Accurate respiratory rates count: So should you!

Noninvasive Early Detection of Systemic Inflammatory Response Syndrome of COVID-19 Inpatients Using a Piezoelectric Respiratory Rates Sensor

In 2020, 20% of patients with COVID-19 developed severe complications, including life-threatening pneumonia with systemic inflammatory response syndrome (SIRS). We developed a preliminary SIRS monitor that does not require blood sampling, is noninvasive, and can collect data 24 h per day. The proposed monitor comprises a piezoelectric respiratory sensor located beneath the patient's mattress and a fingertip pulse sensor that determines ultra-high accuracy respiratory rate (mode of a 40-min frequency distribution of respiratory rates (M40FD-RR)). We assessed the clinical performance of the M40FD-RR preliminary SIRS monitor in 29 patients (12 female, 17 male, aged 15-90 years) hospitalized at Suwa Central Hospital with COVID-19, which was confirmed by a positive polymerase chain reaction test. SIRS was evaluated by logistic regression analysis using M40FD-RR, heart rate, age, and sex as explanatory variables. We compared the results of 109 examinations of 29 COVID-19 inpatients with SIRS against those determined by the proposed monitor. The proposed monitor achieved 75% sensitivity and 83% negative predictive value, making it a promising candidate for future 24 h noninvasive preliminary SIRS tests.

Physiologic changes of pregnancy and considerations for screening and diagnosis of sepsis

Obstetric sepsis is a significant cause of morbidity and mortality in pregnant people worldwide. Initial evaluation and timely intervention are crucial to improving outcomes for birthing persons and their newborns. While many of the therapies and interventions for peripartum sepsis are consistent with the general population, there are considerations unique to pregnancy. Stabilization of the septic pregnant or immediately postpartum patient requires an understanding of the physiologic changes of pregnancy, hemodynamic changes during labor, and infections specific to pregnancy. We will review the interaction between pregnant physiology and sepsis pathophysiology, and how this can guide screening and diagnosis.

Work of Breathing for Aviators: A Missing Link in Human Performance

In this study, we explore the work of breathing (WoB) experienced by aviators during the Anti-G Straining Maneuver (AGSM) to improve pilot safety and performance. Traditional airflow models of WoB fail to adequately distinguish between breathing rate and inspiratory frequency, leading to potentially inaccurate assessments. This mismatch can have serious implications, particularly in critical flight situations where understanding the true respiratory workload is essential for maintaining performance. To address these limitations, we used a non-sinusoidal model that captures the complexities of WoB under high inspiratory frequencies and varying dead space conditions. Our findings indicate that the classical airflow model tends to underestimate WoB, particularly at elevated inspiratory frequencies ranging from 0.5 to 2 Hz, where resistive forces play a significant role and elastic forces become negligible. Additionally, we show that an increase in dead space, coupled with high-frequency breathing, elevates WoB, heightening the risk of dyspnea among pilots. Interestingly, our analysis reveals that higher breathing rates lead to a decrease in total WoB, an unexpected finding suggesting that refining breathing patterns could help pilots optimize their energy expenditure. This research highlights the importance of examining the relationship between alveolar ventilation, breathing rate, and inspiratory frequency in greater depth within realistic flight scenarios. These insights indicate the need for targeted training programs and adaptive life-support systems to better equip pilots for managing respiratory challenges in high-stress situations. Ultimately, our research lays the groundwork for enhancing respiratory support for aviators, contributing to safer and more efficient flight operations.

Monitoring Respiratory Rate Continuously Without Attaching a Sensor During a Challenging Ramped Protocol

INTRODUCTION

Respiratory rate (RR) is a crucial vital sign in patient monitoring and is often the best marker of the deterioration of a sick patient. It can be used to help diagnose numerous medical conditions and has been demonstrated to be an independent predictor of patient outcomes in various critical care settings and is incorporated in many clinical early warning scores. Here, we report on the performance of depth-camera-based system for the noncontact monitoring of RR during a ramped RR protocol. The ramped breathing protocol was developed specifically to test the relatively rapid changes in rates, which include clinically important low and high ranges of RRs.

MATERIALS AND METHODS

We performed a series of experimental runs with healthy volunteers who were instructed to breathe over a wide range of RRs, where the rates were ramped up from 4 breaths/min to 50 breaths/min then back down to 4 breaths/min in a series of ramped steps. Depth information was acquired from the scene and used to determine a respiratory rate (RRdepth), and this was compared to capnograph or spirometer respiratory rate reference (RRref). A total of 9,482 contemporaneous data pairs (RRdepth, RRref) were collected during the study for comparison.

RESULTS

A Pearson correlation coefficient of 0.995 was achieved and a line of best fit given by RRdepth = 0.99 × RRref + 0.36 breaths/min. The overall root mean squared difference (RMSD) across the runs was 1.29 breaths/min with a corresponding bias of 0.16 breaths/min, respectively. The associated Bland-Altman analysis found limits of agreement of -2.45 and 2.75 breaths/min. When the data were subdivided according to low, medium, and high RRs, corresponding to ≤10, >10 to 20, and >20 breaths/min, the RMSD accuracies were found to be 0.94, 1.34, and 1.55 breaths/min, respectively.

