A Novel Non-invasive ECG-based Method for Predicting Adverse Cardiovascular Outcomes

12.8.2009  SPEAKER: Collin M. Stultz, MD, PhD, Principal Investigator, Research Laboratory of Electronics and W. M. Keck Associate Professor of Biomedical Engineering, MIT MODERATOR: John Guttag, PhD, Dugald C. Jackson Professor, MIT Department of Electrical Engineering and Computer Science

Forum Summary

Cardiovascular disease causes five hundred thousand deaths per year in the United States and results in annual expenditures of approximately $450 billion.  Someone in this country has a heart attack every thirty seconds, and most of these heart attacks are related to atherosclerosis, commonly referred to as hardening of the arteries.  Everyone has some degree of atherosclerosis, and the medical community is not very good at predicting whose atherosclerosis will be relatively benign and whose will be deadly.  The goal of researchers led by Collin Stultz is to develop predictive models to identify patients at high risk for adverse cardiac events.

Atherosclerosis describes the formation of cholesterol-filled plaques in major arteries, such as the coronary arteries supplying the heart.  Each of these fatty plaques is covered by a fibrous cap, and sometimes these caps rupture, leading to the formation of a blood clot, which can occlude the artery.  If a coronary artery is suddenly occluded by a clot formed after the rupture of an atherosclerotic plaque, a heart attack is usually the result.   The events leading to the rupture of a plaque’s cap are poorly understood.

Whenever someone is admitted to the emergency room after a heart attack, an electrocardiogram (ECG) is obtained.  The ECG measures the electrical activity of the heart and provides information related to myocardial health, electrical conduction, and autonomic stimulation.  In addition to helping clinicians treat patients immediately, an ECG can be analyzed for heart rate variability, which can be a predictor of future heart problems.

Instead of looking at heart rate variability, Stultz’s group is investigating more subtle, morphology-based ECG parameters in the hopes of improving risk stratification for patients with cardiovascular disease.  Their approach is based on morphological variability, which quantifies the difference between consecutive heartbeats.  Morphological variability is calculated from a time series of morphological distances, each of which measures the difference between two consecutive beats.  Stultz and his colleagues found that morphological variability is a statistically significant predictor of cardiac death in many patient subgroups, even after corrections are made for confounding variables. 

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