磁心动图检测异常心肌灌注的诊断价值：一项初步研究。
Diagnostic Value of Magnetocardiography to Detect Abnormal Myocardial Perfusion: A Pilot Study.
作者信息
Zhang Huan, Ma Zhao, Mi Hongzhi, Jiao Jian, Dong Wei, Yang Shuwen, Liu Linqi, Zhou Shu, Feng Lanxin, Zhao Xin, Yang Xueyao, Tu Chenchen, Song Xiantao, Zhang Hongjia
机构信息
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China.
Department of Nuclear Medicine, Beijing Anzhen Hospital, Capital Medical University, 100029 Beijing, China.
出版信息
Rev Cardiovasc Med. 2024 Oct 23;25(10):379. doi: 10.31083/j.rcm2510379. eCollection 2024 Oct.
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.
背景
磁心动图（MCG）是一种新型非侵入性技术，可检测心肌细胞电活动产生的微弱磁场，能灵敏检测心肌缺血。本研究旨在评估MCG利用单光子发射计算机断层扫描（SPECT）预测心肌灌注受损的能力。
方法
共有112例胸痛患者接受了SPECT和MCG扫描，分析了其中65个MCG输出参数。使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心肌灌注受损：随机森林（RF）、决策树（DT）和支持向量机（SVM）。基于灵敏度、特异性、准确性、阳性预测值（PPV）、阴性预测值（NPV）和受试者工作特征曲线下面积（AUC）评估诊断性能。
结果
从65个自动输出参数中选出5个变量，即R波峰值与T波正向峰值处磁场幅度之比（RoART+）、R波和T波峰值磁场角度（RTA）、最大磁场角度（MAmax）、电流角度最大变化值（CCAmax）以及T波起始点与峰值之间正极点面积变化值（CPPPATbp）。RTA在RF、DT和SVM模型中成为最关键的变量。所有三种模型均表现出优异的诊断性能，AUC分别为0.796、0.780和0.804。虽然所有模型均显示出高灵敏度（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）。
结论
利用5个关键MCG变量的机器学习模型成功预测了SPECT证实的心肌灌注受损情况。这些发现强调了MCG作为未来检测心肌灌注受损的有前景的筛查工具的潜力。
临床试验注册
ChiCTR2200066942，https://www.chictr.org.cn/showproj.html?proj=187904。
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