预测阻塞性冠状动脉疾病存在的磁心动图评分系统。

Magnetocardiography scoring system to predict the presence of obstructive coronary artery disease.

作者信息

Shin Eun-Seok, Park Seung Gu, Saleh Ahmed, Lam Yat-Yin, Bhak Jong, Jung Friedrich, Morita Sumio, Brachmann Johannes

机构信息

Division of Cardiology, Ulsan Medical Center, Ulsan Hospital, Ulsan, Korea.

Korean Genomics Industrialization and Commercialization Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea.

出版信息

Clin Hemorheol Microcirc. 2018;70(4):365-373. doi: 10.3233/CH-189301.

DOI:10.3233/CH-189301

PMID:30320564

Abstract

BACKGROUND

Magnetocardiography (MCG) has been proposed as a non-invasive and functional technique with high accuracy for diagnosis of myocardial ischemia.

OBJECTIVE

This study sought to develop a novel scoring system of MCG for predicting the presence of significant obstructive coronary artery disease (CAD).

METHODS

In a training set of 108 subjects, predictors of ≥70% stenosis in at least one major coronary vessel were prospectively identified from MCG variables. The final model was then retrospectively validated in a separate set of 45 subjects.

RESULTS

In the multivariable logistic regression, among those in the training set, elevated scores were predictive of ≥70% stenosis in all subjects (OR: 40.85; 95% CI: 6.28-265.90; p < 0.001). In the validation set, the score had an area under the receiver-operating characteristic curve of 0.91 (p < 0.001) for ≥70% stenosis. At an optimal cutoff, the score had 89% sensitivity, 77% specificity, 74% positive predictive value (PPV), 91% negative predictive value (NPV), and 82% accuracy for ≥70% stenosis. Partitioning the score into three levels of predicted risk, 91% of subjects could be identified or excluding CAD (81% PPV and 84% NPV).

CONCLUSION

We described an MCG score with high accuracy for predicting the presence of anatomically significant CAD.

摘要

背景

磁心动图（MCG）已被提议作为一种用于诊断心肌缺血的非侵入性且功能准确的技术。

目的

本研究旨在开发一种用于预测严重阻塞性冠状动脉疾病（CAD）存在的新型MCG评分系统。

方法

在一个包含108名受试者的训练集中，从MCG变量中前瞻性地确定至少一根主要冠状动脉血管狭窄≥70%的预测因素。然后在另一组45名受试者中对最终模型进行回顾性验证。

结果

在多变量逻辑回归中，在训练集中，评分升高可预测所有受试者中狭窄≥70%（比值比：40.85；95%置信区间：6.28 - 265.90；p < 0.001）。在验证集中，对于狭窄≥70%，该评分在受试者工作特征曲线下的面积为0.91（p < 0.001）。在最佳截断值时，该评分对于狭窄≥70%具有89%的敏感性、77%的特异性、74%的阳性预测值（PPV）、91%的阴性预测值（NPV）和82%的准确性。将评分分为三个预测风险水平，91%的受试者可被识别或排除CAD（PPV为81%，NPV为84%）。