Fathieh Farhad, Paak Mehdi, Khosousi Ali, Burton Tim, Sanders William E, Doomra Abhinav, Lange Emmanuel, Khedraki Rola, Bhavnani Sanjeev, Ramchandani Shyam
CorVista Health(†), 160 Bloor St. East, Suite 910, Toronto, ON, Canada.
CorVista Health, Inc., 401 Harrison Oaks Blvd, Suite 100, Cary, NC, USA.
Comput Methods Programs Biomed. 2021 Apr;202:105970. doi: 10.1016/j.cmpb.2021.105970. Epub 2021 Feb 7.
Coronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging infrastructure. Early detection of left ventricular dysfunction is critical in heart failure management, the best measure of which is an elevated left ventricular end-diastolic pressure (LVEDP) that can only be measured using invasive cardiac catheterization. There exists a need for non-invasive, safe, and fast diagnostic testing for CAD and elevated LVEDP. This research employs nonlinear dynamics to assess for significant CAD and elevated LVEDP using non-invasively acquired photoplethysmographic (PPG) and three-dimensional orthogonal voltage gradient (OVG) signals. PPG (variations of the blood volume perfusing the tissue) and OVG (mechano-electrical activity of the heart) signals represent the dynamics of the cardiovascular system.
PPG and OVG were simultaneously acquired from two cohorts, (i) symptomatic subjects that underwent invasive cardiac catheterization, the gold standard test (408 CAD positive with stenosis≥ 70% and 186 with LVEDP≥ 20 mmHg) and (ii) asymptomatic healthy controls (676). A set of Poincaré-based synchrony features were developed to characterize the interactions between the OVG and PPG signals. The extracted features were employed to train machine learning models for CAD and LVEDP. Five-fold cross-validation was used and the best model was selected based on the average area under the receiver operating characteristic curve (AUC) across 100 runs, then assessed using a hold-out test set.
The Elastic Net model developed on the synchrony features can effectively classify CAD positive subjects from healthy controls with an average validation AUC=0.90±0.03 and an AUC= 0.89 on the test set. The developed model for LVEDP can discriminate subjects with elevated LVEDP from healthy controls with an average validation AUC=0.89±0.03 and an AUC=0.89 on the test set. The feature contributions results showed that the selection of a proper registration point for Poincaré analysis is essential for the development of predictive models for different disease targets.
Nonlinear features from simultaneously-acquired signals used as inputs to machine learning can assess CAD and LVEDP safely and accurately with an easy-to-use, portable device, utilized at the point-of-care without radiation, contrast, or patient preparation.
冠状动脉疾病（CAD）和心力衰竭是最常见的心血管疾病。CAD的非侵入性诊断检测需要辐射、心率加速和成像基础设施。左心室功能障碍的早期检测在心力衰竭管理中至关重要，其最佳指标是左心室舒张末期压力（LVEDP）升高，而这只能通过侵入性心脏导管插入术来测量。因此，需要一种用于CAD和LVEDP升高的非侵入性、安全且快速的诊断检测方法。本研究采用非线性动力学，利用非侵入性获取的光电容积脉搏波描记法（PPG）和三维正交电压梯度（OVG）信号来评估显著CAD和LVEDP升高情况。PPG（灌注组织的血容量变化）和OVG（心脏的机电活动）信号代表了心血管系统的动力学。
从两个队列中同时采集PPG和OVG信号，（i）接受侵入性心脏导管插入术（金标准检测）的有症状受试者（408例CAD阳性且狭窄≥70%，186例LVEDP≥20 mmHg），以及（ii）无症状健康对照者（676例）。开发了一组基于庞加莱的同步特征来表征OVG和PPG信号之间的相互作用。提取的特征用于训练CAD和LVEDP的机器学习模型。采用五折交叉验证，并根据100次运行的平均受试者工作特征曲线下面积（AUC）选择最佳模型，然后使用保留测试集进行评估。
基于同步特征开发的弹性网络模型能够有效地将CAD阳性受试者与健康对照者区分开来，平均验证AUC = 0.90±0.03，测试集上的AUC = 0.89。开发的LVEDP模型能够将LVEDP升高的受试者与健康对照者区分开来，平均验证AUC = 0.89±0.03，测试集上的AUC = 0.89。特征贡献结果表明，为庞加莱分析选择合适的配准点对于针对不同疾病靶点的预测模型的开发至关重要。
将同时采集的信号中的非线性特征用作机器学习的输入，可以使用易于使用的便携式设备在护理点安全、准确地评估CAD和LVEDP，无需辐射、造影剂或患者准备。
