Accuracy of Self-Injection Locking Radar System for Vital Signs Detection During the COVID-19 Pandemic at a Hospital in Taiwan: Measuring Vital Signs Accurately with SIL Radar for Hospital Healthcare.

BACKGROUND Self-injection locking (SIL) radar uses continuous-wave radar and an injection-locked oscillator-based frequency discriminator that receives and demodulates radar signals remotely to monitor vital signs. This study aimed to compare SIL radar with traditional electrocardiogram (ECG) measurements to monitor respiratory rate (RR) and heartbeat rate (HR) during the COVID-19 pandemic at a single hospital in Taiwan. MATERIAL AND METHODS We recruited 31 hospital staff members (16 males and 15 females) for respiratory rates (RR) and heartbeat rates (HR) detection. Data acquisition with the SIL radar and traditional ECG was performed simultaneously, and the accuracy of the measurements was evaluated using Bland-Altman analysis. RESULTS To analyze the results, participates were divided into 2 groups (individual subject and multiple subjects) by gender (male and female), or 4 groups (underweight, normal weight, overweight, and obesity) by body mass index (BMI). The results were analyzed using mean bias errors (MBE) and limits of agreement (LOA) with a 95% confidence interval. Bland-Altman plots were utilized to illustrate the difference between the SIL radar and ECG monitor. In all BMI groups, results of RR were more accurate than HR, with a smaller MBE. Furthermore, RR and HR measurements of the male groups were more accurate than those of the female groups. CONCLUSIONS We demonstrated that non-contact SIL radar could be used to accurately measure HR and RR for hospital healthcare during the COVID-19 pandemic.

[Bedside monitoring of lung perfusion by electrical impedance tomography].

As a non-invasive and radiation-free bedside imaging method, electrical impedance tomography (EIT) can perform real-time regional pulmonary ventilation evaluation and pulmonary blood flow monitoring for patients, thus realizing bedside ventilation/perfusion matching visualization, effectively guiding the pathophysiological mechanism of hypoxemia, and providing a new method for the study of pulmonary blood flow. EIT has also played a unique and irreplaceable role in COVID-19 research and treatment. At the same time, as functional imaging, the operation details and image reconstruction algorithm of this technology still need to be further optimized by more researches to provide a more robust evaluation in clinical application. In this paper, EIT pulmonary blood flow monitoring methods, operation and implementation of monitoring indicators, application and related research progress will be described.

A Feasibility Study of Remote Non-Contact Vital Signs (NCVS) Monitoring in a Clinic Using a Novel Sensor Realized by Software-Defined Radio (SDR).

The COVID-19 outbreak has caused panic around the world as it is highly infectious and has caused about 5 million deaths globally. A robust wireless non-contact vital signs (NCVS) sensor system that can continuously monitor the respiration rate (RR) and heart rate (HR) of patients clinically and remotely with high accuracy can be very attractive to healthcare workers (HCWs), as such a system can not only avoid HCWs' close contact with people with COVID-19 to reduce the infection rate, but also be used on patients quarantined at home for telemedicine and wireless acute-care. Therefore, we developed a custom Doppler-based NCVS radar sensor system operating at 2.4 GHz using a software-defined radio (SDR) technology, and the novel biosensor system has achieved impressive real-time RR/HR monitoring accuracies within approximately 0.5/3 breath/beat per minute (BPM) on student volunteers tested in our engineering labs. To further test the sensor system's feasibility for clinical use, we applied and obtained an Internal Review Board (IRB) approval from Texas Tech University Health Sciences Center (TTUHSC) and have used this NCVS monitoring system in a doctor's clinic at TTUHSC; following testing on 20 actual patients for a small-scale clinical trial, we have found that the system was still able to achieve good NCVS monitoring accuracies within ~0.5/10 BPM across 20 patients of various weight, height and age. These results suggest our custom-designed NCVS monitoring system may be feasible for future clinical use to help combatting COVID-19 and other infectious diseases.

Applications of Remote Patient Monitoring.

