Ratio of Oxygen Saturation to Inspired Oxygen, ROX Index, Modified ROX Index to Predict High Flow Cannula Success in COVID-19 Patients: Multicenter Validation Study.

INTRODUCTION: High-flow nasal cannula (HFNC) is a respiratory support measure for coronavirus 2019 (COVID-19) patients that has been increasingly used in the emergency department (ED). Although the respiratory rate oxygenation (ROX) index can predict HFNC success, its utility in emergency COVID-19 patients has not been well-established. Also, no studies have compared it to its simpler component, the oxygen saturation to fraction of inspired oxygen (SpO2/FiO2 [SF]) ratio, or its modified version incorporating heart rate. Therefore, we aimed to compare the utility of the SF ratio, the ROX index (SF ratio/respiratory rate), and the modified ROX index (ROX index/heart rate) in predicting HFNC success in emergency COVID-19 patients. METHODS: We conducted this multicenter retrospective study at five EDs in Thailand between January-December 2021. Adult patients with COVID-19 treated with HFNC in the ED were included. The three study parameters were recorded at 0 and 2 hours. The primary outcome was HFNC success, defined as no requirement of mechanical ventilation at HFNC termination. RESULTS: A total of 173 patients were recruited; 55 (31.8%) had successful treatment. The two-hour SF ratio yielded the highest discrimination capacity (AUROC 0.651, 95% CI 0.558-0.744), followed by two-hour ROX and modified ROX indices (AUROC 0.612 and 0.606, respectively). The two-hour SF ratio also had the best calibration and overall model performance. At its optimal cut-point of 128.19, it gave a balanced sensitivity (65.3%) and specificity (61.8%). The two-hour SF≥128.19 was also significantly and independently associated with HFNC failure (adjusted odds ratio 0.29, 95% CI 0.13-0.65; P=0.003). CONCLUSION: The SF ratio predicted HFNC success better than the ROX and modified ROX indices in ED patients with COVID-19. With its simplicity and efficiency, it may be the appropriate tool to guide management and ED disposition for COVID-19 patients receiving HFNC in the ED.

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.

A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19.

BACKGROUND: Patients admitted to the emergency care setting with COVID-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to ICU or having fatal outcome vs. those that were not. METHODS: Two wireless sensors continuously monitored (CM) respiratory rate (RR), heart rate (HR), and peripheral arterial oxygen saturation (SpO2 ). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score (NEWS). RESULTS: SpO2 < 92% for more than 60 min was detected in 92% of the patients with CM vs. 40% with NEWS (p < .00001). RR > 24 breaths per minute for more than 5 min were detected in 70% with CM vs. 33% using NEWS (p = .0001). HR ≥ 111 for more than 60 min was seen in 51% with CM and 22% with NEWS (p = .0002). Patients admitted to ICU or having fatal outcome had longer durations of RR > 24 brpm (p = .01), RR > 21 brpm (p = .01), SpO2 < 80% (p = .01), and SpO2 < 85% (p = .02) compared to patients that were not. CONCLUSION: Episodes of desaturation and tachypnea in hospitalized patients with COVID-19 infection are common and often not detected by routine measurements.

Breath-by-breath P0.1 measured on quasi-occlusion via Hamilton C6 may result in underestimation of respiratory drive and inspiratory effort.

We aimed to identify the threshold for P0.1 in a breath-by-breath manner measured by the Hamilton C6 on quasi-occlusion for high respiratory drive and inspiratory effort. In this prospective observational study, we analyzed the relationships between airway P0.1 on quasi-occlusion and esophageal pressure (esophageal P0.1 and esophageal pressure swing). We also conducted a linear regression analysis and derived the threshold of airway P0.1 on quasi-occlusion for high respiratory drive and inspiratory effort. We found that airway P0.1 measured on quasi-occlusion had a strong positive correlation with esophageal P0.1 measured on quasi-occlusion and esophageal pressure swing, respectively. Additionally, the P0.1 threshold for high respiratory drive and inspiratory effort were calculated at approximately 1.0 cmH2O from the regression equations. Our calculations suggest a lower threshold of airway P0.1 measured by the Hamilton C6 on quasi-occlusion than that which has been previously reported.

