Clinical magnetocardiography: the unshielded bet-past, present, and future

Magnetocardiography in the Evaluation of Sudden Cardiac Death Risk: A Systematic Review

Sudden cardiac death (SCD) is responsible for 15%-20% of deaths globally/year, predominantly due to ventricular arrhythmias (VA) caused by vulnerable cardiac substrate. Identifying those at risk has proved difficult with several limitations of current methods. We evaluated the evidence for magnetocardiography (MCG) in predicting SCD events. We searched Embase/Medline databases for English language papers evaluating MCG in patients at risk of VA. A total of 119 papers were screened with 27 papers included for analysis (23 case-controlled, four cohort studies); study sizes varied (n = 12 to 158). Etiology was ischemic cardiomyopathy (ICM) in 22, dilated cardiomyopathy in 2, arrhythmogenic cardiomyopathy in 1 and mixed in 2. In patients with ICM there were consistent discriminatory features seen using time-based and signal-complexity measures that persisted when evaluating the independence of these parameters. Current flow analysis demonstrated promising discriminatory results in other etiologies. The features studied support the role of MCG in identifying substrate for VA, particularly in ICM.

Development and validation of a clinical diagnostic model for myocardial ischaemia in borderline coronary lesions based on optical pumped magnetometer magnetocardiography: a prospective observational cohort study

OBJECTIVES

To develop and validate a clinical diagnostic model based on optical pumped magnetometer magnetocardiography (OPM-MCG) for the detection of myocardial ischaemia in patients with borderline coronary lesions prior to invasive coronary angiography (ICA).

DESIGN

Prospective observational cohort study.

SETTING

Single centre of the China National Clinical Research Centre for Cardiovascular Disease (NCCMRC).

PARTICIPANTS

Adults with borderline coronary lesions on ICA (n=141).

INTERVENTIONS

Underwent OPM-MCG before ICA and fractional flow reserve measurement.

RESULTS

Five parameters were included in the final diagnostic model: MAgmax-TT, δDtsum-PN, δAgsum-C, δArsum-N and δArmin-N. 1000 bootstrap replications showed that the area under the receiver operating characteristic curve and 95% CI of the diagnostic model were 0.864 (0.803-0.925), with a sensitivity of 79.4%, specificity of 80.8%, positive predictive value of 79.4% and negative predictive value of 80.8%. Decision curve analysis showed a net benefit from the predictive model when the threshold probability of an ischaemic patient was >12%, suggesting the potential utility of the model in the real world.

CONCLUSIONS

A nomogram based on five OPM-MCG parameters was developed to assess myocardial ischaemia in patients with borderline coronary lesions and has the potential to reduce the need for unnecessary ICA.

TRIAL REGISTRATION NUMBER

China Clinical Trial Registry (ChiCTR2300072382).

Harnessing the Heart's Magnetic Field for Advanced Diagnostic Techniques

Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart's magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart's magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology.

National cost savings, operational and safety benefits from use of magnetocardiography in the assessment of emergency department chest pain patients

Study objectives

Patients frequently present to the emergency department (ED) with chest pain requiring further risk stratification. Traditional cardiac diagnostics such as stress testing may expose patients to ionizing radiation, may not be readily available, may take significant time for testing and interpretation, and adds cost to the workup. Magnetocardiography (MCG) is an alternative approach to assess candidates more quickly and efficiently than routine downstream testing.

Design

We created and ran 1000 trials of a Monte Carlo simulation. Using this simulation, we modeled the national annual impact by averting further cardiac diagnostics.

Setting

All EDs in the United States.

Participants

All ED adult patients with chest pain.

Interventions

Simulated use of MCG to reduce avoidable downstream cardiac diagnostics.

Main outcome measures

Our primary outcome was to estimate the impact of an MCG-first strategy on the annual national cost savings among eligible patients in the ED. Our secondary outcomes were the estimated reduction in short-stay hospitalizations, cancer cases, and cancer deaths due to radiation exposure.

Results

An MCG-first strategy was estimated to save a mean (±SD) of $574 million (±$175 million) by avoiding 555,000 (±93,000) downstream cardiac diagnostic tests. This resulted in a national annual cumulative decrease of 500,000 (±84,000) hospitalizations, 7,600,000 (±1,500,000) bed hours, 409 (±110) new cancer diagnoses, and 210 (±56) new cancer deaths due to radiation exposure from avoidable cardiac diagnostics.

Conclusions

If adopted widely and used consistently, an MCG-first strategy among eligible patients could yield substantial benefits by averting avoidable cardiac diagnostic testing.

The magnetocardiogram

The magnetic field produced by the heart's electrical activity is called the magnetocardiogram (MCG). The first 20 years of MCG research established most of the concepts, instrumentation, and computational algorithms in the field. Additional insights into fundamental mechanisms of biomagnetism were gained by studying isolated hearts or even isolated pieces of cardiac tissue. Much effort has gone into calculating the MCG using computer models, including solving the inverse problem of deducing the bioelectric sources from biomagnetic measurements. Recently, most magnetocardiographic research has focused on clinical applications, driven in part by new technologies to measure weak biomagnetic fields.

Non-Invasive Electroanatomical Mapping: A State-Space Approach for Myocardial Current Density Estimation

Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with clinically suspected myocarditis

Background

Myocarditis is a condition that can have severe adverse outcomes and lead to sudden cardiac death if remaining undetected. This study tested the capability of cardiac magnetic field mapping to detect patients with clinically suspected myocarditis. This could open up the way for rapid, non-invasive, and cost-effective screening of suspected cases before a gold standard assessment via endomyocardial biopsy.

Methods

Historical cardiac magnetic field maps (n = 97) and data from a state-of-the-art magnetocardiography device (n = 30) were analyzed using the Kullback-Leibler entropy (KLE) for dimensionality reduction and topological quantification. Linear discriminant analysis was used to discern between patients with ongoing myocarditis and healthy controls.

Results

The STT segment of a magnetocardiogram, i.e., the section between the end of the S wave and the end of the T wave, was best suited to discern both groups. Using a 250-ms excerpt from the onset of the STT segment gave a reliable classification between the myocarditis and control group for both historic data (sensitivity: 0.83, specificity: 0.85, accuracy: 0.84) and recent data (sensitivity: 0.69, specificity: 0.88, accuracy: 0.80) using the KLE to quantify the topology of the cardiac magnetic field map.

Conclusion

The implementation based on KLE can reliably distinguish between clinically suspected myocarditis patients and healthy controls. We implemented an automatized feature selection based on LDA to replace the observer-dependent manual thresholding in previous studies.