Close Window
Aetna Aetna
Clinical Policy Bulletin:
Signal-Averaged Electrocardiography (SAECG)
Number: 0664


Policy

  1. Aetna considers signal-averaged electrocardiography experimental and investigational because no prospective clinical studies have demonstrated the utility of this testing in improving clinical outcomes.

  2. Aetna considers remote algorithmic analysis of electrocardiographic-derived data (Premier Heart's Multifunction Cardiogram (MCG); also known as 3DMP Computerized EKG System) experimental and investigational because the clinical value of the system in managing persons suspected of having significant coronary artery disease has not been established.

See also CPB 0579 - T-Wave Alternans.



Background

Signal-averaged electrocardiography (SAECG) is a technique involving computerized analysis of segments of a standard electrocardiogram that allows the detection of ventricular late potentials.  Ventricular late potentials in patients with cardiac abnormalities, especially coronary artery disease or following an acute myocardial infarction (MI), have been associated with an increased risk of ventricular tachyarrhythmias and sudden cardiac death.  Proponents of SAECG claim that it can obviate the need for invasive techniques commonly used to identify high-risk patients for interventions that treat or prevent ventricular tachyarrhythmia and sudden death.

An Agency for Healthcare Policy and Research's assessment (AHCPR, 1998) found that the current data on SAECG show relatively consistent high negative-predictive values, poor positive-predictive values, and variable sensitivity and specificity when the technique is used on patients with cardiomyopathy or following a MI.   However, the high negative- predictive value of SAECG is largely due to the fact that the incidence of fatal arrhythmic events among post-MI patients is now below 10 %.  The incidence of fatal arrhythmias has declined among post-MI patients, a large percentage of whom are on anti-thrombotic therapy, most likely following the trend of decreased mortality rate following MI.

In 1996, an American College of Cardiology (ACC) consensus statement on SAECG concluded that SAECG has “established value” in assessing the risk of development of sustained ventricular arrhythmias in patients recovering from MI.  However, subsequently published guidelines from the ACC on management of acute MI (1999) stated that the usefulness of SAECG for risk assessment after MI is less well-established by evidence/opinion.  In addition, subsequently published ACC guidelines on implantable anti-arrhythmia devices (1998) do not recommend SAECG for selecting patients for automated implantable cardioverter defibrillators (AICDs).

Although it has been proposed that SAECG may be used to select post-MI patients for anti-arrhythmic drugs or AICD implantation, there are no prospective clinical studies demonstrating the clinical utility of SAECG in selecting patients for these therapies.  In addition, there are no prospective clinical studies proving that SAECG can be used successfully to select patients for electrophysiologic studies or Holter monitoring, or to use SAECG for risk stratification in lieu of these other tests.

Grimm et al (2003) studied arrhythmia risk stratification with regard to prophylactic implantable cardioverter-defibrillator patients with in idiopathic dilated cardiomyopathy (IDC).  These researchers concluded that reduced left ventricular ejection fraction (LVEF) and lack of beta-blocker use are important arrhythmia risk predictors in IDC, whereas SAECG, baroreflex sensitivity, heart rate variability, and T-wave alternans do not seem to be helpful for arrhythmia risk stratification.  Furthermore, in a review on electrocardiographic arrhythmia risk testing, Engel et al (2004) evaluated the various electrocardiographic (ECG) techniques that appear to have potential in assessment of risk for arrhythmia.  The resting ECG (premature ventricular contractions, QRS duration, damage scores, QT dispersion, and ST segment and T wave abnormalities), T-wave alternans, late potentials identified on SAECG, and heart rate variability were explored.  The authors stated that unequivocal evidence to support the widespread use of any single non-invasive technique is lacking; further research in this area is needed.

Guidelines from the European Society for Cardiology (Brignole, et al., 2004) concluded that the systematic use of SAECG in syncope is “not recommended.”

The Premier Heart digital database-driven multi-phase (3DMP) electrocardiograph (EKG) System provides a computer analysis of digitalized 12-lead EKG waveforms in the frequency domain (power spectral estimate) to aid in the detection of significant coronary artery disease.  The 3DMP system was cleared by the FDA based on a 510(k) application.  Weiss et al (2002) reported on a cross-sectional analysis of the use of the 3DMP system in 136 patients with symptoms of potential coronary artery disease who were scheduled for angiography.  Originally, 200 patients were selected for the study, but 64 of the patients were not included in the study because of various technical problems in their 3DMP readings.

