Reavette Ryan M, Sherwin Spencer J, Tang Meng-Xing, Weinberg Peter D
Department of Bioengineering, Imperial College London, London, United Kingdom.
Department of Aeronautics, Imperial College London, London, United Kingdom.
Front Bioeng Biotechnol. 2021 Dec 24;9:737055. doi: 10.3389/fbioe.2021.737055. eCollection 2021.
Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure-velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter-velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups-no one metric in any artery could reliably indicate whether a subject's stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.
心力衰竭是可治疗的，但在英国，1年、5年和10年死亡率分别为24.1%、54.5%和75.5%。预后不佳部分反映了缺乏特异性、简单且经济实惠的诊断技术；该疾病在确诊时往往已发展到晚期。先前的研究表明，基于压力 - 速度的波强度分析得出的某些指标在心脏功能受损时，按组平均会有显著变化，但迄今为止，尚无研究在个体层面上检验波强度的诊断潜力，此外，压力波形只能通过侵入性方法准确获取，这限制了其临床应用。在此，我们研究基于动脉直径和速度的非侵入性测量的一种新的波强度形式是否能够检测个体的心脏功能受损情况。为此，我们生成了一个由两千名老年受试者组成的虚拟群体，将其中一半模拟为健康对照，另一半模拟为每搏量受损。从颈动脉、肱动脉和桡动脉基于直径 - 速度的波强度波形得出的所有指标在两组之间均显示出显著交叉——任何动脉中的任何一个指标都无法可靠地表明受试者的每搏量是正常还是受损。然而，在将机器学习应用于这些指标后，我们发现支持向量分类器能够同时实现高达99%的召回率和95%的精确率。我们得出结论，非侵入性波强度分析在改善心力衰竭筛查和诊断方面具有巨大潜力。