CONCLUSIONS

The technology performed well, exhibiting an RMSD accuracy well within our target of 3 breaths/min, both across the whole range and across each individual subrange. In summary, our results indicate the potential viability of continuous noncontact monitoring for the determination of RR over a clinically relevant range.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

Video plethysmography for contactless measurement of respiratory rate in surgical patients

The accurate recording of respiratory rate (RR) without contact is important for patient care. The current methods for RR measurement such as capnography, pneumography, and plethysmography require patient contact, are cumbersome, or not accurate for widespread clinical use. Video Plethysmography (VPPG) is a novel automated technology that measures RR using a facial video without contact. The objective of our study was to determine whether VPPG can feasibly and accurately measure RR without contact in surgical patients at a clinical setting. After research ethics approval, 216 patients undergoing ambulatory surgery consented to the study. Patients had a 1.5 min video of their faces taken via an iPad preoperatively, which was analyzed using VPPG to obtain RR information. The RR prediction by VPPG was compared to 60-s manual counting of breathing by research assistants. We found that VPPG predicted RR with 88.8% accuracy and a bias of 1.40 ± 1.96 breaths per minute. A significant and high correlation (0.87) was observed between VPPG-predicted and manually recorded RR. These results did not change with the ethnicity of patients. The success rate of the VPPG technology was 99.1%. Contactless RR monitoring of surgical patients at a hospital setting using VPPG is accurate and feasible, making this technology an attractive alternative to the current approaches to RR monitoring. Future developments should focus on improving reliability of the technology.

Non-Invasive Assessment of Abdominal/Diaphragmatic and Thoracic/Intercostal Spontaneous Breathing Contributions

(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing effort. Thus, it has the potential, for example, to help differentiate between respiratory disease and dysfunctional breathing, which otherwise can present with similar symptoms such as breath rate. Current automatic methods of measuring chest expansion are invasive, intrusive, and/or difficult to conduct in conjunction with pulmonary function testing (spontaneous breathing pressure and flow measurements). (2) Methods: A tape measure and rotary encoder band system developed by the authors was used to directly measure changes in thoracic and abdominal circumferences without the calibration required for analogous strain-gauge-based or image processing solutions. (3) Results: Using scaling factors from the literature allowed for the conversion of thoracic and abdominal motion to lung volume, combining motion measurements correlated to flow-based measured tidal volume (normalised by subject weight) with R2 = 0.79 in data from 29 healthy adult subjects during panting, normal, and deep breathing at 0 cmH2O (ZEEP), 4 cmH2O, and 8 cmH2O PEEP (positive end-expiratory pressure). However, the correlation for individual subjects is substantially higher, indicating size and other physiological differences should be accounted for in scaling. The pattern of abdominal and chest expansion was captured, allowing for the analysis of muscular recruitment patterns over different breathing modes and the differentiation of active and passive modes. (4) Conclusions: The method and measuring device(s) enable the validation of patient-specific lung mechanics models and accurately elucidate diaphragmatic-driven volume changes due to intercostal/chest-wall muscular recruitment and elastic recoil.

Accurate and continuous respiratory rate using touchless monitoring technology

PURPOSE

Respiratory rate is a commonly used vital sign with various clinical applications. It serves as a crucial marker of acute health issues and any significant alteration in respiratory rate may be an early warning sign of major issues such as infections in the respiratory tract, respiratory failure, or cardiac arrest. Timely recognition of changes in respiratory rate enables prompt medical action, while neglecting to detect a change may lead to adverse patient outcomes. Here, we report on the performance of respiratory rate determined using a depth sensing camera system (RRdepth) which allows for continuous, non-contact 'touchless' monitoring of this important vital sign.

METHODS

Thirty adult volunteers undertook a range of set breathing rates to cover a target breathing range of 4-40 breaths/min. Depth information was acquired from the torso region of the subjects using an Intel D415 RealSense camera positioned above the bed. The depth information was processed to generate a respiratory signal from which RRdepth was calculated. This was compared to a manually scored capnograph reference (RRcap).

RESULTS

An overall RMSD accuracy of 0.77 breaths/min was achieved across the target respiratory rate range with a corresponding bias of 0.05 breaths/min. This corresponded to a line of best fit given by RRdepth = 1.01 x RRcap - 0.22 breaths/min with an associated high degree of correlation (R = 0.997). A breakdown of the performance with respect to sub-ranges corresponding to respiratory rates or ≤7, >7-10, >10-20, >20-30, >30 breaths/min all exhibited RMSD accuracies of less than 1.00 breaths/min. We also had the opportunity to test the performance of spontaneous breathing of the subjects which occurred during the study and found an overall RMSD accuracy of 1.20 breaths/min with corresponding accuracies ≤1.30 breaths/min across each of the individual sub-ranges.

CONCLUSIONS

We have conducted an investigative study of a prototype depth sensing camera system for the non-contact monitoring of respiratory rate. The system achieved good performance with high accuracy across a wide range of rates including both clinically important high and low rates.

Video-based sympathetic arousal assessment via peripheral blood flow estimation

Electrodermal activity (EDA) is considered a standard marker of sympathetic activity. However, traditional EDA measurement requires electrodes in steady contact with the skin. Can sympathetic arousal be measured using only an optical sensor, such as an RGB camera? This paper presents a novel approach to infer sympathetic arousal by measuring the peripheral blood flow on the face or hand optically. We contribute a self-recorded dataset of 21 participants, comprising synchronized videos of participants' faces and palms and gold-standard EDA and photoplethysmography (PPG) signals. Our results show that we can measure peripheral sympathetic responses that closely correlate with the ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers. We also show that sympathetic arousal is best inferred from the forehead, finger, or palm.