Although the 3DMP system was positive for CAD in 76 of 78 patients with greater than 60 % narrowing by angiography, the 3DMP system also read positive in 8 of 12 patients with 40 to 60 % narrowing.  None of the 10 patients with greater than 0 to 40 % narrowing read as positive by the 3DMP system, but 8 of 36 patients with 0 % narrowing read as positive for CAD.

As a significant number (2 of 78) of patients with significant angiographic lesions were missed by the 3DMP system, it is not clear that the device is sufficiently accurate to either be used in lieu of angiography or to select patients for angiography.

There are no evidence-based guidelines from national professional organizations that address the clinical utility of 3DMP in evaluating patients suspected of having coronary artery disease.  Prospective clinical studies are necessary to demonstrate the clinical utility of the 3DMP system in managing patients suspected of having significant coronary artery disease.

A technology assessment prepared for the AHCPR on ECG-based signal analysis technologies (Coeytaux, et al., 2010) stated that the reliability and test performance of 3DMP in subjects at high-risk or with known CAD is promising.  The horizon scan identified 7 potentially relevant devices, including 3 that use body surface mapping and 1 that uses mathematical signal analysis.  Of the 7 devices, only the PRIME ECG by Heartscape Technologies (body surface mapping) and the 3DMP/MCG/ mfEMT by Premier Heart (mathematical signal analysis; referred to as the 3DMP) are cleared for marketing by the FDA and commercially available.  One body surface mapping device (Visual ECG/Cardio3KG by NewCardio) is commercially available but not cleared; the other devices are not commercially available.  The assessment concluded: "There is currently little available evidence that pertains to the utility of ECG-based signal analysis technologies as a diagnostic test among patients at low to intermediate risk of CAD who present in the outpatient setting with the chief complaint of chest pain.  The limited evidence that is available demonstrates proof of concept, particularly for the PRIME ECG and 3DMP devices.  Further research is needed to better characterize the performance characteristics of these devices to determine in what circumstances, if any, these devices might precede, replace, or add to the standard ECG for the diagnosis of CAD among patients who present with chest pain in the outpatient setting.  The randomized controlled trial (RCT) study design is best suited for evaluating the impact that ECG-based signal analysis technologies may have on clinical decision-making and patient outcomes, but there are indirect approaches that might be applied to answer these questions."

Tamaki and colleagues (2009) prospectively compared the predictive value of cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging for sudden cardiac death (SCD) with that of the SAECG, heart rate variability (HRV), and QT dispersion in patients with chronic heart failure (CHF).  At entry, cardiac MIBG imaging, SAECG, 24-hr Holter monitoring, and standard 12-lead ECG were performed in 106 consecutive stable CHF outpatients with a radionuclide LVEF less than 40 %.  The cardiac MIBG washout rate (WR) was obtained from MIBG imaging.  Furthermore, the time and frequency domain HRV parameters were calculated from 24-hr Holter recordings, and QT dispersion was measured from the 12-lead ECG.  During a follow-up period of 65 +/- 31 months, 18 of 106 patients died suddenly.  A multi-variate Cox analysis revealed that WR and LVEF were significantly and independently associated with SCD, whereas the SAECG, HRV parameters, or QT dispersion were not.  Patients with an abnormal WR (greater than 27 %) had a significantly higher risk of SCD (adjusted hazard ratio: 4.79, 95 % confidence interval: 1.55 to 14.76).  Even when confined to the patients with LVEF greater than 35 %, SCD was significantly more frequently observed in the patients with than without an abnormal WR (p = 0.02).  The authors concluded that cardiac MIBG WR, but not SAECG, HRV, or QT dispersion, is a powerful predictor of SCD in patients with mild-to-moderate CHF, independently of LVEF.