Clinical validation of a contactless respiration rate monitor.

Respiratory rate (RR) is an often underestimated and underreported vital sign with tremendous clinical value. As a predictor of cardiopulmonary arrest, chronic obstructive pulmonary disease (COPD) exacerbation or indicator of health state for example in COVID-19 patients, respiratory rate could be especially valuable in remote long-term patient monitoring, which is challenging to implement. Contactless devices for home use aim to overcome these challenges. In this study, the contactless Sleepiz One+ respiration monitor for home use during sleep was validated against the thoracic effort belt. The agreement of instantaneous breathing rate and breathing rate statistics between the Sleepiz One+ device and the thoracic effort belt was initially evaluated during a 20-min sleep window under controlled conditions (no body movement) on a cohort of 19 participants and secondly in a more natural setting (uncontrolled for body movement) during a whole night on a cohort of 139 participants. Excellent agreement was shown for instantaneous breathing rate to be within 3 breaths per minute (Brpm) compared to thoracic effort band with an accuracy of 100% and mean absolute error (MAE) of 0.39 Brpm for the setting controlled for movement, and an accuracy of 99.5% with a MAE of 0.48 Brpm for the whole night measurement, respectively. Excellent agreement was also achieved for the respiratory rate statistics over the whole night with absolute errors of 0.43, 0.39 and 0.67 Brpm for the 10th, 50th and 90th percentiles, respectively. Based on these results we conclude that the Sleepiz One+ can estimate instantaneous respiratory rate and its summary statistics at high accuracy in a clinical setting. Further studies are required to evaluate the performance in the home environment, however, it is expected that the performance is at similar level, as the measurement conditions for the Sleepiz One+ device are better at home than in a clinical setting.

Performing moderate to severe activity is safe and tolerable for healthy youth while wearing a cloth facemask.

OBJECTIVE: To investigate if a cloth facemask could affect physiological and perceptual responses to exercise at distinct exercise intensities in healthy young individuals. METHODS: Nine participants (sex, female/male: 6/3; age: 13±1 years; VO2peak: 44.5±5.5 mL/kg/min) underwent a progressive square-wave test at four intensities: (1) 80% of ventilatory anaerobic threshold (VAT), (2) VAT, and (3) 40% between VAT and [Formula: see text] wearing a triple-layered cloth facemask or not. Participants then completed a final stage to exhaustion at a running speed equivalent to the maximum achieved during the cardio-respiratory exercise test (Peak). Physiological, metabolic, and perceptual measures were measured. RESULTS: Mask did not affect spirometry (forced vital capacity, peak expiratory flow, forced expiratory volume; all p≥0.27), respiratory (inspiratory capacity, end-expiratory volume [EELV] to functional vital capacity ratio, EELV, respiratory frequency [Rf], tidal volume [VT], Rf/VT, end-tidal carbo dioxide pressure, ventilatory equivalent to carbon dioxide ratio; all p≥0.196), hemodynamic (heart rate, systolic and diastolic blood pressure; all p>0.41), ratings of perceived exertion (p = 0.04) or metabolic measures (lactate; p = 0.78) at rest or at any exercise intensity. CONCLUSIONS: This study shows that performing moderate to severe activity is safe and tolerable for healthy youth while wearing a cloth facemask. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04887714.

Rapid systematic review of respiratory rate as a vital sign of clinical deterioration in COVID-19.