Remote patient monitoring programs collect and analyze a variety of health-related data to detect clinical deterioration with the goal of early intervention. There are many program designs with various deployed devices, monitoring schemes, and escalation protocols. Although several factors are considered, the disease state plays a foundational role when designing a specific program. Remote patient monitoring is used both in chronic disease states and patients with acute self-limited conditions. These programs use health-related data to identify early deterioration and then successfully intervene to improve clinical outcomes and decrease costs of care.

Impact of the COVID-19 Pandemic on Continuous EEG Utilization.

PURPOSE: The coronavirus disease 2019 (COVID-19) has significantly impacted healthcare delivery and utilization. The aim of this article was to assess the impact of the COVID-19 pandemic on in-hospital continuous electroencephalography (cEEG) utilization and identify areas for process improvement. METHODS: A 38-question web-based survey was distributed to site principal investigators of the Critical Care EEG Monitoring Research Consortium, and institutional contacts for the Neurodiagnostic Credentialing and Accreditation Board. The survey addressed the following aspects of cEEG utilization: (1) general center characteristics, (2) cEEG utilization and review, (3) staffing and workflow, and (4) health impact on EEG technologists. RESULTS: The survey was open from June 12, 2020 to June 30, 2020 and distributed to 174 centers with 79 responses (45.4%). Forty centers were located in COVID-19 hotspots. Fifty-seven centers (72.1%) reported cEEG volume reduction. Centers in the Northeast were most likely to report cEEG volume reduction (odds ratio [OR] 7.19 [1.53-33.83]; P = 0.012). Additionally, centers reporting decrease in outside hospital transfers reported cEEG volume reduction; OR 21.67 [4.57-102.81]; P ≤ 0.0001. Twenty-six centers (32.91%) reported reduction in EEG technologist coverage. Eighteen centers had personal protective equipment shortages for EEG technologists. Technologists at these centers were more likely to quarantine for suspected or confirmed COVID-19; OR 3.14 [1.01-9.63]; P = 0.058. CONCLUSIONS: There has been a widespread reduction in cEEG volume during the pandemic. Given the anticipated duration of the pandemic and the importance of cEEG in managing hospitalized patients, methods to optimize use need to be prioritized to provide optimal care. Because the survey provides a cross-sectional assessment, follow-up studies can determine the long-term impact of the pandemic on cEEG utilization.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors.

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation.

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt.

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene's test, the Kruskal-Wallis test, and Dunn's post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.

A 360 degree mixed-methods evaluation of a specialized COVID-19 outpatient clinic and remote patient monitoring program.

BACKGROUND: Our goals are to quantify the impact on acute care utilization of a specialized COVID-19 clinic with an integrated remote patient monitoring program in an academic medical center and further examine these data with stakeholder perceptions of clinic effectiveness and acceptability. METHODS: A retrospective cohort was drawn from enrolled and unenrolled ambulatory patients who tested positive in May through September 2020 matched on age, presence of comorbidities and other factors. Qualitative semi-structured interviews with patients, frontline clinician, and administrators were analyzed in an inductive-deductive approach to identify key themes. RESULTS: Enrolled patients were more likely to be hospitalized than unenrolled patients (N = 11/137 in enrolled vs 2/126 unenrolled, p = .02), reflecting a higher admittance rate following emergency department (ED) events among the enrolled vs unenrolled, though this was not a significant difference (46% vs 25%, respectively, p = .32). Thirty-eight qualitative interviews conducted June to October 2020 revealed broad stakeholder belief in the clinic's support of appropriate care escalation. Contrary to beliefs the clinic reduced inappropriate care utilization, no difference was seen between enrolled and unenrolled patients who presented to the ED and were not admitted (N = 10/137 in enrolled vs 8/126 unenrolled, p = .76). Administrators and providers described the clinic's integral role in allowing health services to resume in other areas of the health system following an initial lockdown. CONCLUSIONS: Acute care utilization and multi-stakeholder interviews suggest heightened outpatient observation through a specialized COVID-19 clinic and remote patient monitoring program may have contributed to an increase in appropriate acute care utilization. The clinic's role securing safe reopening of health services systemwide was endorsed as a primary, if unmeasured, benefit.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19.