Park and colleagues (2009) examined the correlation between parameters of 2-dimensional ECG and SAECG in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC).  A total of 33 patients (13 females, 40.3 +/- 14.4 years old) were included in this study.  Both the right and left ventricular dimensions and systolic function were assessed with 2-dimensional ECG.  The SAECG was performed with high-gain amplification and filtered using bi-directional Butterworth filters between 40 and 250 Hz.  The right ventricular (RV) outflow tract was the most frequently (n = 18, 54 %) involved segment.  Six (18 %) patients had only mildly decreased RV systolic function.  All the other patients had normal RV systolic function.  Although localized left ventricular wall motion abnormalities were observed in 14 (42 %) patients, the LVEF was normal in most (n = 32, 97 %).  Late potentials were positive in 22 (63 %) patients.  There was no significant correlation between parameters of the SAECG and 2-dimensional ECG for the entire patient population.  The authors concluded that the SAECG parameters exhibited no correlation to any of 2-dimensional ECG parameters in the patients with ARVC.  Fragmented electrical activity may develop with no significant relation to the anatomical changes in the patients with ARVC.

The Agency for Healthcare Research and Quality's systematic review of ECG-based signal analysis technologies for evaluating patients with acute coronary syndrome (Coeytaux et al, 2012) concluded that “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”.

 
CPT Codes / HCPCS Codes / ICD-9 Codes
CPT codes not covered for indications listed in the CPB:
0206T
93278
Other CPT codes related to the CPB:
93000 - 93010
Other ICD-9 codes related to the CPB:
393 - 429.9 Chronic rheumatic heart disease, hypertensive disease, ischemic heart disease, diseases of pulmonary circulation, and other forms of heart disease
V45.02 Automatic implantable cardiac defibrillator
V58.61 Long term (current) use of anticoagulants


