PDF(Pubmed)
Abstract：
Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma.
This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR).
A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction.
The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively).
The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.
This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR).
A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction.
The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively).
The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.
摘要：
背景：基于声乐生物标志物的机器学习方法在检测各种健康状况方面显示出有希望的结果，包括哮喘等呼吸道疾病。在这项研究中,我们旨在验证最初在哮喘和健康志愿者数据集上训练的呼吸反应性声带生物标志物（RRVB）平台的区分能力，没有修改，活跃的COVID-19感染与向美国和印度医院展示患者的健康志愿者。
目的：本研究的目的是确定RRVB模型是否可以区分患有活动性COVID-19感染的患者。无症状健康志愿者通过评估其敏感性，特异性，和赔率比。另一个目的是评估RRVB模型输出是否与COVID-19的症状严重程度相关。
方法：使用语音声学特征的加权和的逻辑回归模型先前在约1,700名确诊哮喘患者的数据集上进行了训练和验证类似数量的健康对照。相同的模型已显示出对慢性阻塞性肺疾病（COPD）患者的普遍性，间质性肺病（ILD），还有咳嗽.在本研究中,共有497名参与者(46%为男性，54%女性；94%<65岁，6%>=65岁；51%马拉地语，45%英语,5％的西班牙语使用者）在美国和印度的四个临床站点注册，并在其个人智能手机上提供语音样本和症状报告。参与者包括有症状的COVID-19阳性和阴性患者以及无症状的健康志愿者。通过与RT-PCR证实的COVID-19的临床诊断进行比较，评估了RRVB模型的性能。
结果：RRVB模型区分呼吸系统疾病患者的能力与健康对照以前在哮喘的验证数据上得到了证明，COPD,ILD和咳嗽的比值比分别为4.3、9.1、3.1和3.9。本研究在COVID-19中进行的RRVB模型相同，灵敏度为73.2%，特异性为62.9%，比值比为4.64（p<0.0001）。出现呼吸道症状的患者比未出现呼吸道症状和完全无症状的患者更频繁地检测到（78.4％vs.67.4%与68.0%）。
结论：RRVB模型在呼吸条件下显示出良好的泛化性，地理位置，和语言。COVID-19的结果表明，它有可能作为一种预筛查工具，用于结合温度和症状报告识别有COVID-19感染风险的受试者。虽然不是COVID-19测试，这些结果表明，RRVB模型可以鼓励有针对性的测试。此外，该模型在不同的语言和地理环境中检测呼吸道症状的通用性提示了开发和验证未来用于更广泛疾病监测和监测应用的基于语音的工具的潜在途径.
背景：ClinicalTrials.gov（NCT04582331。
目的：本研究的目的是确定RRVB模型是否可以区分患有活动性COVID-19感染的患者。无症状健康志愿者通过评估其敏感性，特异性，和赔率比。另一个目的是评估RRVB模型输出是否与COVID-19的症状严重程度相关。
方法：使用语音声学特征的加权和的逻辑回归模型先前在约1,700名确诊哮喘患者的数据集上进行了训练和验证类似数量的健康对照。相同的模型已显示出对慢性阻塞性肺疾病（COPD）患者的普遍性，间质性肺病（ILD），还有咳嗽.在本研究中,共有497名参与者(46%为男性，54%女性；94%<65岁，6%>=65岁；51%马拉地语，45%英语,5％的西班牙语使用者）在美国和印度的四个临床站点注册，并在其个人智能手机上提供语音样本和症状报告。参与者包括有症状的COVID-19阳性和阴性患者以及无症状的健康志愿者。通过与RT-PCR证实的COVID-19的临床诊断进行比较，评估了RRVB模型的性能。
结果：RRVB模型区分呼吸系统疾病患者的能力与健康对照以前在哮喘的验证数据上得到了证明，COPD,ILD和咳嗽的比值比分别为4.3、9.1、3.1和3.9。本研究在COVID-19中进行的RRVB模型相同，灵敏度为73.2%，特异性为62.9%，比值比为4.64（p<0.0001）。出现呼吸道症状的患者比未出现呼吸道症状和完全无症状的患者更频繁地检测到（78.4％vs.67.4%与68.0%）。
结论：RRVB模型在呼吸条件下显示出良好的泛化性，地理位置，和语言。COVID-19的结果表明，它有可能作为一种预筛查工具，用于结合温度和症状报告识别有COVID-19感染风险的受试者。虽然不是COVID-19测试，这些结果表明，RRVB模型可以鼓励有针对性的测试。此外，该模型在不同的语言和地理环境中检测呼吸道症状的通用性提示了开发和验证未来用于更广泛疾病监测和监测应用的基于语音的工具的潜在途径.
背景：ClinicalTrials.gov（NCT04582331。
