Risk stratification by analysis of electrocardiographic morphology following acute coronary syndromes
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505525431-MIT.pdf
Description
Full printable version
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684.93 KB
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bb0d182f9b9a680ccdd4664f4478fc87
Author(s)
Sung, Philip Pohong
Advisor(s)
John V. Guttag.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
Patients who have suffered an acute coronary syndrome (ACS) are at elevated risk of future adverse events, including fatal arrhythmias or myocardial infarction. Risk stratification--he identification of high-risk patients--s an important step in determining who is most likely to benefit from aggressive treatments. We propose a new automated risk stratification technique that uses the long-term electrocardiographic data routinely recorded in the days following an ACS. Data obtained from clinical drug trials indicates that our technique, called MV-DF (morphologic variability diagnostic frequencies), can significantly improve prognostication for ACS patients. Patients with MV-DF values in the highest quartile show a more than five-fold elevated risk of death in the 90 days following a non-ST-elevation ACS. We also propose techniques to construct models of the dynamics of cardiac behavior. Preliminary results suggest that such techniques may be useful for short-term prediction of fatal arrhythmias. Our results suggest that long-term ECG-based risk assessment techniques--n particular, methods incorporating information about morphologic variability--re an effective and practical way to select appropriate treatment options for cardiovascular disease patients.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 95-100).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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