Objective: This study aimed to evaluate which voice features can predict health deterioration in patients with chronic HF. Background: Heart failure (HF) is a chronic condition with progressive deterioration and acute decompensations, often requiring hospitalization and imposing substantial healthcare and economic burdens. Current standard-of-care (SoC) home monitoring, such as weight tracking, lacks predictive accuracy and requires high patient engagement. Voice is a promising non-invasive biomarker, though prior studies have mainly focused on acute HF stages. Methods: In a 2-month longitudinal study, 32 patients with HF collected daily voice recordings and SoC measures of weight and blood pressure at home, with biweekly questionnaires for health status. Acoustic analysis generated detailed vowel and speech features. Time-series features were extracted from aggregated lookback windows (e.g., 7 days) to predict next-day health status. Explainable machine learning with nested cross-validation identified top vocal biomarkers, and a case study illustrated model application. Results: A total of 21,863 recordings were analyzed. Acoustic vowel features showed strong correlations with health status. Time-series voice features within the lookback window outperformed corresponding standard care measures, achieving peak sensitivity and specificity of 0.826 and 0.782 versus 0.783 and 0.567 for SoC metrics. Key prognostic voice features identifying deterioration included delayed energy shift, low energy variability, and higher shimmer variability in vowels, along with reduced speaking and articulation rate, lower phonation ratio, decreased voice quality, and increased formant variability in speech. Conclusion: Voice-based monitoring offers a non-invasive approach to detect early health changes in chronic HF, supporting proactive and personalized care.

翻译：目的：本研究旨在评估哪些语音特征可预测慢性心力衰竭（HF）患者健康状况的恶化。背景：心力衰竭是一种呈渐进性恶化并伴有急性失代偿的慢性疾病，常需住院治疗，给医疗系统和经济带来沉重负担。当前标准护理家庭监测（如体重追踪）预测准确性不足且需要患者高度配合。语音作为一种有前景的非侵入性生物标志物，但既往研究主要集中于急性心衰阶段。方法：在一项为期2个月的纵向研究中，32例心衰患者每日在家采集语音录音及体重、血压等标准护理指标，并通过双周问卷评估健康状况。声学分析生成了详细的元音和语音特征。通过聚合回顾窗口（例如7天）提取时间序列特征，用于预测次日健康状况。采用嵌套交叉验证的可解释机器学习识别最佳语音生物标志物，并通过案例研究展示模型应用。结果：共分析21,863条录音。元音声学特征与健康状况呈强相关性。回顾窗口内的语音时间序列特征优于对应标准护理指标，其最高灵敏度与特异度分别为0.826和0.782，而标准护理指标为0.783和0.567。识别恶化的关键预后语音特征包括：元音的能量延迟转移、低能量变异性及更高基频微扰变异性，同时伴语速和发音速率降低、发声比减小、嗓音质量下降及语音共振峰变异性增大。结论：基于语音的监测为检测慢性心衰早期健康变化提供了一种非侵入性方法，有助于实现主动性和个性化护理。