Cui Jian-Guo, Tian Feng, Miao Yu-Hao, Jin Qin-Hua, Shi Ya-Jun, Li Li, Shen Meng-Jun, Xie Xiao-Ming, Zhang Shu-Lin, Chen Yun-Dai
School of Medicine, Nankai University, Tianjin, China.
Senior Department of Cardiology, the Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
J Geriatr Cardiol. 2024 Apr 28;21(4):407-420. doi: 10.26599/1671-5411.2024.04.006.
To evaluate the role of resting magnetocardiography in identifying severe coronary artery stenosis in patients with suspected coronary artery disease.
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.
In the severe coronary stenosis group, QR_MCTDd, S_MDp, and TT_MAC were significantly higher than those in the non-severe coronary stenosis group (10.46 ± 10.66 5.11 ± 6.07, < 0.001; 7.2 ± 8.64 4.68 ± 6.95, = 0.003; 0.32 ± 57.29 0.26 ± 57.29, < 0.001). While, QR_MV, R_MA, and T_MA in the severe coronary stenosis group were lower (0.23 ± 0.16 0.28 ± 0.16, < 0.001; 55.06 ± 48.68 59.24 ± 53.01, < 0.001; 51.67 ± 39.32 60.45 ± 51.33, < 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.
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.
评估静息磁心动图在识别疑似冠心病患者严重冠状动脉狭窄中的作用。
纳入513例有胸痛症状的患者，根据血管造影确定的冠状动脉疾病程度分为两组：非严重冠状动脉狭窄组（狭窄<70%）和严重冠状动脉狭窄组（狭窄≥70%）。使用磁场图（MFM）参数单独或与临床指标相结合构建诊断模型。使用受试者工作特征曲线、准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）评估模型的性能。分别进行校准图和决策曲线分析以研究模型的临床实用性和性能。
在严重冠状动脉狭窄组中，QR_MCTDd、S_MDp和TT_MAC显著高于非严重冠状动脉狭窄组（10.46±10.66对5.11±6.07，P<0.001；7.2±8.64对4.68±6.95，P = 0.003；0.32±57.29对0.26±57.29，P<0.001）。而严重冠状动脉狭窄组的QR_MV、R_MA和T_MA较低（0.23±0.16对0.28±0.16，P<0.001；55.06±48.68对59.24±53.01，P<0.001；51.67±39.32对60.45±51.33，P<0.001）。七个MFM参数被纳入模型，曲线下面积为0.810（95%CI：0.765 - 0.855）。敏感性、特异性、PPV、NPV和准确性分别为71.7%、80.4%、93.3%、42.8%和73.5%；联合模型的曲线下面积为0.845（95%CI：0.798 - 0.892）。敏感性、特异性、PPV、NPV和准确性分别为84.3%、73.8%、92.6%、54.6%和82.1%；校准曲线显示列线图预测与实际观察之间具有良好的一致性。决策曲线分析表明，与磁心动图模型相比，联合模型提供了更大的净效益。
新的定量MFM参数，无论是单独使用还是与临床指标结合使用，都已被证明能有效预测有胸痛样症状患者严重冠状动脉狭窄的风险。磁心动图作为一种新兴的非侵入性诊断工具，其在诊断冠心病方面的潜力值得进一步探索。
