Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2140-2143. doi: 10.1109/EMBC46164.2021.9629794.
The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (31.2 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure (BP) and heart rate (HR) associated with 150 ARDS patients admitted to five University of California academic health centers (containing 77,972 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Our deep learning model is able to achieve 0.81 area under the curve (AUC) to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients. Since our proposed model uses only the BP and HR, it would be possible to review data prior to the first reported cases in the U.S. to validate the presence or absence of COVID-19 in our communities prior to January 2020. In addition, by utilizing wearable devices, and monitoring vital signs of subjects in everyday settings it is possible to early-detect COVID-19 without visiting a hospital or a care site.
世界受到了 COVID-19 冠状病毒的影响。在本研究进行之时,美国的感染人数居全球之首(3120 万例感染)。在感染人群中,被诊断患有急性呼吸窘迫综合征(ARDS)的患者情况更为危急,导致严重的呼吸系统衰竭。多项研究通过监测实验室指标和症状来调查 COVID-19 和 ARDS 的感染情况。不幸的是,这些方法仅局限于临床环境,且基于症状的方法效果不佳。相比之下,生命体征(例如心率)已被用于在普遍健康监测中早期检测不同的呼吸系统疾病。我们假设这些生物标志物在识别感染 COVID-19 的 ARDS 患者方面具有信息价值。在这项研究中,我们通过利用简单的生命体征来研究 COVID-19 对 ARDS 患者的影响。我们分析了与 150 名入住五家加利福尼亚大学学术医疗中心的 ARDS 患者相关的长期日常血压(BP)和心率(HR)日志(每个生命体征各包含 77972 个样本),以区分 COVID-19 检测结果呈阳性和阴性的患者。除了进行统计分析之外,我们还开发了一个深度神经网络模型,从纵向数据中提取特征。我们的深度学习模型能够达到 0.81 的曲线下面积(AUC),从而对感染 COVID-19 的 ARDS 患者与其他 ARDS 确诊患者的生命体征进行分类。由于我们提出的模型仅使用 BP 和 HR,因此可以在 2020 年 1 月之前查看美国首次报告病例之前的数据,以验证我们社区中是否存在 COVID-19。此外,通过利用可穿戴设备,以及在日常环境中监测受试者的生命体征,可以在无需前往医院或护理场所的情况下及早发现 COVID-19。