Wireless Monitoring Systems for Vital Signs in Neonates and Infants: a Systematic Review

The interest in wearable wireless monitoring systems has accelerated secondary to the ongoing COVID-19 pandemic. Moreover, the alarmingly high number of infections in the pediatric population underscores a gap in monitoring these vulnerable populations, particularly in the home setting. This systematic review aims to identify and assess currently available wearables used to monitor cardiopulmonary function in infants and neonates. The study, prospectively registered on PROSPERO (CRD42020200642), completed a search of PubMed 1946-, Embase 1947-, Cochrane Library, Scopus 1823-, and IEEE Explore 1872-in June 2020. A total of 2324 unique citations were identified, with 16 studies describing 17 unique devices meeting inclusion criteria. Types of devices included smart clothing, belts, and mechanical adhesives, each with unique battery designs, data collection, and transmission hardware. Only four of the 17 devices underwent rigorous comparative testing, and three demonstrated correlation with the standard of care monitoring systems. Low sensitivity and specificity were reported in two commercially available consumer devices compared to the standard of care monitoring systems. The risk of bias in the entire cohort was highly based on a modified ROBINS-I scale. Further development and rigorous wearable device testing are necessary for neonatal and infant deployment.

Long-term, continuous, and multimodal monitoring of respiratory digital biomarkers via wireless epidermal mechano-acoustic sensing in clinical and home settings for COVID-19 patients (preprint)

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors represent a powerful class of technology for digital, wireless measurements of mechano-acoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. Here, we introduce an effort that integrates such an MA sensor, a cloud data infrastructure and data analytics approaches based on digital filtering and convolutional neural networks for comprehensive monitoring of COVID-19 infections in sick and healthy individuals in a population, both in the hospital and the home. This hardware/software system extracts diverse signatures of health status in an automated fashion from a single device and time series data stream. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate direct correlations between the time and intensity of coughing, speaking and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a comprehensive collection of other biometrics, with recording times for individuals of more than a month after disease diagnosis. These pilot studies include 3,111 hours of data spanning 363 days from 37 COVID-19 patients (20 females, 17 males) with 27,651 coughs detected in total along with continuous measurements of heart rate, respiratory rate, physical activity, and skin temperature. Manual labeling of randomly sampled 10,258 vocal events from 11 COVID-19 patients (6 females, 5 males) suggests a sensitivity of 85% and a specificity of 96% in cough detection using automated algorithms. The collective results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology also opens opportunities to study patterns in biometrics across individuals and among different demographic groups.

A Single-Center Retrospective and Descriptive Study of Hepatobiliary and Pancreatic Surgical Oncology during the COVID-19 Epidemic (preprint)

Objective: In the battle against COVID-19, most medical resources in China have been directed to infected patients in Wuhan. Thus, patients with hepatobiliary pancreatic tumors who are not suffering from COVID-19 are often not given timely and effective anti-cancer treatments. In this study, we aimed to describe clinical characteristics, treatment, and outcomes of patients with hepatobiliary and pancreatic oncology from our department, which retained normal working during the COVID-19 epidemic. We also sought to formulate a set of standardized hospitalization and treatment processes.Methods: A retrospective and descriptive study was conducted involving patients hospitalized from February 1, 2020, to February 29, 2020 (Return to work after the Spring Festival), at our Department of Hepatobiliary and Pancreatic Surgical Oncology. Results: The study included 92 patients from 12 provinces in the north of China who underwent surgical resection at our Department of Hepatobiliary and Pancreatic Surgical Oncology during the COVID-19 epidemic. Robotic surgery was performed on 82% (75/92) of patients, while the rest underwent laparoscopic (2/92) and open surgery (15/92). Eighty-six patients had malignant tumor, and six had emergency benign diseases. Only five patients had severe pancreatic fistula, and three had biliary fistula after operation. Conclusions: The standardized hospitalization and treatment processes described in this study could prevent cross-infection of patients and still ensure timely treatment of patients with hepatobiliary and pancreatic cancers. These study findings will guide the management of surgical oncology departments and treatment of patients with hepatobiliary and pancreatic oncology during serious epidemics.

SARS-CoV-2 infection in health care workers: a retrospective analysis and a model study (preprint)

Background There had been a preliminary occurrence of human-to-human transmissions between healthcare workers (HCWs), but risk factors in the susceptibility for COVID-19, and infection patterns among HCWs have largely remained unknown. Methods Retrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (include 12 COVID-19 HCWs) from a single-center. Sleep quality and working pressure were evaluated by Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. Follow-up duration was from Dec 25, 2019, to Feb 15, 2020. Risk factors and transmission models of COVID-19 among HCWs were analyzed and constructed. Findings A high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt they were working under pressure (66.7% vs. 32.1%) than uninfected HCWs. COVID-19 HCWs had higher total scores of PSQI and NSI than uninfected HCWs. Furthermore, these scores were both positively associated with COVID-19 risk. An individual-based model (IBM) estimated the outbreak duration among HCWs in a non-typical COVID-19 ward at 62-80 days and the basic reproduction number =1.27 [1.06, 1.61]. By reducing the average contact rate per HCW by a 1.35 factor and susceptibility by a 1.40 factor, we can avoid an outbreak of the basic case among HCWs. Interpretation Poor sleep quality and high working pressure were positively associated with high risks of COVID-19. A novel IBM of COVID-19 transmission is suitable for simulating different outbreak patterns in a hospital setting.

Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China (preprint)

IAs of February 11, 2020, all prefecture-level cities in mainland China have reported confirmed cases of 2019 novel coronavirus (2019-nCoV), but the city-level epidemical dynamics is unknown. The aim of this study is to model the current dynamics of 2019-nCoV at city level and predict the trend in the next 30 days under three possible scenarios in mainland China. We developed a spatially explicit epidemic model to consider the unique characteristics of the virus transmission in individual cities. Our model considered that the rate of virus transmission among local residents is different from those with Wuhan travel history due to the self-isolation policy. We introduced a decay rate to quantify the effort of each city to gradually control the disease spreading. We used mobile phone data to obtain the number of individuals in each city who have travel history to Wuhan. This city-level model was trained using confirmed cases up to February 10, 2020 and validated by new confirmed cases on February 11, 2020. We used the trained model to predict the future dynamics up to March 12, 2020 under different scenarios: the current trend maintained, control efforts expanded, and person-to-person contact increased due to work resuming. We estimated that the total infections in mainland China would be 72172, 54348, and 149774 by March 12, 2020 under each scenario respectively. Under the current trend, all cities will show the peak point of daily new infections by February 21. This date can be advanced to February 14 with control efforts expanded or postponed to February 26 under pressure of work resuming. Except Wuhan that cannot eliminate the disease by March 12, our model predicts that 95.4%, 100%, and 75.7% cities will have no new infections by the end of February under three scenarios. The spatial pattern of our prediction could help the government allocate resources to cities that have a more serious epidemic in the next 30 days.