Rodriguez Javier, Schulz Steffen, Voss Andreas, Herrera Sergio, Benito Salvador, Giraldo Beatriz F
Automatic Control Department (ESAII), Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain.
Front Physiol. 2023 Aug 10;14:1184293. doi: 10.3389/fphys.2023.1184293. eCollection 2023.
A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM-24 patients) and dilated (DCM-17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.
很大一部分老年人口受到心血管疾病的影响。心肌病的早期预后仍然是一项挑战。本研究的目的是根据从压力反射机制特征中提取的显著指标，根据心肺活动对血压的影响，对心肌病患者进行病因分类。研究纳入了41例心肌病患者（CMP），分为缺血性心肌病（ICM，24例患者）和扩张型心肌病（DCM，17例患者）。此外，39名对照（CON）受试者作为参考。提取了来自心电图（ECG）信号的逐搏（BBI）时间序列、来自血压信号（BP）的收缩压（SBP）和舒张压（DBP）时间序列以及来自呼吸流量（RF）信号的呼吸时间（TT）。通过拟合包含95%数据的多边形并结合统计描述指标，从几何角度对心肺和血管活动的三维表示进行了表征。DCM患者在血压下降活动的呼吸反应中呈现出特定模式。与DCM患者相比，ICM患者的心肺活动更稳定。总体而言，CMP患者调节血压变化的能力有限。此外，患者的心脏和呼吸系统对血管变异性增量变化的调节能力也有限，心率变异性较低。使用最佳分类器构建支持向量机模型。用于区分ICM和DCM患者的最佳模型准确率达到92.7%，灵敏度达到94.1%，特异性达到91.7%。在比较CMP患者和CON受试者时，最佳模型准确率达到86.2%，灵敏度达到82.9%，特异性达到89.7%。在比较ICM患者和CON受试者时，最佳模型准确率达到88.9%，灵敏度达到87.5%，特异性达到89.7%。在比较DCM患者和CON受试者时，最佳模型准确率达到87.5%，灵敏度达到76.5%，特异性达到92.3%。总之，本研究基于压力反射机制分析的新指标，引入了一种根据病因对患者进行分类的新方法。
