Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

• Main Authors: Ka-Chun Un, Chun-Ka Wong, Yuk-Ming Lau, Jeffrey Chun-Yin Lee, Frankie Chor-Cheung Tam, Wing-Hon Lai, Yee-Man Lau, Hao Chen, Sandi Wibowo, Xiaozhu Zhang, Minghao Yan, Esther Wu, Soon-Chee Chan, Sze-Ming Lee, Augustine Chow, Raymond Cheuk-Fung Tong, Maulik D. Majmudar, Kuldeep Singh Rajput, Ivan Fan-Ngai Hung, Chung-Wah Siu
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-02-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-021-82771-7

Abstract Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.