Airflow and dynamic circumference of abdomen and thorax for adults at varied continuous positive airway pressure ventilation settings and breath rates

Continuous positive airway pressure (CPAP) ventilation is a commonly prescribed respiratory therapy providing positive end-expiratory pressure (PEEP) to assist breathing and prevent airway collapse. Setting PEEP is highly debated and it is thus primarily titrated based on symptoms of excessive or insufficient support. However, titration periods are clinician intensive and can result in barotrauma or under-oxygenation during the process. Developing model-based methods to more efficiently personalise CPAP therapy based on patient-specific response requires clinical data of lung/CPAP interactions. To this end, a trial was conducted to establish a dataset of healthy subjects lung/CPAP interaction. Pressure, flow, and tidal volume were recorded alongside secondary measures of dynamic chest and abdominal circumference, to better validate model outcomes and assess breathing modes, muscular recruitment, and effort. N = 30 subjects (15 male; 15 female) were included. Self-reported asthmatics and smokers/vapers were included, offering a preliminary assessment of any potential differences in response to CPAP from lung stiffness changes in these scenarios. Additional demographics associated with lung function (sex, age, height, and weight) were also recorded.

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.

Piloting a Nurse-Led Critical Care Outreach Service to Pre-Empt Medical Emergency Team Calls and Facilitate Staff Learning

A nurse-led critical care outreach service (NLCCOS) can support staff education and decision making in the wards, managing at-risk patients with ward nurses to avoid further deterioration. We aimed to investigate the characteristics of patients identified as at-risk, the types of treatments they required to prevent deterioration, the education initiated by the NLCCOS, and the perceived experiences of ward nurses. This prospective observational pilot study using mixed methods took place in one medical and one surgical ward at a university hospital in Denmark. Participants were patients nominated as at-risk by head nurses in each ward, the ward nurses, and nurses from the NLCCOS. In total, 100 patients were reviewed, 51 medical and 49 surgical patients, over a six-month period. Most patients (70%) visited by the NLCCOS had a compromised respiratory status, and ward nurses received teaching and advice regarding interventions. Sixty-one surveys were collected from ward nurses on their learning experience. Over 90% (n = 55) of nurses believed they had learned from, and were more confident with, managing patients following the experience. The main educational areas were respiratory therapy, invasive procedures, medications, and benefits of mobilization. Further research needs to measure the impact of the intervention on patient outcomes and MET call frequency over time in larger samples.

Assessment of Virtual Mindfulness-Based Training for Health Care Professionals: Improved Self-Reported Respiration Rates, Perceived Stress, and Resilience

Background

Mindfulness in Motion (MIM) is a workplace resilience-building intervention that has shown reductions in perceived stress and burnout, as well as increased resilience and work engagement in health care workers.

Objective

To evaluate effects of MIM delivered in a synchronous virtual format on self-reported respiratory rates (RR), as well as perceived stress and resiliency of health care workers.

Methods

Breath counts were self-reported by 275 participants before and after 8 weekly MIM sessions. MIM was delivered virtually in a group format as a structured, evidence-based workplace intervention including a variety of mindfulness, relaxation, and resilience-building techniques. Participants counted their breaths for 30 seconds, which was then multiplied by 2 to report RR. Additionally, participants completed Perceived Stress Scale and Connor-Davidson Resiliency Scale.

Results

According to mixed effect analyses there were main effects of MIM Session (P < .001) and Weeks (P < .001), but no Session by Week interaction (P = .489) on RR. On average, RR prior to MIM sessions were reduced from 13.24 bpm (95% CI = 12.94, 13.55 bpm) to 9.69 bpm (95% CI = 9.39, 9.99 bpm). When comparing average Pre-MIM and Post-MIM RR throughout the MIM intervention, Week-2 (mean = 12.34; 95% CI = 11.89, 12.79 bpm) was not significantly different than Week-1 (mean = 12.78; 95% CI = 12.34, 13.23 bpm), but Week-3 through Week-8 demonstrated significantly lower average Pre-MIM and Post-MIM RR compared to Week-1 (average weekly difference range: 1.36 to 2.48 bpm, P < .05). Perceived stress was reduced from Week-1 (17.52 ± 6.25) to after Week-8 (13.52 ± 6.04; P < .001), while perceived resiliency was increased from Week-1 (11.30 ± 5.14) to after Week-8 (19.29 ± 2.58); P < .001).

Conclusion

Thus far, completion of MIM sessions has shown acute and long-term effects on self-reported RR, but more research is required to determine the extent of improved parasympathetic (relaxed) states. Collectively, this work has shown value for mind-body stress mitigation and resiliency-building in high stress acute health care environments.

