Journal Article

Diagnostic Value of Magnetocardiography to Detect Abnormal Myocardial Perfusion: A Pilot Study

Jiao, Jian, Zhang, Hongjia, Zhao, Xin, Zhou, Shu, Liu, Linqi, Feng, Lanxin, Song, Xiantao, Mi, Hongzhi, Zhang, Huan, Yang, Xueyao, Yang, Shuwen, Tu, Chenchen, Ma, Zhao, Dong, Wei

2024

Overview

Background: Magnetocardiography (MCG) is a novel non-invasive technique that detects subtle magnetic fields generated by cardiomyocyte electrical activity, offering sensitive detection of myocardial ischemia. This study aimed to assess the ability of MCG to predict impaired myocardial perfusion using single-photon emission computed tomography (SPECT). Methods: A total of 112 patients with chest pain underwent SPECT and MCG scans, from which 65 MCG output parameters were analyzed. Using least absolute shrinkage and selection operator (LASSO) regression to screen for significant MCG variables, three machine learning models were established to detect impaired myocardial perfusion: random forest (RF), decision tree (DT), and support vector machine (SVM). The diagnostic performance was evaluated based on the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: Five variables, the ratio of magnetic field amplitude at R-peak and positive T-peak (RoART+), R and T-peak magnetic field angle (RTA), maximum magnetic field angle (MAmax), maximum change in current angle (CCAmax), and change positive pole point area between the T-wave beginning and peak (CPPPATbp), were selected from 65 automatic output parameters. RTA emerged as the most critical variable in the RF, DT, and SVM models. All three models exhibited excellent diagnostic performance, with AUCs of 0.796, 0.780, and 0.804, respectively. While all models showed high sensitivity (RF = 0.870, DT = 0.826, SVM = 0.913), their specificity was comparatively lower (RF = 0.500, DT = 0.300, SVM = 0.100). Conclusions: Machine learning models utilizing five key MCG variables successfully predicted impaired myocardial perfusion, as confirmed by SPECT. These findings underscore the potential of MCG as a promising future screening tool for detecting impaired myocardial perfusion. Clinical Trial Registration: ChiCTR2200066942, https://www.chictr.org.cn/showproj.html?proj=187904.

• Publisher: IMR Press
• Subject: Acute coronary syndromes / Adenosine / Algorithms / Cardiac stress tests / Cardiomyocytes / chronic coronary syndromes / Data analysis / Decision making / Decision trees / Heart attacks / Ischemia / Machine learning / machine learning algorithm / Magnetic fields / magnetocardiography / Medical imaging / myocardial perfusion imaging / Original Research / Patients / Support vector machines / Tomography