By RAFFI B. SHEN, NORBERT RAINFORD, MD, AND JOSEPH T. SHEN, MD 

 Coronary artery heart disease (CAD) is a leading, major cause of death and disability in developed countries, responsible for around one-third of all deaths among individuals over the age of 35 (1-3). About half of all middle-aged men and one-third of middle-aged women in the United States will develop some manifestation of CAD in their lifetime (4) with several unrecognized drivers and causes of the persistence of this morbidity despite our general knowledge of major risk factors based on population data such as the Framingham Heart Study. The lack of adaptable, inexpensive, noninvasive, and accurate modalities to detect CAD in its early stages as well as the lack of potent monitoring of the effects of diet and other lifestyle interventions has been a major factor in this information gap.  

The current state of diagnostic testing is dismally poor, with accuracy from both normal ECG testing, of less than 15-30 percent, and other “gold standard” modalities such as stress testing, nuclear scintigraphy, stress echocardiography, and other various types of noninvasive cardiac stress imaging stress tests, 38-40 percent either missing patients with critical issues entirely, or misattributing diagnosis leading to unacceptably high false positive rates, leading to a significant number of patients undergoing unnecessary coronary angiography of nearly 60 percent. Patients who are given a false positive diagnosis are exposed to potential risks involved with invasive procedures and radiation exposure without expected commensurate clinical benefit because of this inadequacy (5). Women, more than men, die from cardiovascular disease simply due to the lack of a better tool. Compounding this, there is an emerging consensus of the role of non-obstructive coronary disease and microvascular disease being the cause of clinical manifestation of ischemic heart disease, further burying the relevance of conventional testing for obstructive CAD.   

A review in Circulation, 1995, by Erlin Falk demonstrated that the progression to plaque rupture and myocardial infarction (MI) over time occurs most frequently in patients with obstruction of 50 percent or less (6), far lesser than what most current conventional tools would be generally able to detect as a “problem.” To put it lightly, between the normal ECG testing rates having 30 percent accuracy at best, and the other “gold standards” maybe reaching 40 percent, that means all the greatest tools of our arsenal are worse than a flip of a coin.  

It is within this medical and industrial context, that we address this deficiency in our field by introducing a viable alternative in the form of the Multifunction Cardiogram (MCG) (7), a noninvasive, bedside, radiation and drug free diagnostic tool that requires no stress or strenuous physical activity from the patient to quantitatively assess lesions across all stages of the disease from microvascular level, early non-obstructive to later stages of significantly obstructive spectrum, capable of monitoring the effectiveness of any form of therapeutic intervention quickly in 10 minutes at bedside, affordably, and without the usual bounds of human error.   

After 20 years and two generations of mathematicians, computer engineers, and physicians, MCG Technology stands as the first embodiment of a mathematical and empirical application of systems theory to a dynamic biological environment, expressing the physiological state of the heart with a primary focus on the level of its ischemic burden, regardless the cause, whether it is large coronary artery obstruction or microvascular metabolic disease. The digital, deep machine learning platform is capable of describing the functional, dynamic state of the heart without relying on merely anatomical information.  

The development team’s greatest priority has been ischemic burden measurement, but additional markers needed to be used to describe the heart within a functional/physiological context, and to do that, the mathematical expressions of the communication between two standard ECG leads over multiple cycles was used, converting the digitized signals into a frequency domain via multiple nonlinear mathematical functions, thus the term “multifunction” cardiography.  

This, combined with systems analysis principles, advanced and proven digital signal processing methods, Lagrangian Mechanics Mathematics, empirical clinical data-lining, evidence driven deep machine learning, and specialty artificial intelligence algorithms have created, to our knowledge, the first example of a commercially available information technology solution in the discipline of “Clinical Computational Electrophysiology” (7). 

MCG Technology fills the knowledge gap left empty by other diagnostic tools, able to deliver high levels of unprecedented diagnostic accuracy, ranging from 89 percent to as high as 100 percent accuracy for patients affected by heart diseases at all stages, from its earliest to its latest (8-21). 

Compare this, to the 10-year, $100 million Ischemia trial (22), in which around 15 percent of patients alone included in both the interventional and conservative treatment arms of this landmark study, using the best of the best tools in diagnostic cardiology’s currently available arsenal, still suffered Major Adverse Cardiac Events (MACE). If even the gold standard and most expensive available diagnostic tools from the best institutions can still have such an unacceptably high rate of MACE, then perhaps it is time for a new 100 percent empirical evidence based diagnostic platform to step in, one that doesn’t require assumptions driven by “expert opinions.” This is probably the most important difference between MCG Technology and the rest of the mainstream diagnostic tools.   

This technology presents a significant opportunity to early detect and timely monitor patients at risk of suffering MACE, especially those prone for sudden cardiac death, enabling clinicians to identify, treat, and reverse the deadly disease trends of these at-risk patients, and more than likely save their lives.  