Agreement between standard and continuous wireless vital sign measurements after major abdominal surgery: a clinical comparison study

Objective. Continuous wireless monitoring outside the post-anesthesia or intensive care units may enable early detection of patient deterioration, but good accuracy of measurements is required. We aimed to assess the agreement between vital signs recorded by standard and novel wireless devices in postoperative patients.Approach. In 20 patients admitted to the post-anesthesia care unit, we compared heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO₂), and systolic and diastolic blood pressure (SBP and DBP) as paired data. The primary outcome measure was the agreement between standard wired and wireless monitoring, assessed by mean bias and 95% limits of agreement (LoA). LoA was considered acceptable for HR and PR, if within ±5 beats min^-1(bpm), while RR, SpO2, and BP were deemed acceptable if within ±3 breaths min^-1(brpm), ±3%-points, and ±10 mmHg, respectively.Main results.The mean bias between standard versus wireless monitoring was -0.85 bpm (LoA -6.2 to 4.5 bpm) for HR, -1.3 mmHg (LoA -19 to 17 mmHg) for standard versus wireless SBP, 2.9 mmHg (LoA -17 to 22) for standard versus wireless DBP, and 1.7% (LoA -1.4 mmHg to 4.8 mmHg) for SpO₂, comparing standard versus wireless monitoring. The mean bias of arterial blood gas analysis versus wireless SpO₂measurements was 0.02% (LoA -0.02% to 0.06%), while the mean bias of direct observation of RR compared to wireless measurements was 0.0 brpm (LoA -2.6 brpm to 2.6 brpm). 80% of all values compared were within predefined clinical limits.Significance.The agreement between wired and wireless HR, RR, and PR recordings in postoperative patients was acceptable, whereas the agreement for SpO₂recordings (standard versus wireless) was borderline. Standard wired and wireless BP measurements may be used interchangeably in the clinical setting.

Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations

Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.

How registered nurses are measuring respiratory rates in adult acute care health settings: An integrative review

AIMS AND OBJECTIVES

This integrative review aimed to draw conclusions from evidence on how registered nurses are measuring respiratory rates for acute care patients.

BACKGROUND

Despite the growing research supporting respiratory rate as an early indicator for clinical deterioration, respiratory rate has consistently been the least frequently measured and accurately documented vital sign.

DESIGN

An integrative review.

METHODS

A systematic literature search was conducted in June 2022 in four databases: CINAHL, PubMed, Medline and Scopus. Quality appraisal was undertaken using the Joanna Briggs Institute's Checklist. PRISMA guidelines were followed to ensure explicit reporting and reported in the PRISMA checklist.

RESULTS

Overall, 9915 records were identified, and 19 met the inclusion criteria. Of these 19 articles, seven themes emerged: estimation and digit preference, lack of understanding and knowledge, not valuing the clinical significance of respiratory rate, oxygen saturation substitute, interobserver agreement, subjective concern and count duration. A high prevalence of bias, estimation and incorrect technique was evident. A total of 15 articles reported specifically on how registered nurses are measuring respiratory rates on general medical and surgical wards.

CONCLUSIONS

Despite its importance, this integrative review has determined that respiratory rates are not being assessed correctly by nursing staff in the acute care environment. Evidence of using estimation, value bias or quick count and multiply techniques are emerging themes which urgently require further research. No patient or public contribution.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform

Background

Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it.

Methods

In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation.

Results

By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved.

Conclusion

The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Characterization and Toxicity Analysis of Lab-Created Respirable Coal Mine Dust from the Appalachians and Rocky Mountains Regions

Coal mine workers are continuously exposed to respirable coal mine dust (RCMD) in workplaces, causing severe lung diseases. RCMD characteristics and their relations with dust toxicity need further research to understand the adverse exposure effects to RCMD. The geographic clustering of coal workers’ pneumoconiosis (CWP) suggests that RCMD in the Appalachian region may exhibit more toxicity than other geographic regions such as the Rocky Mountains. This study investigates the RCMD characteristics and toxicity based on geographic location. Dissolution experiments in simulated lung fluids (SLFs) and in vitro responses were conducted to determine the toxicity level of samples collected from five mines in the Rocky Mountains and Appalachian regions. Dust characteristics were investigated using Fourier-transform infrared spectroscopy, scanning electron microscopy, the BET method, total microwave digestion, X-ray diffraction, and X-ray photoelectron spectroscopy. Inductively coupled plasma mass spectrometry was conducted to determine the concentration of metals dissolved in the SLFs. Finer particle sizes and higher mineral and elemental contents were found in samples from the Appalachian regions. Si, Al, Fe, Cu, Sr, and Pb were found in dissolution experiments, but no trends were found indicating higher dissolutions in the Appalachian region. In vitro studies indicated a proinflammatory response in epithelial and macrophage cells, suggesting their possible participation in pneumoconiosis and lung diseases development.

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.

CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnograph signal has received a lot of attention in recent years as a valuable indicator of respiratory conditions. However, the typical capnograph signal extraction method is quite expensive and also unpleasant to the patient due to the involvement of a nasal cannula. With the advent of wearable sensor technology, there has been significant research on the use of photoplethysmogram (PPG) signals as a less expensive alternative to extract respiratory information. In this paper, we propose CapNet, a novel deep learning-based framework which takes the regular PPG signal as input, and estimates the capnograph signal as output. Training, validation and testing of the proposed networks in CapNet is done using the IEEE TMBE Respiratory Rate Benchmark dataset by utilizing reference capnograph respiration signals. With a lower MSE and higher cross-correlation values, CapNet outperforms two traditional signal processing algorithms and another recently proposed deep neural network, RespNet. The proposed framework expectantly can be implementable and feasible for constant supervising of patients undergoing respiratory ailments.

Multimode optical fiber specklegram smart bed sensor array

SIGNIFICANCE

Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes. We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially.

AIM

Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed.

APPROACH

Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3  ×  4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3  ×  4 image is formed by comparing the signal at each crossing point between two fibers.

RESULTS

The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing.

CONCLUSIONS

The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.

