Tran Que N N, Moriguchi Takeshi, Ueno Masateru, Iwano Tomohiko, Yoshimura Kentaro
Emergency & Critical Care Medicine Department, Graduate School of Medicine, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan.
Anatomy and Cell Biology Department, Graduate School of Medicine, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan.
Mass Spectrom (Tokyo). 2024;13(1):A0147. doi: 10.5702/massspectrometry.A0147. Epub 2024 Jul 11.
The purpose of this study is to establish a novel diagnosis system in early acute coronary syndrome (ACS) using probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning (ML) and to validate the diagnostic accuracy. A total of 32 serum samples derived from 16 ACS patients and 16 control patients were analyzed by PESI-MS. The acquired mass spectrum dataset was subsequently analyzed by partial least squares (PLS) regression to find the relationship between the two groups. A support vector machine, an ML method, was applied to the dataset to construct the diagnostic algorithm. Control and ACS groups were separated into the two clusters in the PLS plot, indicating ACS patients differed from the control in the profile of serum composition obtained by PESI-MS. The sensitivity, specificity, and accuracy of our diagnostic system were all 93.8%, and the area under the receiver operating characteristic curve showed 0.965 (95% CI: 0.84-1). The PESI-MS and ML-based diagnosis system are likely an optimal solution to assist physicians in ACS diagnosis with its remarkably predictive accuracy.
本研究的目的是利用探针电喷雾电离质谱（PESI-MS）和机器学习（ML）建立一种针对早期急性冠状动脉综合征（ACS）的新型诊断系统，并验证其诊断准确性。通过PESI-MS对来自16例ACS患者和16例对照患者的共32份血清样本进行了分析。随后，通过偏最小二乘（PLS）回归对获得的质谱数据集进行分析，以找出两组之间的关系。将一种ML方法——支持向量机应用于该数据集以构建诊断算法。在PLS图中，对照组和ACS组被分为两个簇，这表明通过PESI-MS获得的血清成分谱中，ACS患者与对照组不同。我们诊断系统的敏感性、特异性和准确性均为93.8%，受试者工作特征曲线下面积为0.965（95%CI：0.84 - 1）。基于PESI-MS和ML的诊断系统以其显著的预测准确性，可能是协助医生进行ACS诊断的最佳解决方案。