OBJECTIVES: This meta-analysis aimed to establish a clinical evidence base for respiratory rate (RR) as a single predictor of early-onset COVID-19. The review also looked to determine the practical implementation of mobile respiratory rate measuring devices where information was available. METHODS: We focused on domestic settings with older adults. Relevant studies were identified through MEDLINE, Embase, and CENTRAL databases. A snowballing method was also used. Articles published from the beginning of the COVID-19 pandemic (2019) until Feb 2022 were selected. Databases were searched for terms related to COVID-19 and respiratory rate measurements in domestic patients. RESULTS: A total of 2,889 articles were screened for relevant content, of which 60 full-text publications were included. We compared the Odds Ratios and statistically significant results of both vital signs. CONCLUSION: Multinational studies across dozens of countries have shown respiratory rate to have predictive accuracy in detecting COVID-19 deterioration. However, considerable variability is present in the data, making it harder to be sure about the effects. There is no meaningful difference in data quality in terms of variability (95% CI intervals) between vital signs as predictors of decline in COVID-19 patients. Contextual and economic factors will likely determine the choice of measurement used.

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.

Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients.

BACKGROUND: Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks. METHODS: When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected. RESULTS: In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events. CONCLUSION: Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.

Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients.

Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.

Numerical study of transient indoor airflow and virus-laden droplet dispersion: Impact of interactive human movement.

Human movement affects indoor airflow and the airborne transmission of respiratory infectious diseases, which has attracted scholars. However, the interactive airflow between moving and stationary people has yet to be studied in detail. This study used the numerical method validated by experimental data to explore the transient indoor airflow and virus-laden droplet dispersion in scenes with interactive human movement. Human-shaped numerical models and the dynamic mesh method were adopted to realize human movement in scenes with different lateral distances (0.2-1.2 m) between a moving person and stationary (standing/sitting) persons. The interactive human movement obviously impacts other persons' respiratory airflow, and the lateral fusion ranged about 0.6 m. The interactive human movement strengthens the indoor airflow convection, and some exhaled virus-laden droplets were carried into wake flow and enhanced long-range airborne transmission. The impact of interactive human movement on sitting patients' exhalation airflow seems more evident than on standing patients. The impact might last over 2 min after movement stopped, and people in the affected area might be at a higher exposure. The results can provide a reference for epidemic control in indoor environments.

The value of ROX index in predicting the outcome of high flow nasal cannula: a systematic review and meta-analysis.

BACKGROUND: High flow nasal cannula (HFNC) therapy is widely employed in acute hypoxemic respiratory failure (AHRF) patients. However, the techniques for predicting HFNC outcome remain scarce. METHODS: PubMed, EMBASE, and Cochrane Library were searched until April 20, 2021. We included the studies that evaluated the potential predictive value of ROX (respiratory rate-oxygenation) index for HFNC outcome. This meta-analysis determined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic score, diagnostic odds ratio (DOR), and pooled area under the summary receiver operating characteristic (SROC) curve. RESULTS: We assessed nine studies with 1933 patients, of which 745 patients experienced HFNC failure. This meta-analysis found that sensitivity, specificity, PLR, NLR, diagnostic score, and DOR of ROX index in predicting HFNC failure were 0.67 (95% CI 0.57-0.76), 0.72 (95% CI 0.65-0.78), 2.4 (95% CI 2.0-2.8), 0.46 (95% CI 0.37-0.58), 1.65(95% CI 1.37-1.93), and 5.0 (95% CI 4.0-7.0), respectively. In addition, SROC was 0.75 (95% CI 0.71-0.79). Besides, our subgroup analyses revealed that ROX index had higher sensitivity and specificity for predicting HFNC failure in COVID-19 patients, use the cut-off value > 5, and the acquisition time of other times after receiving HFNC had a greater sensitivity and specificity when compared to 6 h. CONCLUSIONS: This study demonstrated that ROX index could function as a novel potential marker to identify patients with a higher risk of HFNC failure. However, the prediction efficiency was moderate, and additional research is required to determine the optimal cut-off value and propel acquisition time of ROX index in the future. PROSPERO registration number: CRD42021240607.

Multi-Sensor Respiratory-Swallow Telecare System for Safe Feeding in Different Trunk Inclinations: System Development and Clinical Application.