ROX index and SpO2/FiO2 ratio for predicting high-flow nasal cannula failure in hypoxemic COVID-19 patients: A multicenter retrospective study

BACKGROUND

The ratio of oxygen saturation (ROX) index, defined as the ratio of oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) to respiratory rate, can help identify patients with hypoxemic respiratory failure at high risk for intubation following high-flow nasal cannula (HFNC) initiation; however, whether it is effective for predicting intubation in coronavirus disease 2019 (COVID-19) patients receiving HFNC remains unknown. Moreover, the SpO2/FiO2 ratio has been assessed as a prognostic marker for acute hypoxemic respiratory failure. This study aimed to determine the utility of the ROX index and the SpO2/FiO2 ratio as predictors of failure in COVID-19 patients who received HFNC.

METHODS

This multicenter study was conducted in seven university-affiliated hospitals in Korea. Data of consecutive hospitalized patients diagnosed with COVID-19 between February 10, 2020 and February 28, 2021 were retrospectively reviewed. We calculated the ROX index and the SpO2/FiO2 ratio at 1 h, 4 h, and 12 h after HFNC initiation. The primary outcome was HFNC failure defined as the need for subsequent intubation despite HFNC application. The receiver operating characteristic curve analysis was used to evaluate discrimination of prediction models for HFNC failure.

RESULTS

Of 1,565 hospitalized COVID-19 patients, 133 who received HFNC were analyzed. Among them, 63 patients (47.4%) were successfully weaned from HFNC, and 70 (52.6%) were intubated. Among patients with HFNC failure, 32 (45.7%) died. The SpO2/FiO2 ratio at 1 h after HFNC initiation was an important predictor of HFNC failure (AUC 0.762 [0.679-0.846]). The AUCs of SpO2/FiO2 ratio at 4 h and ROX indices at 1 h and 4 h were 0.733 (0.640-0.826), 0.697 (0.597-0.798), and 0.682 (0.583-0.781), respectively. Multivariable analysis showed that the patients aged ≥70 years are 3.4 times more likely to experience HFNC failure than those aged <70 years (HR 3.367 [1.358-8.349], p = 0.009). The SpO2/FiO2 ratio (HR 0.983 [0.972-0.994], p = 0.003) at 1 h was significantly associated with HFNC failure.

CONCLUSIONS

The SpO2/FiO2 ratio following HFNC initiation was an acceptable predictor of HFNC failure. The SpO2/FiO2 ratio may be a good prognostic marker for predicting intubation in COVID-9 patients receiving HFNC.

Factors associated with the frequency of respiratory rate measurement by hospital nurses: a multicentre cross-sectional study

BACKGROUND

Although the respiratory rate (RR) is a sensitive predictor of patient deterioration, it is often neglected. Moreover, only a few studies have investigated the factors that cause health professionals to disregard RR.

AIMS

This cross-sectional study aimed to elucidate the factors affecting the frequency of RR measurement by the nurses.

METHODS

An original questionnaire, comprising 18 factors extracted from previous studies, was administered to nurses from nine hospitals.

FINDINGS

Of the 644 eligible nurses, 592 (92%) completed the questionnaire. The adjusted odds ratios and 95% confidence intervals of the factors of importance, educational experiences, shortened-count method use, negative experiences, and inconvenience were 2.24 (1.13-4.45), 2.26 (1.20-4.26), 0.61 (0.42-0.91), 0.45 (0.29-0.70), and 0.41 (0.26-0.65), respectively.

CONCLUSION

Education, feedback systems, and automation are the primary issues that need attention. Prioritising these factors could provide a practical guide for optimising the frequency of RR measurement.

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

Patient Deterioration on General Care Units: A Concept Analysis

Patient deterioration is a phenomenon that occurs from the inability to recognize it or respond to a change in condition. Despite the published reports on recognizing a deteriorating patient on general care floors, a gap remains in the ability of nurses to describe the concept, affecting patient outcomes. Walker and Avant's approach was applied to analyze patient deterioration. The aim of this article was to explore and clarify the meaning of patient deterioration and identify attributes, antecedents, and consequences. The defining attributes were compared to early warning scores. An operational definition was developed and its value to nurses established.

The use of early warning system scores in prehospital and emergency department settings to predict clinical deterioration: A systematic review and meta-analysis

Background

It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration of patients when applied in the Emergency Department (ED) or prehospital setting.

Methods

This systematic review (SR) and meta-analysis assessed the predictive abilities of five commonly used EWS scores (National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS), and Cardiac Arrest Risk Triage (CART)). Outcomes of interest included admission to intensive care unit (ICU), and 3-to-30-day mortality following hospital admission. Using DerSimonian and Laird random-effects models, pooled estimates were calculated according to the EWS score cut-off points, outcomes, and study setting. Risk of bias was evaluated using the Newcastle-Ottawa scale. Meta-regressions investigated between-study heterogeneity. Funnel plots tested for publication bias. The SR is registered in PROSPERO (CRD42020191254).

Results

Overall, 11,565 articles were identified, of which 20 were included. In the ED setting, MEWS, and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (95% Confidence Interval (CI) 2.35–6.99) to 6.48 (95% CI 1.83–22.89), p = 0.757. MEWS at a cut-off point ≥3 had a similar DOR when predicting ICU admission (5.54 (95% CI 2.02–15.21)). MEWS ≥5 and NEWS ≥7 had DORs of 3.05 (95% CI 2.00–4.65) and 4.74 (95% CI 4.08–5.50), respectively, when predicting 30-day mortality in patients presenting with sepsis in the ED. In the prehospital setting, the EWS scores significantly predicted 3-day mortality but failed to predict 30-day mortality.