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.

Electrical impedance tomography in the adult intensive care unit: clinical applications and future directions.

PURPOSE OF REVIEW: Electrical impedance tomography (EIT) is a novel, noninvasive, radiation-free, bedside imaging and monitoring tool to assess and visualize regional distribution of lung ventilation and perfusion. Although primarily a research tool, rapidly emerging data are beginning to define its clinical role, and it is poised to become a ubiquitous addition to the arsenal of the intensive care unit (ICU). In this review, we summarize the data supporting clinical use of EIT in adult ICUs, with an emphasis on appropriate application while highlighting future directions. RECENT FINDINGS: Recent major studies have primarily focused on the role of EIT in setting correct positive end-expiratory pressure to balance regional overdistention and collapse. Over the last few years, our Lung Rescue Team has demonstrated that incorporating EIT into a multimodal approach to individualizing ventilator management can improve outcomes, particularly in the obese. We also review recent data surrounding EIT use during COVID, as well as other broad potential applications. SUMMARY: As EIT becomes more common and its clinical role more defined, intensivists will benefit from a clear understanding of its applications and limitations.

Remote Patient Monitoring During COVID-19: An Unexpected Patient Safety Benefit.

This Viewpoint explains how some hospitals used home monitoring of pulse oximetry during the COVID-19 pandemic to avoid patient overcrowding and control high patient to staff ratios and how increased use of home monitoring for other vital signs could potentially improve patient safety and decrease costs.

Telemonitoring for the Follow-Up of Obstructive Sleep Apnea Patients Treated with CPAP: Accuracy and Impact on Therapy.

Continuous positive airway pressure (CPAP) telemonitoring (TMg) has become widely implemented in routine clinical care. Objective measures of CPAP compliance, residual respiratory events, and leaks can be easily monitored, but limitations exist. This review aims to assess the role of TMg in CPAP-treated obstructive sleep apnea (OSA) patients. We report recent data related to the accuracy of parameters measured by CPAP and try to determine the role of TMg in CPAP treatment follow-up, from the perspective of both healthcare professionals and patients. Measurement and accuracy of CPAP-recorded data, clinical management of these data, and impacts of TMg on therapy are reviewed in light of the current literature. Moreover, the crucial questions of who and how to monitor are discussed. TMg is a useful tool to support, fine-tune, adapt, and control both CPAP efficacy and compliance in newly-diagnosed OSA patients. However, clinicians should be aware of the limits of the accuracy of CPAP devices to measure residual respiratory events and leaks and issues such as privacy and cost-effectiveness are still a matter of concern. The best methods to focus our efforts on the patients who need TMg support should be properly defined in future long-term studies.

Non-Invasive Monitoring for Rejection in Kidney Transplant Recipients After SARS-CoV-2 mRNA Vaccination.

Introduction: Studies have shown reduced antiviral responses in kidney transplant recipients (KTRs) following SARS-CoV-2 mRNA vaccination, but data on post-vaccination alloimmune responses and antiviral responses against the Delta (B.1.617.2) variant are limited. Materials and methods: To address this issue, we conducted a prospective, multi-center study of 58 adult KTRs receiving mRNA-BNT162b2 or mRNA-1273 vaccines. We used multiple complementary non-invasive biomarkers for rejection monitoring including serum creatinine, proteinuria, donor-derived cell-free DNA, peripheral blood gene expression profile (PBGEP), urinary CXCL9 mRNA and de novo donor-specific antibodies (DSA). Secondary outcomes included development of anti-viral immune responses against the wild-type and Delta variant of SARS-CoV-2. Results: At a median of 85 days, no KTRs developed de novo DSAs and only one patient developed acute rejection following recent conversion to belatacept, which was associated with increased creatinine and urinary CXCL9 levels. During follow-up, there were no significant changes in proteinuria, donor-derived cell-free DNA levels or PBGEP. 36% of KTRs in our cohort developed anti-wild-type spike antibodies, 75% and 55% of whom had neutralizing responses against wild-type and Delta variants respectively. A cellular response against wild-type S1, measured by interferon-γ-ELISpot assay, developed in 38% of KTRs. Cellular responses did not differ in KTRs with or without antibody responses. Conclusions: SARS-CoV-2 mRNA vaccination in KTRs did not elicit a significant alloimmune response. About half of KTRs who develop anti-wild-type spike antibodies after two mRNA vaccine doses have neutralizing responses against the Delta variant. There was no association between anti-viral humoral and cellular responses.

