Division of Pediatric Cardiology, Children's Hospital Los Angeles, Los Angeles, CA, United States of America.
Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.
Physiol Meas. 2023 Mar 1;44(3). doi: 10.1088/1361-6579/acba7b.
Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients.. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%.The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameterω1was elevated among patients with reduced LVEF.A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.
患有心力衰竭的儿童急诊就诊率、医疗支出和住院率更高。因此，需要一种简单、非侵入性且廉价的方法来筛查左心室（LV）功能障碍。我们最近证明了一种基于无线智能手机的手持式设备在捕获颈动脉压力波形和推导心血管固有频率（IF）方面的实用性和可靠性，该设备可用于具有正常 LV 功能的儿童。我们在这项研究中的目标是证明基于 IF 的机器学习方法（IF-ML）应用于非侵入性颈动脉压力波形可以区分正常和异常 LV 射血分数（LVEF）在儿科患者中。共有 50 名年龄在 0 至 21 岁的患者接受了超声心动图或心脏磁共振成像测量 LVEF。在同一天，患者使用 Vivio 记录颈动脉波形。排除标准是已知的血管疾病，这会干扰获得颈动脉脉搏。我们采用了一种混合 IF-机器学习（IF-ML）方法，通过将生理相关的 IF 参数作为输入应用于决策树分类器。选择低 LVEF 的阈值为<50%。拟议的 IF-ML 方法能够以 92%的准确度（灵敏度=100%，特异性=89%，曲线下面积（AUC）=0.95）检测异常 LVEF。与先前的临床研究一致，ω1IF 参数在 LVEF 降低的患者中升高。基于智能手机的手持式设备记录的颈动脉波形的混合 IF-ML 方法可区分具有正常心脏解剖结构的儿童的正常和异常 LV 收缩功能。
