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Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center,
Jian-Guo Cui1,2, Feng Tian2, Yu-Hao Miao2
1School of Medicine, Nankai University, Tianjin, China.
Insights
Resting magnetocardiography effectively identifies severe coronary artery stenosis using novel magnetic field map (MFM) parameters. A combined model integrating MFM data and clinical indicators showed high accuracy in diagnosing coronary heart disease in patients with angina symptoms.
Area of Science:
- Cardiology
- Biophysics
- Medical Imaging
Background:
- Coronary artery disease (CAD) poses a significant health burden, necessitating accurate diagnostic tools.
- Identifying severe coronary artery stenosis is crucial for timely intervention and improved patient outcomes.
- Resting magnetocardiography (MCG) offers a non-invasive approach to assess cardiac electrical activity.
Purpose of the Study:
- To evaluate the diagnostic performance of resting magnetocardiography (MCG) in identifying severe coronary artery stenosis (≥70% stenosis).
- To assess the utility of novel quantitative magnetic field map (MFM) parameters in predicting severe coronary stenosis.
- To compare the diagnostic accuracy of MFM parameters alone versus in combination with clinical indicators.
Main Methods:
- 513 patients with angina symptoms underwent resting MCG and coronary angiography.
- Diagnostic models were developed using individual and combined MFM parameters and clinical indicators.
- Model performance was evaluated using receiver operating characteristic (ROC) curves, sensitivity, specificity, and predictive values.
Main Results:
- Seven MFM parameters were identified as significant predictors of severe coronary stenosis.
- A model combining MFM parameters achieved an area under the curve (AUC) of 0.845, with 84.3% sensitivity and 73.8% specificity.
- The combined model demonstrated superior clinical utility and net benefit compared to the MFM-only model.
Conclusions:
- Novel quantitative MFM parameters derived from MCG are effective in predicting severe coronary stenosis in patients with suspected CAD.
- MCG, as an emerging non-invasive tool, shows significant potential for diagnosing coronary heart disease.
- Combining MFM parameters with clinical indicators enhances diagnostic accuracy and clinical utility.
Objective:
To evaluate the role of resting magnetocardiography in identifying severe coronary artery stenosis in patients with suspected coronary artery disease.
Methods:
A total of 513 patients with angina symptoms were included and divided into two groups based on the extent of coronary artery disease determined by angiography: the non-severe coronary stenosis group (< 70% stenosis) and the severe coronary stenosis group (≥ 70% stenosis). The diagnostic model was constructed using magnetic field map (MFM) parameters, either individually or in combination with clinical indicators. The performance of the models was evaluated using receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Calibration plots and decision curve analysis were performed to investigate the clinical utility and performance of the models, respectively.
Results:
In the severe coronary stenosis group, QR_MCTDd, S_MDp, and TT_MAC50 were significantly higher than those in the non-severe coronary stenosis group (10.46 ± 10.66 vs. 5.11 ± 6.07, P < 0.001; 7.2 ± 8.64 vs. 4.68 ± 6.95, P = 0.003; 0.32 ± 57.29 vs. 0.26 ± 57.29, P < 0.001). While, QR_MVamp, R_MA, and T_MA in the severe coronary stenosis group were lower (0.23 ± 0.16 vs. 0.28 ± 0.16, P < 0.001; 55.06 ± 48.68 vs. 59.24 ± 53.01, P < 0.001; 51.67 ± 39.32 vs. 60.45 ± 51.33, P < 0.001). Seven MFM parameters were integrated into the model, resulting in an area under the curve of 0.810 (95% CI: 0.765-0.855). The sensitivity, specificity, PPV, NPV, and accuracy were 71.7%, 80.4%, 93.3%, 42.8%, and 73.5%; respectively. The combined model exhibited an area under the curve of 0.845 (95% CI: 0.798-0.892). The sensitivity, specificity, PPV, NPV, and accuracy were 84.3%, 73.8%, 92.6%, 54.6%, and 82.1%; respectively. Calibration curves demonstrated excellent agreement between the nomogram prediction and actual observation. The decision curve analysis showed that the combined model provided greater net benefit compared to the magnetocardiography model.
Conclusions:
The novel quantitative MFM parameters, whether used individually or in combination with clinical indicators, have been shown to effectively predict the risk of severe coronary stenosis in patients presenting with angina-like symptoms. Magnetocardiography, an emerging non-invasive diagnostic tool, warrants further exploration for its potential in diagnosing coronary heart disease.
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