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Systematic Review of ECG-based Signal Analysis Technologies for Evaluating Patients With Acute Coronary Syndrome
Technology Assessment Report
Remy R Coeytaux, MD, PhD, Philip J Leisy, BS, Galen S Wagner, MD, Amanda J McBroom, PhD, Cynthia L Green, PhD, Liz Wing, MA, R Julian Irvine, MCM, and Gillian D Sanders, PhD.
Author Information and AffiliationsStructured Abstract
Objectives:
To summarize the clinical and scientific evidence for commercially available ECG-based signal analysis technologies used or proposed to be used to evaluate patients at low to intermediate risk for coronary artery disease (CAD) who have chest pain or other symptoms suggestive of acute coronary syndrome (ACS).
Data Sources:
Searches of gray literature sources, MEDLINE®, EMBASE®, and the Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects.
Review Methods:
We conducted a systematic search of English-language literature to identify published evidence for ECG-based technologies that may improve the diagnosis of CAD and/or ACS through signal analysis or other forms of advanced data transformation. For inclusion, a technology had to be (1) a physical device that obtains and interprets information about the electrical activity of the heart in ways that are different from the standard 12-lead ECG, (2) cleared for marketing by the U.S. Food and Drug Administration (FDA) and commercially available in the United States with feasible implementation, (3) tested in patients at low to intermediate risk for CAD who have a clinical presentation consistent with ACS, and (4) reported in a peer-reviewed, published study that reports performance characteristics, effects on diagnostic or treatment decisions, or effects on patient outcomes. Reviewers worked in pairs to extract data, assess applicability, and evaluate the quality of each study. We used a bivariate random-effects generalized linear regression model to compute summary estimates of sensitivity and specificity.
Results:
We identified eight commercially available, FDA-cleared ECG-based devices proposed for use to diagnose CAD or detect ACS. Of these, published evidence meeting inclusion criteria was available for only two devices: PRIME ECG and LP 3000. PRIME ECG test performance was reported in 10 studies. Meta-analysis of eight of these studies determined a 68.4 percent sensitivity (95% CI, 35.1 to 89.7) and 91.4 percent specificity (CI, 83.6 to 95.7) for the PRIME ECG in detecting MI (with all but one study using elevated cardiac biomarkers as the reference standard) compared with 40.5 percent sensitivity (CI, 19.6 to 65.5) and 95.0 percent specificity (CI, 87.9 to 98.0) for the standard 12-lead ECG. Differences in test performance between the PRIME ECG and 12-lead ECG are not statistically significant as judged by the overlapping confidence intervals. A single study of LP 3000 demonstrated that QRS prolongation on the signal-averaging ECG was associated with a sensitivity and specificity of 70 percent and 89 percent, respectively, compared with 56 percent and 89 percent for ST changes detected by 12-lead ECG. The improved sensitivity of the signal-averaging ECG versus the 12-lead ECG in that study was statistically significant (p<0.01).
Conclusions:
Existing research is largely insufficient to confidently inform the appropriate use of ECG-based signal analysis technologies in diagnosing CAD and/or ACS. Further research is needed to better describe the performance characteristics of these devices to determine in what circumstances, if any, these devices might precede, replace, or add to the standard ECG in test strategies to identify clinically significant CAD in the patient population of interest. To fully assess the impact of these devices on diagnostic strategies for patients with chest pain, test performance needs to be linked to clinically important outcomes through modeling or longitudinal studies.
Contents
- Peer Reviewers
- Executive Summary
- Introduction
- Methods
- Results
- Discussion
- Summary and Conclusions
- References
- Abbreviations and Acronyms
- Appendix A Gray Literature Search Strategy
- Appendix B Search Terms
- Appendix C Inclusion and Exclusion Criteria
- Appendix D Quality Assessment Criteria and Ratings
- Appendix E Excluded Studies
Acknowledgements: The authors thank Connie Schardt, M.L.S., and Megan von Isenburg, M.S.L.S., for help with the literature search and retrieval.
This report is based on research conducted by the Duke Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. HHSA 290-2007-10066-I). The findings and conclusions in this document are those of the authors, who are responsible for its contents. The findings and conclusions do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of the Agency for Healthcare Research and Quality or of the U.S. Department of Health and Human Services.
The information in this report is intended to help health care decisionmakers, patients and clinicians, health system leaders, and policymakers make well-informed decisions and thereby improve the quality of health care services. This report is not intended to be a substitute for the application of clinical judgment. Decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information; i.e., in the context of available resources and circumstances presented by individual patients.
This report may be used, in whole or in part, as the basis for development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.
None of the investigators has any affiliations or financial involvement related to the material presented in this report.
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