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

IF 1.3 4区医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Reviews in cardiovascular medicine Pub Date : 2024-10-23 eCollection Date: 2024-10-01 DOI:10.31083/j.rcm2510379

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

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Abstract

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.

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磁共振心动图对检测异常心肌灌注的诊断价值：一项试点研究

背景：磁心动图（MCG）是一种新型无创技术，可检测心肌细胞电活动产生的微弱磁场，从而灵敏地检测心肌缺血。本研究旨在评估 MCG 预测单光子发射计算机断层扫描（SPECT）心肌灌注受损的能力：共有 112 名胸痛患者接受了 SPECT 和 MCG 扫描，其中 65 个 MCG 输出参数得到了分析。利用最小绝对收缩和选择算子（LASSO）回归筛选重要的 MCG 变量，建立了三种机器学习模型来检测受损的心肌灌注：随机森林（RF）、决策树（DT）和支持向量机（SVM）。根据敏感性、特异性、准确性、阳性预测值（PPV）、阴性预测值（NPV）和接收者工作特征曲线下面积（AUC）评估诊断性能：从 65 个自动输出参数中选出了五个变量，即 R 峰和正 T 峰磁场振幅比值（RoART+）、R 峰和 T 峰磁场角（RTA）、最大磁场角（MAmax）、最大电流角变化（CCAmax）和 T 波起始点与峰值之间的正极点面积变化（CPPPATbp）。在 RF、DT 和 SVM 模型中，RTA 成为最关键的变量。所有三个模型都表现出卓越的诊断性能，AUC 分别为 0.796、0.780 和 0.804。虽然所有模型都显示出较高的灵敏度（RF = 0.870，DT = 0.826，SVM = 0.913），但其特异性相对较低（RF = 0.500，DT = 0.300，SVM = 0.100）：利用五个关键 MCG 变量的机器学习模型成功地预测了心肌灌注受损的情况，SPECT 也证实了这一点。这些发现强调了 MCG 作为未来检测心肌灌注受损的筛查工具的潜力：临床试验注册：ChiCTR2200066942，https://www.chictr.org.cn/showproj.html?proj=187904。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Reviews in cardiovascular medicine 医学-心血管系统

CiteScore

2.70

自引率

3.70%

发文量

377

审稿时长

1 months

期刊介绍： RCM is an international, peer-reviewed, open access journal. RCM publishes research articles, review papers and short communications on cardiovascular medicine as well as research on cardiovascular disease. We aim to provide a forum for publishing papers which explore the pathogenesis and promote the progression of cardiac and vascular diseases. We also seek to establish an interdisciplinary platform, focusing on translational issues, to facilitate the advancement of research, clinical treatment and diagnostic procedures. Heart surgery, cardiovascular imaging, risk factors and various clinical cardiac & vascular research will be considered.