Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, Los Angeles, California.
Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, California.
Am J Cardiol. 2023 Oct 1;204:195-199. doi: 10.1016/j.amjcard.2023.07.076. Epub 2023 Aug 4.
The primary goal of this study was to test the hypothesis that a hybrid intrinsic frequency-machine learning (IF-ML) approach can accurately evaluate total arterial compliance (TAC) and aortic characteristic impedance (Zao) from a single noninvasive carotid pressure waveform in both women and men with heart failure (HF). TAC and Zao are cardiovascular biomarkers with established clinical significance. TAC is lower and Zao is higher in women than in men, so women are more susceptible to the consequent deleterious effects of them. Although the principles of TAC and Zao are pertinent to a multitude of cardiovascular diseases, including HF, their routine clinical use is limited because of the requirement for simultaneous measurements of flow and pressure waveforms. For this study, the data were obtained from the Framingham Heart Study (n = 6,201, 53% women). The reference values of Zao and TAC were computed from carotid pressure and aortic flow waveforms. IF parameters of carotid pressure waveform were used in ML models. IF models were developed on n = 5,168 of randomly selected data and blindly tested the remaining data (n = 1,033). The final models were evaluated in patients with HF. Correlations between IF-ML and reference values in all HF and HF with preserved ejection fraction for TAC were 0.88 and 0.90, and for Zao were 0.82 and 0.80, respectively. The classification accuracy in all HF and HF with preserved ejection fraction for TAC were 0.9 and 0.93, and for Zao were 0.81 and 0.89, respectively. In conclusion, the IF-ML method provides an accurate estimation of TAC and Zao in all subjects with HF and in the general population.
本研究的主要目的是验证一个假设，即混合固有频率-机器学习（IF-ML）方法能否从女性和男性心力衰竭（HF）患者的单一非侵入性颈动脉压力波形中准确评估总动脉顺应性（TAC）和主动脉特征阻抗（Zao）。TAC 和 Zao 是具有临床意义的心血管生物标志物。TAC 在女性中较低，而 Zao 在女性中较高，因此女性更容易受到它们的不利影响。尽管 TAC 和 Zao 的原理与包括 HF 在内的多种心血管疾病有关，但由于需要同时测量流量和压力波形，其常规临床应用受到限制。本研究的数据来自弗雷明汉心脏研究（n=6201，女性占 53%）。Zao 和 TAC 的参考值是从颈动脉压力和主动脉流量波形中计算出来的。颈动脉压力波形的 IF 参数用于 ML 模型。IF 模型是在随机选择的 n=5168 个数据上开发的，并对其余数据（n=1033）进行了盲测。最终模型在 HF 患者中进行了评估。在所有 HF 和射血分数保留的 HF 患者中，IF-ML 与参考值之间的相关性在 TAC 方面为 0.88 和 0.90，在 Zao 方面为 0.82 和 0.80。在所有 HF 和射血分数保留的 HF 患者中，TAC 的分类准确率为 0.9 和 0.93，Zao 的分类准确率为 0.81 和 0.89。总之，IF-ML 方法能够准确估计所有 HF 患者和一般人群的 TAC 和 Zao。