Conclusion

EWS scores’ predictability of clinical deterioration is improved when the score is applied to patients treated in the hospital setting. However, the high thresholds used and the failure of the scores to predict 30-day mortality make them less suited for use in the prehospital setting.

High efficacy of layered controls for reducing exposure to airborne pathogens

To optimize strategies for curbing the transmission of airborne pathogens, the efficacy of three key controls-face masks, ventilation, and physical distancing-must be well understood. In this study, we used the Quadrature-based model of Respiratory Aerosol and Droplets to quantify the reduction in exposure to airborne pathogens from various combinations of controls. For each combination of controls, we simulated thousands of scenarios that represent the tremendous variability in factors governing airborne transmission and the efficacy of mitigation strategies. While the efficacy of any individual control was highly variable among scenarios, combining universal mask-wearing with distancing of 1 m or more reduced the median exposure by more than 99% relative to a close, unmasked conversation, with further reductions if ventilation is also enhanced. The large reductions in exposure to airborne pathogens translated to large reductions in the risk of initial infection in a new host. These findings suggest that layering controls is highly effective for reducing transmission of airborne pathogens and will be critical for curbing outbreaks of novel viruses in the future.

Modifying naloxone to reverse fentanyl-induced overdose

Developing an effective antidote for fentanyl-induced overdose (OD) is an unmet medical need that requires both lipophilicity comparable to fentanyl and fast onset of overdose reversal. We synthesized and evaluated a bioreversible derivative of naloxone (NX-90) in silico, in vitro and in vivo to yield a robust reversal of fentanyl-induced OD in rats. All monitored reflexes along with the heart rate (HR) and respiratory rate (RR) were fully restored faster in the NX-90 groups than in naloxone groups on equimolar bases when given intranasally. In NX-90 treated rats RR over the time of observation (RR AUC) was significantly higher at all respective doses with no re-narcotization observed. Apart from the enhanced pharmacodynamics profile, NX-90 was found to have lower circulating levels of naloxone, clean profile in in vitro selectivity panels, as well as Ames and CYP450 counter screens. Finally, we demonstrated a robust release of the parent naloxone in brain matrix, as well as lower peripheral naloxone levels after NX-90 iv administration. With the demonstrated pharmacological profile superior yet congruent to naloxone we nominated NX-90 for preclinical development as an effective intranasal fentanyl antidote.

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

Informing future nursing: An exploration of respiratory teaching in the pre-registration nurse curriculum

AIM/OBJECTIVE

The aim is to examine and map the respiratory skills taught in the pre-registration nursing curriculum (2010).

BACKGROUND

Respiratory assessment and care are fundamental clinical skills enabling nurses to treat and care for people with acute and chronic respiratory diseases. The incidence of respiratory disease is rising, globally and most nurses will care for respiratory patients during their career. The extent of pre-registration respiratory specific education delivered in UK NMC (Nursing and Midwifery Council) approved education institutions (AEIs) is currently unknown. The move to the 2018 revised NMC standards for pre-registration nursing offers AEIs the opportunity to review provision of respiratory education. This study describes respiratory education delivered to pre-registration nurses in UK AEIs prior to implementation of the new NMC standards. Curriculum re-design can be adapted for the global nursing community.

DESIGN

This is a freedom of information survey; to gather, examine and map curriculum content.

METHODS

A survey of UK AEIs was conducted to initially scope provision of respiratory education for pre-registration nursing programmes. AEIs were emailed a freedom of information (FOI) request and provided information about the curriculum between April-June 2019.

RESULTS

Seventy-five UK AEIs providing pre-registration nursing programmes responded. Over half of AEIs dedicated over 4 h of teaching respiratory anatomy and physiology (60.8%), respiratory pathophysiology (75.3%) and long- term respiratory conditions (60.3%). Less than half (44.4%) spent over 4 h teaching respiratory health and prevention of respiratory disease. Just over a third spent over 4 h on respiratory pharmacology (33.8%), local and national respiratory guidelines (33.3%) and information on pulmonary rehabilitation and other interventions for the management of respiratory conditions (35.2%). In most AEIs, skills laboratories were used to teach respiratory skills. Student competence was not always assessed. Respiratory learning was reported to take place during practice placements, but this was variable.

CONCLUSIONS

Variation exists in provision of respiratory education in pre-registration nursing programmes across the UK. Whilst some respiratory topics appear to be covered adequately, others have limited time on knowledge and skills teaching. New standards and curricula offer AEIs the opportunity to enhance this provision. Adaptations can be made and the curriculum transferred to the global nursing workforce.

TWEETABLE ABSTRACT

Gaps have been identified in respiratory teaching pre-registration nurse education. Curriculum redesign to focus on respiratory care.

Validity of a novel respiratory rate monitor comprising stretchable strain sensors during a 6-min walking test in patients with chronic pulmonary obstructive disease

BACKGROUND

Breathing frequency is rarely measured during a field walking test since the current monitoring system using a face mask is cumbersome for older adults. For effective clinical application, we aimed to validate the new respiratory monitor using wearable strain sensors during a 6-min walk test (6MWT) in young adults and patients with chronic obstructive pulmonary disease (COPD).

METHODS

The study included young adults and patients with stable COPD voluntarily recruited from three hospitals. Breathing frequency during 6MWT were measured by the strain sensor and a nasal capnometer. Total breathing frequencies were measured by the capnometer. The Bland-Altman method was used to estimate the mean limit of agreement for breathing frequency.

