Okada Kozo, Mizuguchi Daisuke, Omiya Yasuhiro, Endo Koji, Kobayashi Yusuke, Iwahashi Noriaki, Kosuge Masami, Ebina Toshiaki, Tamura Kouichi, Sugano Teruyasu, Ishigami Tomoaki, Kimura Kazuo, Hibi Kiyoshi
Division of Cardiology, Yokohama City University Medical Center Yokohama Japan.
PST Inc. Yokohama Japan.
Circ Rep. 2024 Jul 20;6(8):303-312. doi: 10.1253/circrep.CR-24-0064. eCollection 2024 Aug 9.
This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers.
This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist. Machine-learning (ML) models to estimate HF conditions were created using a Light Gradient Boosting Machine. Voice analysis identified 27 acoustic features that correlated with conventional HF indicators and GOKAN scores. When creating ML models based on the acoustic features, there was a significant correlation between actual and ML-derived BNP levels (r=0.49; P<0.001). ML models also identified good diagnostic accuracies in determining HF conditions characterized by NYHA class ≥2, BNP ≥300 pg/mL, presence of pulmonary congestion or pleural effusion on chest X-ray, and decompensated HF (defined as NYHA class ≥2 and BNP levels ≥300 pg/mL; accuracy: 75.1%, 69.1%, 68.7%, 66.4%, and 80.4%, respectively).
The present study successfully extracted HF-related acoustic features that correlated with conventional HF indicators. Although the data are preliminary, ML models based on acoustic features (vocal biomarkers) have the potential to infer various HF conditions, which warrant future studies.
本研究旨在系统评估心力衰竭（HF）治疗期间的声音症状，并探索性提取与HF相关的声音生物标志物。
这项单中心前瞻性研究纵向收集了59例急性失代偿性HF患者的839个音频文件。对患者的声音进行了分析，并结合传统的HF指标（纽约心脏协会[NYHA]分级、胸部X线片上肺淤血和胸腔积液的存在情况以及B型利钠肽[BNP]）和基于心脏病专家评估的GOKAN评分。使用轻量级梯度提升机创建了用于估计HF状况的机器学习（ML）模型。声音分析确定了27个与传统HF指标和GOKAN评分相关的声学特征。基于这些声学特征创建ML模型时，实际BNP水平与ML得出的BNP水平之间存在显著相关性（r = 0.49；P < 0.001）。ML模型在确定以NYHA分级≥2、BNP≥300 pg/mL、胸部X线片上存在肺淤血或胸腔积液以及失代偿性HF（定义为NYHA分级≥2且BNP水平≥300 pg/mL）为特征的HF状况时，也具有良好的诊断准确性（准确率分别为75.1%、69.1%、68.7%、66.4%和80.4%）。
本研究成功提取了与传统HF指标相关的HF声学特征。尽管数据是初步的，但基于声学特征（声音生物标志物）的ML模型有潜力推断各种HF状况，这值得未来进一步研究。
