Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis

Jian-Guo Cui; Feng Tian; Yu-Hao Miao; Qin-Hua Jin; Ya-Jun Shi; Li Li; Meng-Jun Shen; Xiao-Ming Xie; Shu-Lin Zhang; Yun-Dai Chen

doi:10.26599/1671-5411.2024.04.006

Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis

J Geriatr Cardiol. 2024 Apr 28;21(4):407-420. doi: 10.26599/1671-5411.2024.04.006.

Authors

Jian-Guo Cui^{1

2}, Feng Tian², Yu-Hao Miao², Qin-Hua Jin², Ya-Jun Shi², Li Li³, Meng-Jun Shen³, Xiao-Ming Xie⁴, Shu-Lin Zhang⁴, Yun-Dai Chen^{1

2}

Affiliations

¹ School of Medicine, Nankai University, Tianjin, China.
² Senior Department of Cardiology, the Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
³ Joint Laboratory of Bioimaging Technology and Applications, SAS-SIMIT & MEDI, Shanghai, China.
⁴ Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.

Abstract

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_MV_amp, 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.