Pro-active monitoring and social interventions at community level mitigate the impact of coronavirus (COVID-19) epidemic on older adults' mortality in Italy: A retrospective cohort analysis.

BACKGROUND: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. METHODS: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. RESULTS: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1-35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7-33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71-0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). CONCLUSIONS: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19.

Remote monitoring in telehealth care delivery across the U.S. cystic fibrosis care network.

BACKGROUND: Cystic fibrosis (CF) programs and people with CF (PwCF) employed various monitoring methods for virtual care during the COVID-19 pandemic. This paper characterizes experiences with remote monitoring across the U.S. CF community. METHODS: The CF Foundation (CFF) sponsored distribution of home spirometers (April 2020 to May 2021), surveys to PwCF and CF programs (July to September 2020), and a second program survey (April to May 2021). We used mixed methods to explore access, use, and perspectives regarding the use of remote monitoring in future care. RESULTS: By October 2020, 13,345 spirometers had been distributed, and 19,271 spirometers by May 2021. Programs (n=286) estimated proportions of PwCF with home devices increased over seven months: spirometers (30% to 70%), scales (50% to 70%), oximeters (5% to 10%) with higher estimates in adult programs for spirometers and oximeters. PwCF (n=378) had access to scales (89%), followed by oximeters (48%) and spirometers (47%), often using scales and oximeters weekly, and spirometers monthly. Over both surveys, some programs had no method to collect respiratory specimens for cultures associated with telehealth visits (47%, n=132; 41%, n=118). Most programs (81%) had a process for phlebotomy associated with a telehealth visit, primarily through off-site labs. Both PwCF and programs felt future care should advance remote monitoring and recommended improvements for access, training, and data collection systems. CONCLUSIONS: PwCF and programs experienced unprecedented access to remote monitoring and raised its importance for future care. Improvements to current systems may leverage these shared experiences to augment future care models.

Respiratory support after delayed cord clamping: a prospective cohort study of at-risk births at ≥35+0 weeks gestation.

OBJECTIVE: To identify risk factors associated with delivery room respiratory support in at-risk infants who are initially vigorous and received delayed cord clamping (DCC). DESIGN: Prospective cohort study. SETTING: Two perinatal centres in Melbourne, Australia. PATIENTS: At-risk infants born at ≥35+0 weeks gestation with a paediatric doctor in attendance who were initially vigorous and received DCC for >60 s. MAIN OUTCOME MEASURES: Delivery room respiratory support defined as facemask positive pressure ventilation, continuous positive airway pressure and/or supplemental oxygen within 10 min of birth. RESULTS: Two hundred and ninety-eight infants born at a median (IQR) gestational age of 39+3 (38+2-40+2) weeks were included. Cord clamping occurred at a median (IQR) of 128 (123-145) s. Forty-four (15%) infants received respiratory support at a median of 214 (IQR 156-326) s after birth. Neonatal unit admission for respiratory distress occurred in 32% of infants receiving delivery room respiratory support vs 1% of infants who did not receive delivery room respiratory support (p<0.001). Risk factors independently associated with delivery room respiratory support were average heart rate (HR) at 90-120 s after birth (determined using three-lead ECG), mode of birth and time to establish regular cries. Decision tree analysis identified that infants at highest risk had an average HR of <165 beats per minute at 90-120 s after birth following caesarean section (risk of 39%). Infants with an average HR of ≥165 beats per minute at 90-120 s after birth were at low risk (5%). CONCLUSIONS: We present a clinical decision pathway for at-risk infants who may benefit from close observation following DCC. Our findings provide a novel perspective of HR beyond the traditional threshold of 100 beats per minute.