocardiographic data collected from patients in the TIMI-DISPERSE2 clinical trial through machine learning and data mining methods designed to extract information that is difficult to visualize directly in these data. We evaluated these biomarkers in a blinded, prespecified, and fully automated study on more than 4500 patients in the MERLIN-TIMI36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non–ST-Elevation Acute Coronary Syndrome–Thrombolysis in Myocardial Infarction 36) clinical trial. Our results showed a strong association between all three computationally generated cardiac biomarkers and cardiovascular death in the MERLIN-TIMI36 trial over a 2-year period after acute coronary syndrome. Moreover, the information in each of these biomarkers was independent of the information in the others and independent of the information provided by existing clinical risk scores, electrocardiographic metrics, and echocardiography. The addition of MV, SM, and HRM to existing metrics significantly improved model discrimination, as well as the precision and recall of prediction rules based on left ventricular ejection fraction. These biomarkers can be extracted from data that are routinely captured from patients with acute coronary syndrome and will allow for more accurate risk stratification and potentially for better patient treatment.