Chen Yibing, Cao Lu, Zhao Dahui, Meng Song, Li Dan, Li Jing, Cui Yuqi, Xie Lixin
Department of Pulmonary and Critical Care Medicine, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
Senior Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China.
Digit Health. 2025 Sep 17;11:20552076251377938. doi: 10.1177/20552076251377938. eCollection 2025 Jan-Dec.
COPD underdiagnosis persists in China due to limited spirometry access. Smart wearables enabling cough and physiological monitoring (SpO, respiratory rate) offer a scalable screening solution.
Participants were randomly allocated to training and validation cohorts. All underwent cough sound recordings, smartwatch monitoring (heart rate variability, respiratory rate, oxygen saturation), and pre-/post-bronchodilator spirometry. Machine learning algorithms extracted cough sound features to predict lung function (evaluated via MAE, Pearson correlation, and Bland-Altman analysis). These predictions were combined with physiological data in a multimodal COPD screening model, with diagnostic performance assessed against physician diagnosis.
The training cohort included 178 patients (112 males) with COPD or pulmonary dysfunctions, aged 54.42 ± 14.77 years, BMI 24.81 ± 3.73 kg/m², FVC 3.64 ± 1.09 L, and FEV 2.42 ± 0.96 L, alongside 298 healthy volunteers (151 males) aged 35.3 ± 12.35 years, BMI 22.62 ± 3.12 kg/m², FVC 3.63 ± 0.89 L, and FEV 3.14 ± 0.73 L. The validation cohort comprised 47 COPD patients (35 males) aged 65.53 ± 7.62 years, BMI 25.38 ± 4.38 kg/m², FVC 3.27 ± 0.59 L, and FEV 1.91 ± 0.50 L, and 71 healthy controls (27 males) aged 45.51 ± 12.15 years, BMI 25.79 ± 4.00 kg/m², FVC 3.35 ± 0.80 L, and FEV 2.72 ± 0.67 L. Using cough sounds, the model's mean absolute error for FEV/FVC, FVC%, and FEV% prediction was 7.4%, 10.6%, and 17.78% ( Table 3 - 5), respectively, compared to spirometry. Significant correlations were found between predicted and measured FVC (r = 0.798, P < 0.001), FEV (r = 0.752, P < 0.001), and FEV/FVC (r = 0.784, < 0.001) ( Table 6). Combined with physiological parameters, our model's overall accuracy, sensitivity, and specificity for differentiating between COPD and normal controls were 87.82%, 86.96%, and 87.73% ( Table 9).
Our wearable-based algorithm effectively screens for ventilatory dysfunction and COPD, showing potential for large-scale population screening to reduce medical burdens.
Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR2100050843; Registration Date: 2021-9-4 Clinical Trial Number: ChiCTR2100050843. https://www.chictr.org.cn/showproj.html?proj=126556.
由于肺功能仪的使用受限，慢性阻塞性肺疾病（COPD）在中国仍存在诊断不足的情况。具备咳嗽和生理监测功能（血氧饱和度、呼吸频率）的智能可穿戴设备提供了一种可扩展的筛查解决方案。
将参与者随机分配至训练队列和验证队列。所有参与者均进行咳嗽声音记录、智能手表监测（心率变异性、呼吸频率、血氧饱和度）以及支气管扩张剂使用前后的肺功能仪检测。机器学习算法提取咳嗽声音特征以预测肺功能（通过平均绝对误差、皮尔逊相关性和布兰德-奥特曼分析进行评估）。这些预测结果与生理数据相结合，形成一个多模式COPD筛查模型，并根据医生诊断评估其诊断性能。
训练队列包括178例患有COPD或肺功能障碍的患者（112例男性），年龄为54.42±14.77岁，体重指数（BMI）为24.81±3.73kg/m²，用力肺活量（FVC）为3.64±1.09L，第1秒用力呼气容积（FEV）为2.42±0.96L，以及298名健康志愿者（151例男性），年龄为35.3±12.35岁，BMI为22.62±3.12kg/m²，FVC为3.63±0.89L，FEV为3.14±0.73L。验证队列包括47例COPD患者（35例男性），年龄为65.53±7.62岁，BMI为25.38±4.38kg/m²，FVC为3.27±0.59L，FEV为1.91±0.50L，以及71名健康对照者（27例男性），年龄为45.51±12.15岁，BMI为25.79±4.00kg/m²，FVC为3.35±0.80L，FEV为2.72±0.67L。利用咳嗽声音，该模型对FEV/FVC、FVC%和FEV%预测的平均绝对误差分别为7.4%、10.6%和17.78%（表3 - 5），与肺功能仪检测结果相比。在预测的FVC（r = 0.798，P < 0.001）、FEV（r = 0.752，P < 0.001）和FEV/FVC（r = 0.784，P < 0.001）与测量值之间发现了显著相关性（表6）。结合生理参数，我们的模型区分COPD和正常对照的总体准确率、敏感性和特异性分别为87.82%、86.96%和87.73%（表9）。
我们基于可穿戴设备的算法能有效筛查通气功能障碍和COPD，显示出在大规模人群筛查中减轻医疗负担的潜力。
世界卫生组织国际临床试验注册平台中国临床试验注册中心ChiCTR2100050843；注册日期：2021年9月4日；临床试验编号：ChiCTR2100050843。https://www.chictr.org.cn/showproj.html?proj=126556。