The above policy is based on the following references:
  1. Cain ME, Anderson JL, Arnsdorf MF, et al. Signal-averaged electrocardiography. ACC Expert Consensus Document. JACC J Am Col Cardiol. 1996;27(1):238-249.
  2. U.S. Department of Health and Human Services, Public Health Service, Agency for Healthcare Policy and Research (AHCPR). Signal-averaged electrocardiography. Health Technology Assessment No. 11. AHCPR Pub. No. 98-0020. Rockville, MD: AHCPR; May 1998.
  3. Gregoratos G, Cheitlin MD, Conill A, et al. ACC/AHA guidelines for implantation of cardiac pacemakers and antiarrhythmia devices: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Pacemaker Implantation). J Am Coll Cardiol. 1998;31(5):1175-1209.
  4. Ryan TJ, Antman EM, Brooks NH, et al. 1999 update: ACC/AHA guidelines for the management of patients with acute myocardial infarction. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 1999;34(3):890-911.
  5. Weiss MB, Narasimhadevara SM, Feng GQ, Shen JT. Computer-enhanced frequency-domain and 12-lead electrocardiography accurately detect abnormalities consistent with obstructive and nonobstructive coronary artery disease. Heart Dis. 2002;4(1):2-12.
  6. U.S. Food and Drug Administration (FDA), Center for Devices and Radiologic Health (CDRH).  Cardiotron multi-phase EKG information analysis system. 510(k) no. K992703. Rockville, MD: FDA; March 21, 2000.
  7. Grimm W, Christ M, Bach J, et al. Noninvasive arrhythmia risk stratification in idiopathic dilated cardiomyopathy: Results of the Marburg Cardiomyopathy Study. Circulation. 2003;108(23):2883-2891.
  8. Engel G, Beckerman JG, Froelicher VF, et al. Electrocardiographic arrhythmia risk testing. Curr Probl Cardiol. 2004;29(7):365-432.
  9. Hunt SA, Baker DW, Chin MH, et al. ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult. Bethesda, MD: American College of Cardiology Foundation (ACCF); September 2001.
  10. Husser D, Stridh M, Sornmo L, et al. Analysis of the surface electrocardiogram for monitoring and predicting antiarrhythmic drug effects in atrial fibrillation. Cardiovasc Drugs Ther. 2004;18(5):377-386.
  11. Priori SG, Aliot E, Blomstrom-Lundqvist C, et al. Task Force on Sudden Cardiac Death of the European Society of Cardiology. Eur Heart J. 2001;22(16):1374-450.
  12. Brignole M, Alboni P, Benditt DG, et al. Guidelines on management (diagnosis and treatment) of syncope--update 2004. Europace. 2004;6(6):467-537.
  13. Bennhagen RG, Sornmo L, Pahlm O, Pesonen E. Serial signal-averaged electrocardiography in children after cardiac transplantation. Pediatr Transplant. 2005;9(6):773-779.
  14. Jaroszynski AJ, Glowniak A, Sodolski T, et al. Effect of haemodialysis on signal-averaged electrocardiogram P-wave parameters. Nephrol Dial Transplant. 2006;21(2):425-430.
  15. Haghjoo M, Arya A, Parsaie M, et al. Does the abnormal signal-averaged electrocardiogram predict future appropriate therapy in patients with implantable cardioverter-defibrillators? J Electrocardiol. 2006;39(2):150-155. 
  16. Horenstein MS, Idriss SF, Hamilton RM, et al. Efficacy of signal-averaged electrocardiography in the young orthotopic heart transplant patient to detect allograft rejection. Pediatr Cardiol. 2006;27(5):589-593.
  17. Omeroglu RE, Olgar S, Nisli K. Signal-averaged electrocardiogram may be a beneficial prognostic procedure in the postoperative follow-up tetralogy of fallot patients to determine the risk of ventricular arrhythmias. Pediatr Cardiol. 2007;28(3):208-212.
  18. Schoenenberger AW, Erne P, Ammann S, et al. Prediction of arrhythmic events after myocardial infarction based on signal-averaged electrocardiogram and ejection fraction. Pacing Clin Electrophysiol. 2008;31(2):221-228.
  19. Grube E, Bootsveld A, Buellesfeld L, et al. Computerized two-lead resting ECG analysis for the detection of coronary artery stenosis after coronary revascularization. Int J Med Sci. 2008;5(2):50-61.
  20. Tamaki S, Yamada T, Okuyama Y, et al. Cardiac iodine-123 metaiodobenzylguanidine imaging predicts sudden cardiac death independently of left ventricular ejection fraction in patients with chronic heart failure and left ventricular systolic dysfunction: Results from a comparative study with signal-averaged electrocardiogram, heart rate variability, and QT dispersion. J Am Coll Cardiol. 2009;53(5):426-435.
  21. Park Y, Cho Y, Lee DY, et al. Correlation between the parameters of signal-averaged ECG and two-dimensional echocardiography in patients with arrhythmogenic right ventricular cardiomyopathy. Ann Noninvasive Electrocardiol. 2009;14(1):50-56.
  22. Strobeck JE, Shen JT, Singh B, et al. Comparison of a two-lead, computerized, resting ECG signal analysis device, the MultiFunction-CardioGram or MCG (a.k.a. 3DMP), to quantitative coronary angiography for the detection of relevant coronary artery stenosis (>70%) - a meta-analysis of all published trials performed and analyzed in the US. Int J Med Sci. 2009;6(4):143-155.
  23. Urbanova D, Urban L, Mikuskova E, et al. Frequency-domain analysis of the signal-averaged electrocardiogram in hematological malignancies survivors. Bratisl Lek Listy. 2010;111(3):144-149.
  24. Coeytaux RR, Williams JW, Chung E, Gharacholou M. ECG-based signal analysis technologies. Technology Assessment. Prepared for the Agency for Healthcare Research and Quality (AHRQ) by the Duke Evidence-based Practice Center (Contract No. HHSA 290-2007-10066I). Rockville, MD: AHRQ; May 24, 2010. Available at: http://www.cms.gov/determinationprocess/downloads/id73TA.pdf. Accessed August 4, 2010.
  25. Coeytaux RR, Leisy PJ, Wagner GS, et al.  Systematic review of ECG-based signal analysis technologies for evaluating patients with acute coronary syndrome. Agency for Healthcare Research and Quality's (AHRQ): Rockville, MD. June 2012. Available at: http://www.cms.gov/Medicare/Coverage/DeterminationProcess/downloads/id83TA-1.pdf. Accessed July 12, 2013.
  26. Proclemer A, Lewalter T, Bongiorni MG, et al; conducted by the Scientific Initiative Committee, European Heart Rhythm Association. Screening and risk evaluation for sudden cardiac death in ischaemic and non-ischaemic cardiomyopathy: Results of the European Heart Rhythm Association survey. Europace. 2013;15(7):1059-1062.


email this page   


Copyright Aetna Inc. All rights reserved. Clinical Policy Bulletins are developed by Aetna to assist in administering plan benefits and constitute neither offers of coverage nor medical advice. This Clinical Policy Bulletin contains only a partial, general description of plan or program benefits and does not constitute a contract. Aetna does not provide health care services and, therefore, cannot guarantee any results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Aetna or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. This Clinical Policy Bulletin may be updated and therefore is subject to change.
Aetna
Back to top