Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
JMIR Mhealth Uhealth. 2021 May 6;9(5):e22591. doi: 10.2196/22591.
The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality.
The aim of this study was to develop a prediction system using lifestyle data, environmental factors, and patient symptoms for the early detection of AECOPD in the upcoming 7 days.
This prospective study was performed at National Taiwan University Hospital. Patients with COPD that did not have a pacemaker and were not pregnant were invited for enrollment. Data on lifestyle, temperature, humidity, and fine particulate matter were collected using wearable devices (Fitbit Versa), a home air quality-sensing device (EDIMAX Airbox), and a smartphone app. AECOPD episodes were evaluated via standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models, including random forest, decision trees, k-nearest neighbor, linear discriminant analysis, and adaptive boosting, and a deep neural network model.
The continuous real-time monitoring of lifestyle and indoor environment factors was implemented by integrating home air quality-sensing devices, a smartphone app, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean 4-month follow-up period, resulting in the detection of 25 AECOPD episodes. For 7-day AECOPD prediction, the proposed AECOPD predictive model achieved an accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. Receiver operating characteristic curve analysis showed that the area under the curve of the model in predicting AECOPD was greater than 0.9. The most important variables in the model were daily steps walked, stairs climbed, and daily distance moved.
Using wearable devices, home air quality-sensing devices, a smartphone app, and supervised prediction algorithms, we achieved excellent power to predict whether a patient would experience AECOPD within the upcoming 7 days. The AECOPD prediction system provided an effective way to collect lifestyle and environmental data, and yielded reliable predictions of future AECOPD events. Compared with previous studies, we have comprehensively improved the performance of the AECOPD prediction model by adding objective lifestyle and environmental data. This model could yield more accurate prediction results for COPD patients than using only questionnaire data.
世界卫生组织预计，到 2030 年，慢性阻塞性肺疾病（COPD）将成为全球第三大死亡原因和第七大发病原因。慢性阻塞性肺疾病急性加重（AECOPD）与肺功能加速下降、生活质量下降和死亡率升高有关。准确地早期发现急性加重将有助于早期管理并降低死亡率。
本研究旨在开发一种基于生活方式数据、环境因素和患者症状的预测系统，以早期检测未来 7 天内的 AECOPD。
本前瞻性研究在台湾大学医院进行。邀请患有 COPD 且没有起搏器和未怀孕的患者参加。使用可穿戴设备（Fitbit Versa）、家庭空气质量感应设备（EDIMAX Airbox）和智能手机应用程序收集生活方式、温度、湿度和细颗粒物数据。通过标准化问卷评估 AECOPD 发作情况。利用这些输入特征，我们评估了机器学习模型（包括随机森林、决策树、k-最近邻、线性判别分析和自适应增强）和深度神经网络模型的预测性能。
通过整合家庭空气质量感应设备、智能手机应用程序和可穿戴设备，实现了对生活方式和室内环境因素的连续实时监测。在平均 4 个月的随访期间，对 67 名 COPD 患者的所有数据进行了前瞻性收集，共检测到 25 例 AECOPD 发作。对于 7 天的 AECOPD 预测，提出的 AECOPD 预测模型的准确率为 92.1%，灵敏度为 94%，特异性为 90.4%。受试者工作特征曲线分析显示，该模型预测 AECOPD 的曲线下面积大于 0.9。模型中最重要的变量是每天行走的步数、爬楼梯的次数和每天移动的距离。
使用可穿戴设备、家庭空气质量感应设备、智能手机应用程序和监督预测算法，我们实现了对患者未来 7 天内是否会发生 AECOPD 的出色预测能力。AECOPD 预测系统提供了一种有效的方法来收集生活方式和环境数据，并可靠地预测未来的 AECOPD 事件。与之前的研究相比，我们通过添加客观的生活方式和环境数据，全面提高了 AECOPD 预测模型的性能。与仅使用问卷数据相比，该模型可为 COPD 患者提供更准确的预测结果。