RESULTS

A total of 23 young adults (age = 23.1 ± 3.7, mean ± SD) and 50 patients with COPD (age = 75.2 ± 7.2, %FEV1 = 59.1 ± 19.7) were analyzed. During the entire test period, the total breathing frequencies were measured based on an average of 252 ± 46 breaths, and the total breathing frequency was higher in patients with COPD than in young adults (mean difference = -3.349, p < 0.0013). The mean difference in breathing frequency between the strain sensors and capnometer was -0.28 (95%CI: 0.75 to 0.20), and the limit of agreement ranged from -4.1 to 3.6. The CI of the limit of agreement included the limit of equivalence (4 counts/min).

CONCLUSIONS

The novel respiratory monitor with wearable sensors achieved the target accuracy in both young adults and patients with COPD in the 6MWT.

Contactless Heart Rate Variability (HRV) Estimation Using a Smartphone During Respiratory Maneuvers and Body Movement

Heart rate variability (HRV) has been extensively investigated as a noninvasive marker to evaluate the functionality of the autonomic nervous system (ANS). Many studies have provided photoplethysmography (PPG) as a surrogate for electrocardiogram (ECG) signal HRV measurements. Remote PPG (rPPG) has been also investigated for pulse rate variability (PRV) estimation but in controlled conditions. We remotely extracted PRV using a smartphone camera for subjects in static and lateral motion while their respiratory rate was set to three breathing rates in an indoor illumination environment. PRV was compared with ECG-based HRV as a gold standard. We tested our algorithms on five healthy subjects. The results showed high correlation for rPPG-based HRV by presenting means of standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive heartbeat interval differences (RMSSD) correlation coefficient greater than 0.95 in rest and greater than 0.87 in motion. The error of mean low frequency over high frequency (LF/HF) ratio estimated from PRV was 0.13 in rest and 0.25 in lateral motion. Moreover, a statistically significant correlation was obtained between HRV and PRV power spectra and temporal signals for all performed tasks. The obtained results contributed to confirm that remote imaging measurement of cardiac parameters is a promising, convenient, and low-cost alternative to specialized biomedical sensors in a diversity of relevant experimental maneuver.

Simulating near-field enhancement in transmission of airborne viruses with a quadrature-based model

Some infectious diseases, such as influenza, tuberculosis, and SARS-CoV-2, may be transmitted when virus-laden particles expelled from an infectious person are inhaled by someone else, which is known as the airborne transmission route. These virus-laden particles are more concentrated in the expiratory jet of an infectious person than elsewhere in a well-mixed room, but this near-field enhancement in virion exposure has not been well quantified. Transmission of airborne viruses depends on factors that are inherently variable and, in many cases, poorly constrained, and quantifying this uncertainty requires large ensembles of model simulations that span the variability in input parameters. However, models that are well-suited to simulate the near-field evolution of respiratory particles are also computationally expensive, which limits the exploration of parametric uncertainty. In order to perform many simulations that span the wide variability in factors governing airborne transmission, we developed the Quadrature-based model of Respiratory Aerosol and Droplets (QuaRAD). QuaRAD is an efficient framework for simulating the evolution of virus-laden particles after they are expelled from an infectious person, their deposition to the nasal cavity of a susceptible person, and the subsequent risk of initial infection. We simulated 10 000 scenarios to quantify the risk of initial infection by a particular virus, SARS-CoV-2. The predicted risk of infection was highly variable among scenarios and, in each scenario, was strongly enhanced near the infectious individual. In more than 50% of scenarios, the physical distancing needed to avoid near-field enhancements in airborne transmission was beyond the recommended safe distance of two meters (six feet) if the infectious person is not wearing a mask, though this distance defining the near-field extent was also highly variable among scenarios; the variability in the near-field extent is explained predominantly by variability in expiration velocity. Our findings suggest that maintaining at least two meters of distance from an infectious person greatly reduces exposure to airborne virions; protections against airborne transmission, such as N95 respirators, should be available when distancing is not possible.

Basic Airway Management for the Professional Nurse

The registered nurse (RN) on a medical-surgical nursing unit may be the first health care professional to encounter a patient with the signs of impending respiratory failure. Importantly, the RN must recognize the signs of respiratory compromise and possess the competence and confidence to intervene without delay. Signs of respiratory deterioration, physical assessment, and respiratory laboratory studies are reviewed. Modes of oxygen therapy, basic airway management techniques, including bag mask ventilation, and use of oropharyngeal and nasopharyngeal airways are discussed. The assembly of equipment and medications frequently used for intubation are also outlined.