Proper positioning is especially important to ensure feeding and eating safely. With many nursing facilities restricting visitations and close contact during the coronavirus pandemic, there is an urgent need for remote respiratory-swallow monitoring. This study aimed to develop a semiautomatic feeding telecare system that provides instant feedback and warnings on-site and remotely. It also aimed to analyze the effects of trunk positions on respiratory-swallow coordination. A signal collector with multiple integrated sensors for real-time respiratory-swallow monitoring and warning was developed. A repeated measures design was implemented to evaluate the effects of trunk inclination angles on the swallow-related functions. Significant differences in inclination angles were discovered for swallowing apnea (p = 0.045) and total excursion time of thyroid cartilage (p = 0.037), and pairwise comparisons indicated that these differences were mostly present at 5° to 45°. Alerts were triggered successfully when undesired respiratory patterns or piecemeal occurred. The results indicated that a care recipient can swallow more easily when sitting upright (5°) than when leaning backward (45°). This telecare system provides on-site and remote respiratory-swallow monitoring and alerting for residents in care facilities and can serve as a pipeline for the early screening of swallowing dysfunction.

Prediction of high-flow nasal cannula outcomes at the early phase using the modified respiratory rate oxygenation index.

BACKGROUND: This study was designed to explore the early predictive value of the respiratory rate oxygenation (ROX) index modified by PaO2 (mROX) in high-flow nasal cannula (HFNC) therapy in patients with acute hypoxemia respiratory failure (AHRF). METHOD: Seventy-five patients with AHRF treated with HFNC were retrospectively reviewed. Respiratory parameters at baseline and 2 h after HFNC initiation were analyzed. The predictive value of the ROX (ratio of pulse oximetry/FIO2 to respiratory rate) and mROX (ratio of arterial oxygen /FIO2 to respiratory rate) indices with two variations by adding heart rate to each index (ROX-HR and mROX-HR) was evaluated. RESULTS: HFNC therapy failed in 24 patients, who had significantly higher intensive care unit (ICU) mortality and longer ICU stay. Both the ROX and mROX indices at 2 h after HFNC initiation can predict the risk of intubation after HFNC. Two hours after HFNC initiation, the mROX index had a higher area under the receiver operating characteristic curve (AUROC) for predicting HFNC success than the ROX index. Besides, baseline mROX index of greater than 7.1 showed a specificity of 100% for HFNC success. CONCLUSION: The mROX index may be a suitable predictor of HFNC therapy outcomes at the early phase in patients with AHRF.

In-line miniature 3D-printed pressure-cycled ventilator maintains respiratory homeostasis in swine with induced acute pulmonary injury.

The COVID-19 pandemic demonstrated the need for inexpensive, easy-to-use, rapidly mass-produced resuscitation devices that could be quickly distributed in areas of critical need. In-line miniature ventilators based on principles of fluidics ventilate patients by automatically oscillating between forced inspiration and assisted expiration as airway pressure changes, requiring only a continuous supply of pressurized oxygen. Here, we designed three miniature ventilator models to operate in specific pressure ranges along a continuum of clinical lung injury (mild, moderate, and severe injury). Three-dimensional (3D)-printed prototype devices evaluated in a lung simulator generated airway pressures, tidal volumes, and minute ventilation within the targeted range for the state of lung disease each was designed to support. In testing in domestic swine before and after induction of pulmonary injury, the ventilators for mild and moderate injury met the design criteria when matched with the appropriate degree of lung injury. Although the ventilator for severe injury provided the specified design pressures, respiratory rate was elevated with reduced minute ventilation, a result of lung compliance below design parameters. Respiratory rate reflected how well each ventilator matched the injury state of the lungs and could guide selection of ventilator models in clinical use. This simple device could help mitigate shortages of conventional ventilators during pandemics and other disasters requiring rapid access to advanced airway management, or in transport applications for hands-free ventilation.

Breathing pattern, accessory respiratory muscles work, and gas exchange evaluation for prediction of NIV failure in moderate-to-severe COVID-19-associated ARDS after deterioration of respiratory failure outside ICU: the COVID-NIV observational study.

