Magnetocardiographic assessment of healed myocardial infarction

Accurate diagnosis of ischemic heart disease without exposure to radiation using non-stress unshielded magnetocardiography

Study objectives

To evaluate the capability and accuracy of magnetocardiography (MCG) to identify patients with ischemic chest pain from those with non-ischemic pain and to verify normalcy in the MCG in healthy subjects.

Design

We studied 133 patients (mean age 59 ± 14 years, 69 % male) with chronic or acute chest pain syndrome and 63 healthy subjects (mean age 41.7 ± 12.2 years, 51 % male) using unshielded cryogenically cooled MCG systems (Cardiomag Imaging Inc., 9 and 36 channels) in a general clinical setting. Scan time was 90 s to 6 min. Interventions: The MCG data were processed with the same automated analysis software and results were immediately available. All patients were chest pain free at the time of scanning.

Results

A diagnosis of ischemic chest pain was established in 41 % after non-invasive and invasive testing. Rest MCG was normal in all healthy subjects. An abnormal rest MCG was strongly associated with ischemic chest pain, p < 0.0001 (sensitivity of 86 %, specificity of 80 %, positive (PPV) and negative predictive value (NPV) of 75 % and 89 %, respectively). In comparison, the sensitivity, specificity, PPV and NPV of stress SPECT was 93 %, 72 %, 77 % and 91 %, respectively.

Conclusion

Resting MCG is a rapid risk-free method for the detection of ischemic chest pain without the use of radiation or contrast with results comparable with stress SPECT.

Detection of coronary artery disease in patients with chest pain: A machine learning model based on magnetocardiography parameters

BACKGROUD

Patients with chest pain and suspected of coronary artery disease(CAD) need further test to confirm the diagnosis. Magnetocardiography (MCG) is a non-invasive and emission-free technology which can detect and measure the weak magnetic fields created by the electrical activity of the heart.

OBJECTIVE

This study aimed to investigate the usefulness of the 10 MCG parameters to detect CAD in patients with chest pain by means of a machine learning method of multilayer perceptron(MLP) neural network.

METHODS

209 patients who were suffering from chest pain and suspected of CAD were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography(ECG) and MCG test were performed before percutaneous coronary angiography(PCA). 10 MCG parameters were analyzed by MLP neural networks.

RESULTS

11 diagnostic models(M1 to M11) were established after MLP analysis. The accuracies ranged from 71.2% to 90.5%. Two models(M10 and M11) were further analyzed. The accuracy, sensitivity, specificity, PPV, NPV, PLR and NLR were 89.5%, 89.8%, 88.9%, 92.7%, 84.7%, 11.10 and 0.11, of M10, and were 90.0%, 91.4%, 87.7%, 92.1%, 86.6%, 7.43 and 0.10, of M11.

CONCLUSIONS

By a method of MLP neural network, MCG is applicable in identifying CAD in patients with chest pain, which seems beneficial for detection of CAD.

Incremental diagnostic value of combined quantitative and qualitative parameters of magnetocardiography to detect coronary artery disease

BACKGROUND/OBJECTIVES

Magnetocardiography (MCG) has been proposed as a non-invasive and functional technique with high accuracy for diagnosis of myocardial ischemia. This study sought to investigate the incremental diagnostic value of combined quantitative and qualitative parameters of MCG to detect coronary artery disease (CAD).

METHODS

Ninety six patients with suspected CAD who underwent coronary angiography were enrolled in the analysis to test the diagnostic accuracy of 2 MCG parameters (a quantitative parameter of the percent change of ST-segment fluctuation score and a qualitative parameter of non-dipole phenomenon).

RESULTS

The best cut-off value for the percent change of ST-segment fluctuation score was -51.0%. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.1, 73.9, 82.0, 79.1, and 77.4, in the percent change of ST-segment fluctuation score and 86.5, 84.8, 88.0, 86.7, and 86.3 in non-dipole phenomenon. The area under the curve of receiver-operating characteristics was 0.79 for the percent change of ST-segment fluctuation score and 0.86 for non-dipole phenomenon (p<0.001). However, the incorporation of non-dipole phenomenon into a model with the percent change of ST-segment fluctuation score significantly improved C-statistics, indicating the enhancement of diagnostic performance in the detection of significant CAD (0.790 to 0.930; p<0.001).

CONCLUSIONS

Qualitative assessment of non-dipole phenomenon has a better diagnostic value than the quantitative parameter of percent change of ST-segment fluctuation score in the detection of significant CAD. Furthermore, this study found that the incorporation of non-dipole phenomenon into the percent change of ST-segment fluctuation score significantly improved the diagnostic performance of CAD detection.

Diagnostic value of magnetocardiography in coronary artery disease and cardiac arrhythmias: a review of clinical data

Despite the availability of several advanced non-invasive diagnostic tests such as echocardiography and magnetic resonance imaging, electrocardiography (ECG) remains as the most widely used diagnostic technique in clinical cardiology. ECG detects electrical potentials that are generated by cardiac electrical activity. In addition to electrical potentials, the same electrical activity of the heart also induces magnetic fields. These extremely weak cardiac magnetic signals are detected by a non-invasive, contactless technique called magnetocardiography (MCG), which has been evaluated in a number of clinical studies for its usefulness in diagnosing heart diseases. We reviewed the basic principles, history and clinical data on the diagnostic role of MCG in coronary artery disease and cardiac arrhythmias.

