Nose Daisuke, Matsui Tomokazu, Otsuka Takuya, Matsuda Yuki, Arimura Tadaaki, Yasumoto Keiichi, Sugimoto Masahiro, Miura Shin-Ichiro
Department of Cardiology, Fukuoka University Faculty of Medicine, Fukuoka 814-0180, Japan.
Department of Cardiology, Fukuoka Heartnet Hospital, Fukuoka 819-0002, Japan.
J Cardiovasc Dev Dis. 2023 Jul 7;10(7):291. doi: 10.3390/jcdd10070291.
Transthoracic impedance values have not been widely used to measure extravascular pulmonary water content due to accuracy and complexity concerns. Our aim was to develop a foundational model for a novel system aiming to non-invasively estimate the intrathoracic condition of heart failure patients.
We employed multi-frequency bioelectrical impedance analysis to simultaneously measure multiple frequencies, collecting electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers. Measurements were taken upon admission and after treatment, and longitudinal analysis was conducted.
Using a light gradient boosting machine, and a decision tree-based machine learning method, we developed an intrathoracic estimation model based on electrical measurements and clinical findings. Out of the 286 features collected, the model utilized 16 features. Notably, the developed model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an area under the receiver characteristic curves (AUC) of 0.905 (95% CI: 0.870-0.940, < 0.0001) in the cross-validation test. The accuracy significantly outperformed the conventional frequency-based method with an AUC of 0.740 (95% CI: 0.688-0.792, and < 0.0001).
Our findings indicate the potential of machine learning and transthoracic impedance measurements for estimating pleural effusion. By incorporating noninvasive and easily obtainable clinical and laboratory findings, this approach offers an effective means of assessing intrathoracic conditions.
由于对准确性和复杂性的担忧，经胸阻抗值尚未广泛用于测量血管外肺水含量。我们的目标是为一种新型系统开发一个基础模型，该系统旨在无创估计心力衰竭患者的胸腔状况。
我们采用多频生物电阻抗分析同时测量多个频率，收集了63名住院心力衰竭患者和82名健康志愿者的电学、物理和血液学数据。在入院时和治疗后进行测量，并进行纵向分析。
使用轻梯度提升机和基于决策树的机器学习方法，我们基于电学测量和临床发现开发了一种胸腔估计模型。在所收集的286个特征中，该模型利用了16个特征。值得注意的是，所开发的模型在鉴别胸腔积液患者方面表现出高准确性，在交叉验证测试中，受试者工作特征曲线下面积（AUC）达到0.905（95%CI：0.870-0.940，<0.0001）。其准确性显著优于传统的基于频率的方法，后者的AUC为0.740（95%CI：0.688-0.792，<0.0001）。
我们的研究结果表明机器学习和经胸阻抗测量在估计胸腔积液方面具有潜力。通过纳入无创且易于获得的临床和实验室检查结果，这种方法提供了一种评估胸腔状况的有效手段。