BACKGROUND: Data on the efficacy of non-invasive ventilation (NIV) after progression of respiratory failure in patients who have already received oxygen therapy, or CPAP outside ICU is limited. The study aimed to find predictors of NIV failure based on breathing pattern, gas exchange, and accessory respiratory muscles evaluation in patients who progressed to moderate-to-severe COVID-19 ARDS. METHODS: This was a prospective observational study in patients with moderate-to-severe COVID-19-ARDS on NIV (n = 80) admitted to COVID-ICU of Sechenov University. The combined success rate for conventional oxygen and CPAP outside ICU was 78.6% (440 of 560 patients). The primary endpoints were intubation rate and mortality. We measured respiratory rate, exhaled tidal volume (Vte), mean peak inspiratory flow (PIF), inspiratory time (Ti), PaO2, SpO2, end-tidal carbon dioxide (PETCO2), and Patrick score, and calculated ROX index, PaO2/FiO2, ventilatory ratio, and alveolar dead space (Vdalv/Vt) on Days 1, 3, 5, 7, 10, and 14. For all significant differences between NIV success and failure groups in measured data, we performed ROC analysis. RESULTS: NIV failure rate in ICU after deterioration of respiratory failure outside ICU was 71.3% (n = 57). Patients with the subsequent NIV failure were older at inclusion, more frail, had longer duration of disease before ICU admission, and higher rate of CPAP use outside ICU. ROC-analysis revealed that the following respiratory parameters after 48 h of NIV can serve as a predictors for NIV failure in moderate-to-severe COVID-19-associated ARDS: PaO2/FiO2 < 112 mmHg (AUROC 0.90 (0.93-0.97), p < 0.0001); PETCO2 < 19.5 mmHg (AUROC 0.84 (0.73-0.94), p < 0.0001); VDalv/VT > 0.43 (AUROC 0.78 (0.68-0.90), p < 0.0001); ROX-index < 5.02 (AUROC 0.89 (0.81-0.97), p < 0.0001); Patrick score > 2 points (AUROC 0.87 (0.78-0.96), p = 0.006). CONCLUSION: In patients who progressed to moderate-to-severe COVID-19-ARDS probability of NIV success rate was about 1/3. Prediction of the NIV failure can be made after 48 h based on ROX index < 5.02, PaO2/FiO2 < 112 mmHg, PETCO2 < 19.5 mmHg, and Patrick score > = 2. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04667923 , registered on 16/12/2020.

A novel hamster model of SARS-CoV-2 respiratory infection using a pseudotyped virus.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a biosafety level (BSL)-3 pathogen; therefore, its research environment is limited. Pseudotyped viruses that mimic the infection of SARS-CoV-2 have been widely used for in vitro evaluation because they are available in BSL-2 containment laboratories. However, in vivo application is inadequate. Therefore, animal models instigated with animal BSL-2 will provide opportunities for in vivo evaluation. Hamsters (6-10-week-old males) were intratracheally inoculated with luciferase-expressing vesicular stomatitis virus (VSV)-based SARS-CoV-2 pseudotyped virus. The lungs were harvested 24-72 h after inoculation and luminescence was measured using an in vivo imaging system. Lung luminescence after inoculation with the SARS-CoV-2 pseudotyped virus increased in a dose-dependent manner and peaked at 48 h. The VSV-G (envelope G) pseudotyped virus also induced luminescence; however, a 100-fold concentration was required to reach a level similar to that of the SARS-CoV-2 pseudotyped virus. The SARS-CoV-2 pseudotyped virus is applicable to SARS-CoV-2 respiratory infections in a hamster model. Because of the single-round infectious virus, the model can be used to study the steps from viral binding to entry, which will be useful for future research on SARS-CoV-2 entry without using live SARS-CoV-2 or transgenic animals.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland-Altman consistency analysis statistical method with the algorithm's respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Estimation of Respiratory Rate from Breathing Audio.

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods. Clinical Relevance- We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.