Assessment of Aortic Characteristic Impedance and Arterial Compliance from Non-invasive Carotid Pressure Waveform in The Framingham Heart Study
Assessment of Aortic Characteristic Impedance and Arterial Compliance from Non-invasive Carotid Pressure Waveform in The Framingham Heart Study
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DOI：
10.1016/j.amjcard.2023.07.076
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发表时间：
2023-08-04
影响因子：
2.8
通讯作者：
Pahlevan, Niema Mohammed
中科院分区：
文献类型：
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作者：
Niroumandi, Soha;Alavi, Rashid;Pahlevan, Niema Mohammed
本研究的主要目标是检验以下假设：混合固有频率机器学习（IF-ML）方法可以从患有心力衰竭（HF）的女性和男性的单一无创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是具有确定临床意义的心血管生物标志物。与男性相比，女性的TAC较低，Zao较高，因此女性更容易受到其随之而来的有害影响。尽管TAC和Zao的原理与包括HF在内的多种心血管疾病相关，但由于需要同时测量流量和压力波形，因此其常规临床使用受到限制。对于本研究，数据来自Frachial Heart研究（n = 6，201，53%为女性）。根据颈动脉压和主动脉血流波形计算Zao和TAC的参考值。ML模型中使用颈动脉压力波形的IF参数。IF模型是在n = 5，168个随机选择的数据上开发的，并对其余数据（n = 1，033）进行盲法检验。在HF患者中评价最终模型。在所有HF和保留射血分数的HF中，TAC的IF-ML与参考值之间的相关性分别为0.88和0.90，Zao的IF-ML与参考值之间的相关性分别为0.82和0.80。TAC对所有HF和射血分数保留HF的分类准确度分别为0.9和0.93，Zao分别为0.81和0.89。总之，IF-ML方法可准确估计所有HF受试者和一般人群中的TAC和Zao。#20203;作者。爱思唯尔公司出版这是CC BY-NC许可下的开放获取文章（http：creativecommons.org/licenses/by-nc/ 4.0/）（Am J Cardiol 2023;204：195-199）
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. & COPY; 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/ 4.0/) (Am J Cardiol 2023;204:195-199)