Usefulness of magnetocardiogram to detect unstable angina pectoris and non-ST elevation myocardial infarction

Electrophysiologic information as well as anatomic information to detect coronary artery disease is important for accurate diagnosis. A diagnostic tool that can detect patients with unstable angina pectoris (UAP) or non-ST elevation myocardial infarction (NSTEMI) with severe stenosis would be beneficial for patients and clinicians. Magnetocardiography has been recognized as a noncontact, noninvasive, fast tool to detect ischemic coronary artery disease and provide direct electrophysiologic information from the heart. In this study, 10 magnetocardiographic (MCG) parameters from 4 groups, including 185 young controls, 19 age-matched controls (AMCs), 110 patients with UAP, and 83 patients with NSTEMIs, were analyzed. A 64-channel MCG system in a magnetically shielded room was used. All 10 parameters showed significant differences (p <0.001) between controls and patients with NSTEMIs, and 6 parameters showed significant differences (p <0.01) between AMCs and patients with UAP. MCG parameters significantly increased when ischemic heart conditions worsened. Of the 10 parameters, the magnetic field map was among the easiest ways to detect the severity of coronary artery disease. Abnormal magnetic field maps were observed frequently with worsening ischemic coronary artery disease (70% of patients with UAP and 92.5% of those with NSTEMIs had abnormal maps). The combination of the binary boundaries of the 10 parameters had 96.4% sensitivity and 85% specificity to detect NSTEMI. In conclusion, the MCG parameters and magnetic field maps may detect UAP and NSTEMI easily when they are considered together.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common primary inherited cardiac muscle disorder, defined clinically by the presence of unexplained left ventricular hypertrophy. The detection of affected patients remains challenging. Genetic testing is limited because only in 50%-60% of all HCM diagnoses an underlying mutation can be found. Furthermore, the disease has a varied clinical course and outcome, with many patients having little or no discernible cardiovascular symptoms, whereas others develop profound exercise limitation and recurrent arrhythmias or sudden cardiac death. Therefore prospective screening of HCM family members is strongly recommended. According to the current guidelines this includes serial echocardiographic and electrocardiographic examinations. In this study we investigated the capability of cardiac magnetic field mapping (CMFM) to detect patients suffering from HCM. We introduce for the first time a combined diagnostic approach based on map topology quantification using Kullback-Leibler (KL) entropy and regional magnetic field strength parameters. The cardiac magnetic field was recorded over the anterior chest wall using a multichannel-LT-SQUID system. CMFM was calculated based on a regular 36 point grid. We analyzed CMFM in patients with confirmed diagnosis of HCM (HCM, n=33, 43.8+/-13 years, 13 women, 20 men), a control group of healthy subjects (NORMAL, n=57, 39.6+/-8.9 years; 22 women and 35 men), and patients with confirmed cardiac hypertrophy due to arterial hypertension (HYP, n=42, 49.7+/-7.9 years, 15 women and 27 men). A subgroup analysis was performed between HCM patients suffering from the obstructive (HOCM, n=19) and nonobstructive (HNCM, n=14) form of the disease. KL entropy based map topology quantification alone identified HCM patients with a sensitivity of 78.8% and specificity of 86.9% (overall classification rate 84.8%). The combination of the KL parameters with a regional field strength parameter improved the overall classification rate to 87.9% (sensitivity: 84.8%, specificity: 88.9%, area under ROC curve: 0.94). KL measures applied to discriminate between HOCM and HNCM patients showed a correct classification of 78.8%. The combination of one KL and one regional parameter again improved the overall classification rate to 97%. A preliminary prospective analysis in two HCM families showed the feasibility of this diagnostic approach with a correct diagnosis of all 22 screened family members (1 HOCM, 4 HNCM, 17 normal). In conclusion, Cardiac Magnetic Field Mapping including KL entropy based topology quantifications is a suitable tool for HCM screening.

Can magnetocardiography detect patients with non-ST-segment elevation myocardial infarction?

BACKGROUND AND AIM

Magnetocardiography (MCG) has been proposed as a noninvasive diagnostic tool to risk-stratify patients with myocardial infarction (MI) and ischemia. The purpose of this study is to find the MCG parameters that are sensitive enough to detect the non-ST-segment elevation myocardial infarction (NSTEMI) patients.

METHODS

MCG data were recorded and analyzed from 165 young controls (mean age = 27.2 +/- 9.0 years), 57 age-matched controls (mean age = 55.9 +/- 10.5 years) and 83 NSTEMI patients (mean age = 59.7 +/- 11.1 years). The MCG recordings were obtained using a 64-channel MCG system in a magnetically shielded room. Statistical analyses were performed for 24 parameters derived from QRS-, R-, T-wave, and ST-T period. Binary boundaries to detect NSTEMI patients out of control subjects were found using the receiver operating characteristic (ROC) curve for each parameter.

RESULTS

Fifteen parameters showed a significant difference (P < 0.05 and P < 0.01) between NSTEMI and both of the control groups. For detection of NSTEMI, the angle of the maximum current and the filed map angle on T-wave peak showed the highest diagnostic performance from 75% to 92% including accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (area under ROC curve = 0.87 approximately 0.93).

CONCLUSIONS

Our study showed that MCG has potential clinical application for detection of NSTEMI and should be further investigated.