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      Remote Respiratory Monitoring in the Time of COVID-19

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          Abstract

          Introduction The COVID-19 pandemic has created an unprecedented need for remote patient monitoring. At the time of writing this article, the majority of countries worldwide are on lockdown to minimize the spread of the virus, and most of the patients who tested (or are suspected to be) positive for COVID-19 are in self-isolation at home. Even robust healthcare systems are facing a shortage of healthcare professionals, personal protective equipment, beds, and mechanical ventilators in intensive-care-units (ICU) (Kissler et al., 2020), thus highlighting the need for alternative medical solutions, including remote patient monitoring (Alwashmi, 2020; FDA, 2020; Ohannessian et al., 2020). New policies have therefore been introduced to promote the development of monitoring devices (FDA, 2020), thus creating favorable opportunities to improve the remote monitoring of some overlooked vital signs. This is especially the case for respiratory rate (RR), which is currently poorly recorded despite its relevance in the context of COVID-19. Current COVID-19 guidelines suggest measuring resting RR to inform triage decisions, diagnosis, prognosis, and as a criterion for ICU admission and for the early recognition of COVID-19 patient deterioration. The World Health Organization indicates that a resting value of RR > 30 breaths/min is a critical sign for the diagnosis of severe pneumonia in adults, while the cut-off value for children varies according to age (World Health Organization, 2020). At triage, RR values are used to support the assignment of patients to different categories and to make decisions on the use of supplemental oxygen (Ayebare et al., 2020; Italian Thoracic Society Italian Respiratory Society, 2020). The treatment of patients affected by acute respiratory insufficiency from COVID-19 is also tailored considering RR values (Italian Thoracic Society Italian Respiratory Society, 2020). Furthermore, RR helps with the timely recognition of COVID-19 patient deterioration, thus contributing to the implementation of early intervention strategies (Sun et al., 2020). Resting RR values also contribute to the prognosis of COVID-19 patients as ICU admission and mortality are associated with significantly higher RR values compared to non-ICU patients and survivors (Huang et al., 2020; Zhou et al., 2020). Besides, the normalization of resting RR is among the signs used to quantify the time to clinical recovery from COVID-19 infection (Al-Tawfiq et al., 2020). The clinical relevance of RR mandates the improvement of the accuracy of RR measurements in response to the COVID-19 pandemic. RR measurements too often rely on manual counting in the clinical setting and are often poorly performed out of the hospital. For instance, guidelines for telephone consultation of COVID-19 patients recognize the fundamental role of RR in the assessment of patients, but propose unsatisfying solutions for its remote assessment (Greenhalgh et al., 2020). Since patients usually have no direct access to respiratory devices, RR is self-reported by answering the question “Is your breathing faster, slower, or the same as normal?” (Greenhalgh et al., 2020). However, experimental data discourage the self-report of RR and highlight how measurement awareness affects resting RR values (Hill et al., 2018). On the other hand, several sensors and techniques can be used for the accurate remote monitoring of RR; some of these are ready-to-use in the face of the COVID-19 challenge, while others deserve further consideration in the future. Technological Solutions for Remote Respiratory Rate Monitoring The abundance of techniques currently available can satisfy the various needs of COVID-19 patients with different severities of symptoms (see Figure 1). We can differentiate techniques for patients that need (1) periodic short-term screening or (2) continuous monitoring. The majority of COVID-19 patients have mild symptoms and can be screened periodically (e.g., twice a day) for vital signs. These patients can take advantage of emerging technologies exploiting built-in cameras of smartphones, tablets, and laptops to record RR from the respiratory-induced chest wall movements or superficial changes in face perfusion (Poh et al., 2011; Brüser et al., 2015). By registering a short video capturing the torso area or the face of the seated patient, RR (and other vital signs like heart rate) can be streamed to healthcare professionals using an internet connection (Brüser et al., 2015). This technique is discreet, available in the market, and shows medical-grade accuracy (error <1 breaths/min) when the patient is stationary (Massaroni et al., 2019a). The wide spread use of smart devices makes camera-based solutions immediately available, relatively low-cost, and user friendly. Furthermore, the availability of smart devices is becoming fundamental in the time of COVID-19, and healthcare systems can benefit from it. Figure 1 Technological solutions that can be used for the remote RR monitoring of COVID-19 patients. (A) The built-in camera of a smart device can be used to record RR from the respiratory-induced chest wall movements or superficial changes in face perfusion of a seated patient. (B) The built-in microphone of a smartphone can be used to record RR from the breathing sounds of the patient. (C) An instrumented mattress can be adopted for continuous RR monitoring by registering the breathing-related chest wall movements of the patient. (D) Radio wave or Wi-Fi signal sources and receivers can be used for registering RR values through the modulation of the transmitted signals by respiratory-related thoracic movements (no body sensors are needed). (E) A smart garment (e.g., a strap with conductive strain sensors) can be used to record RR continuously from the respiratory-related periodic changes in chest wall circumference, even during daily activities (e.g., walking). For patients needing continuous monitoring (e.g., older adults, patients with comorbidities or more severe symptoms), prolonged acquisition through camera-based methods is not recommended given the higher amount of data to be processed. In these cases, smart devices offer another ready-to-use technological solution, i.e., the recording of breathing sounds using built-in microphones (Brüser et al., 2015; Nam et al., 2015). Currently available systems have implemented this technology for continuous RR monitoring and unobtrusive sleep apnea detection in quiet environments, with good results in terms of accuracy (error <1%) (Nam et al., 2015). Other solutions available in the market monitor RR continuously with strain or pressure sensors installed underneath mattresses, under bedposts, and on the seating area and backrest of chairs (Watanabe et al., 2005; Brüser et al., 2015). These solutions are discreet, relatively low-cost (~100€), and show medical grade-accuracy (error <2 breaths/min) (Brüser et al., 2015). Furthermore, promising technologies for continuous RR monitoring exploit the modulation of radio waves and Wi-Fi signals by respiratory-related thoracic movements (Lai et al., 2011; Abdelnasser et al., 2015). These waves or signals can be generated by radio sources or traditional modems for internet connection, and have been used to monitor the RR of patients in different postures (seated, standing and lying on the bed) (Brüser et al., 2015). Some of these technological solutions are incredibly small, but are still under development and unavailable in the market (Brüser et al., 2015). Patients that need continuous vital sign monitoring, even during everyday-life activities, can be equipped with wearable devices like smart garments (e.g., t-shirts or chest bands embedding sensors) (Massaroni et al., 2019b). Unlike most of the aforementioned technologies, smart garments may provide accurate and robust RR values even during daily activities. Several commercial solutions are available in the market with prices around 300€, although further efforts are required to advance the field of respiratory monitoring with wearable devices (Massaroni et al., 2019b). Altogether, a variety of technological solutions are already available for the accurate remote monitoring of RR, and a multidisciplinary approach is required to implement these techniques effectively in medical surveillance programs. Discussion Accurate remote RR monitoring is expected to play an important role in the context of the COVID-19 pandemic. It may facilitate healthcare assistance for self-isolated COVID-19 patients as well as for all patients that have restricted access to medical services in this time of crisis. The improvement of remote patient monitoring would also favor the implementation of timely and cost-effective healthcare services, including the early warning of patient deterioration, remote triage, and home monitoring of COVID-19 patients discharged from hospitals. This would help mitigate the burden on hospitals, decrease the risk of infection for healthcare professionals, and thus reduce virus transmission. The availability of a large number of accurate RR data will also contribute to improving the development of predictive models for the risk of hospital admission, and the development of diagnostic and prognostic models. Finally, we argue that an accurate remote monitoring of RR is essential and should be performed alongside the monitoring of other vital signs. Ready-to-use technological solutions are available to accomplish this goal. Effective RR monitoring would be an important contribution to facing the current COVID-19 crisis and managing similar scenarios that may possibly occur in the next months or years (Kissler et al., 2020). Author Contributions CM, AN, ES, and MS contributed to the conception and design of the work, manuscript writing, critical revision of the article, and approval of the final version of the article. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

            Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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              Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

              It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained since a resurgence in contagion could be possible as late as 2024.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                29 May 2020
                2020
                29 May 2020
                : 11
                : 635
                Affiliations
                [1] 1Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma , Rome, Italy
                [2] 2Department of Movement, Human and Health Sciences, University of Rome “Foro Italico” , Rome, Italy
                Author notes

                Edited by: Yu Ru Kou, National Yang-Ming University, Taiwan

                Reviewed by: Iulia Ioan, Centre Hospitalier Universitaire de Toulouse, France

                *Correspondence: Carlo Massaroni c.massaroni@ 123456unicampus.it

                This article was submitted to Respiratory Physiology, a section of the journal Frontiers in Physiology

                †These authors have contributed equally to this work

                †These authors have contributed equally to this work and share same authorship

                Article
                10.3389/fphys.2020.00635
                7274133
                32038307
                9cccb226-4a0f-417b-8b73-27b666936b80
                Copyright © 2020 Massaroni, Nicolò, Schena and Sacchetti.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 April 2020
                : 18 May 2020
                Page count
                Figures: 1, Tables: 0, Equations: 0, References: 21, Pages: 4, Words: 2365
                Categories
                Physiology
                Opinion

                Anatomy & Physiology
                respiratory rate,remote patient monitoring,technologies,vital signs,accurate measurement,coronavirus

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