Joseph T. Shen, MD is the World’s First Clinical Computational Electrophysiologist. He is also the Multifunction Cardiograph Technology Pioneer and Developer.  He is currently serving as the director of Premier Heart. 

References: 

1. Rosamond W, Flegal K, Furie K, et al. Heart disease and stroke statistics–2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008;117:e
2. Nichols M, Townsend N, Scarborough P, Rayner M. Cardiovascular disease in Europe 2014: epidemiological update. Eur. Heart J. 2014;35:2950.
3. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137:e
4. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary angiography. N Engl J Med. 2010;362:886-895.
5. von Bertalanffy’s L. General System Theory: Foundations, Development, Applications (Revised Edition) ISBN-10: 0807604534
6. | ISBN-13: 978-0807604533. New York, NY: George Braziller; March 17, 1969.
7. | ISBN-13: 978-1-975115_28-9 Personalized and Precision Integrative Cardiovascular Medicine, Chapter 12
8. Weiss WB, NarasimhadevaraSM, Feng GQ, Shen JT. Computer- enhanced frequency-domain and 12-lead electrocardiography accurately detect abnormalities consistent with obstructive and non-obstructive coronary artery disease. Heart Dis. 2002;4:2-12. 
9. Grube E, BootsveldA, Yuecel S, et al. Computerized two-lead resting electro-myocardium analysis for the detection of coronary artery stenosis. Int J Med Sci. 2007;4:249-263. 
10. Grube E, BootsveldA, Buellesfeld L, et al. Computerized two-lead resting electro-myocardium analysis for the detection of coronary artery stenosis after coronary revascularization. Int J Med Sci. 2008;5(2):50-61. 
11. Hosokawa J, Shen JT, Imhoff M. Computerized 2-lead resting ECG analysis for the detection of relevant coronary artery stenosis in comparison with angiographic findings. Congest Heart Fail. 2008;14:251-260.
12. Strobeck JE, Shen JT, Singh B, et al. Comparison of a two-lead, computerized, resting ECG signal analysis device, the MultiFunction- CardioGramsor MCG (a.k.a. 3DMP), to quantitative coronary angiography for the detection of relevant coronary artery stenosis (>70 percent) – a meta-analysis of all published trials performed and analyzed in the US. Int J Med Sci. 2009;6:143-155. 
13. Strobeck JE, Mangieri A, Rainford N, et al. A paired-comparison of the MultiFunctionCardiogram (MCG) and sestamibi SPECT myocardial perfusion imaging (MPI) to quantitative coronary angiography for the detection of relevant coronary artery obstruction (≥70 percent) – a single-center study of 116 consecutive patients referred for coronary angiography. Int J Med Sci. 2011;8(8):717-724. 
14. Strobeck JE, Rainford N, Arkus B, Imhoff M. Comparing Multifunction-Cardiogram and Coronary Angiography for Detection of Hemodynamically Relevant Coronary Artery Stenosis (>70 percent) in Women. Treat Strategy. 2010:83.
15. Amano T, Shinoda N, Kunimura A, et al. Non-invasive assessment of functionally significant coronary stenoses through mathematical analysis of spectral ECG components. BMJ/Open Heart. 2014;1:e doi:10.1136/openhrt-2014-000144.
16. Takeshita M, Shinoda N, Takashima H, et al. Noninvasive mathematical analysis of spectral electrocardiographic components for coronary lesions of intermediate to obstructive stenosis severity–relationship with classic and functional SYNTAX score. Catheter Cardiovasc Interv. 2015;86(1):21-29. doi:10.1002/ccd.25924.
17. Tetsuya A, Norihiro S, Hiroaki T, et al. Impact of noninvasive mathematical analysis of spectral electrocardiographic components on the prediction of recurrent cardiac ischemic events after coronary intervention, An Abstract submitted for 2015 PCI TCT San Francisco.
18. KawajiT, Shiomi H, Morimoto T, et al. Noninvasive detection of functional myocardial ischemia: multifunction cardiogram evaluation in the diagnosis of functional coronary ischemia study. Ann Noninvasive Electrocardiol. 2015;20(5):446-453. 
19. Imhoff M, Rainford N. It all depends on your references: electrophysiology compared to angiography. Ann Noninvasive Electrocardiol. 2015;20(5):506-507.
20. Supplement Annals of Emergency Medicine Volume 76 Number 44 October 2020 Anabstract# 97- Evaluation of Multifunction Cardiogram for low risks patients press tin g to the emergency room with chest pains. 
21. https://www.ischemiatrial.org/ischemia-study-results-presented-aha-2019

           https://www.ischemiatrial.org/ischemia-study-results-presented-aha-2019