Magnetic resonance imaging with gradient sound respiration guide

Respiratory motion management is crucial for high-resolution MRI of the heart, lung, liver and kidney. In this article, respiration guide using acoustic sound generated by pulsed gradient waveforms was introduced in the pulmonary ultrashort echo time (UTE) sequence and validated by comparing with retrospective respiratory gating techniques. The validated sound-guided respiration was implemented in non-contrast enhanced renal angiography. In the sound-guided respiration, breathe−in and–out instruction sounds were generated with sinusoidal gradient waveforms with two different frequencies (602 and 321 Hz). Performance of the sound-guided respiration was evaluated by measuring sharpness of the lung-liver interface with a 10–90% rise distance, w_10-90, and compared with three respiratory motion managements in a free-breathing UTE scan: without respiratory gating (w/o gating), 0-dimensional k-space navigator (k-point navigator), and image-based self-gating (Img-SG). The sound-guided respiration was implemented in stack-of-stars balanced steady-state free precession with inversion recovery preparation for renal angiography. No subjects reported any discomfort or inconvenience with the sound-guided respiration in pulmonary or renal MRI scans. The lung-liver interface of the UTE images for sound-guided respiration (w_10-90 = 6.99 ± 2.90 mm), k-point navigator (8.51 ± 2.71 mm), and Img-SG (7.01 ± 2.06 mm) was significantly sharper than that for w/o gating (17.13 ± 2.91 mm; p < 0.0001 for all of sound-guided respiration, k-point navigator and Img-SG). Sharpness of the lung-liver interface was comparable between sound-guided respiration and Img-SG (p = 0.99), but sound-guided respiration achieved better visualization of pulmonary vasculature. Renal angiography with the sound-guided respiration clearly delineated renal, segmental and interlobar arteries. In conclusion, the gradient sound guided respiration can facilitate a consistent diaphragm position in every breath and achieve performance of respiratory motion management comparable to image-based self-gating.

An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive Wearable RFID Smart Garment

Currently, wired respiratory rate sensors tether patients to a location and can potentially obscure their body from medical staff. In addition, current wired respiratory rate sensors are either inaccurate or invasive. Spurred by these deficiencies, we have developed the Bellyband, a less invasive smart garment sensor, which uses wireless, passive Radio Frequency Identification (RFID) to detect bio-signals. Though the Bellyband solves many physical problems, it creates a signal processing challenge, due to its noisy, quantized signal. Here, we present an algorithm by which to estimate respiratory rate from the Bellyband. The algorithm uses an adaptively parameterized Savitzky-Golay (SG) filter to smooth the signal. The adaptive parameterization enables the algorithm to be effective on a wide range of respiratory frequencies, even when the frequencies change sharply. Further, the algorithm is three times faster and three times more accurate than the current Bellyband respiratory rate detection algorithm and is able to run in real time. Using an off-the-shelf respiratory monitor and metronome-synchronized breathing, we gathered 25 sets of data and tested the algorithm against these trials. The algorithm's respiratory rate estimates diverged from ground truth by an average Root Mean Square Error (RMSE) of 4.1 breaths per minute (BPM) over all 25 trials. Further, preliminary results suggest that the algorithm could be made as or more accurate than widely used algorithms that detect the respiratory rate of non-ventilated patients using data from an Electrocardiogram (ECG) or Impedance Plethysmography (IP).

Initial assessment in emergency departments by chief complaint and respiratory rate

BACKGROUND

Understanding heterogeneity of the respiratory rate (RR) as a risk stratification marker across chief complaints is important to reduce misinterpretation of the risk posed by outcome events and to build accurate risk stratification tools. This study was conducted to investigate the associations between RR and clinical outcomes according to the five most frequent chief complaints in an emergency department (ED): fever, shortness of breath, altered mental status, chest pain, and abdominal pain.

METHODS

This retrospective cohort study examined ED data of all adult patients who visited the ED of a tertiary medical center during April 2018-September 2019. The primary exposure was RR at the ED visit. Outcome measures were hospitalization and mechanical ventilation use. We used restrictive cubic spline and logistic regression models to assess the association of interest.

RESULTS

Of 16 956 eligible ED patients, 4926 (29%) required hospitalization; 448 (3%) required mechanical ventilation. Overall, U-shaped associations were found between RR and the risk of hospitalization (eg, using RR = 16 as the reference, the odds ratio [OR] of RR = 32, 6.57 [95% CI 5.87-7.37]) and between RR and the risk of mechanical ventilation. This U-shaped association was driven by patients' association with altered mental status (eg, OR of RR = 12, 2.63 [95% CI 1.25-5.53]). For patients who have fever or shortness of breath, the risk of hospitalization increased monotonously with increased RR.

CONCLUSIONS

U-shaped associations of RR with the risk of overall clinical outcomes were found. These associations varied across chief complaints.

Preclinical MRI to Quantify Pulmonary Disease Severity and Trajectories in Poorly Characterized Mouse Models: A Pedagogical Example Using Data from Novel Transgenic Models of Lung Fibrosis

Structural remodeling in lung disease is progressive and heterogeneous, making temporally and spatially explicit information necessary to understand disease initiation and progression. While mouse models are essential to elucidate mechanistic pathways underlying disease, the experimental tools commonly available to quantify lung disease burden are typically invasive (e.g., histology). This necessitates large cross-sectional studies with terminal endpoints, which increases experimental complexity and expense. Alternatively, magnetic resonance imaging (MRI) provides information noninvasively, thus permitting robust, repeated-measures statistics. Although lung MRI is challenging due to low tissue density and rapid apparent transverse relaxation (T₂* <1 ms), various imaging methods have been proposed to quantify disease burden. However, there are no widely accepted strategies for preclinical lung MRI. As such, it can be difficult for researchers who lack lung imaging expertise to design experimental protocols-particularly for novel mouse models. Here, we build upon prior work from several research groups to describe a widely applicable acquisition and analysis pipeline that can be implemented without prior preclinical pulmonary MRI experience. Our approach utilizes 3D radial ultrashort echo time (UTE) MRI with retrospective gating and lung segmentation is facilitated with a deep-learning algorithm. This pipeline was deployed to assess disease dynamics over 255 days in novel, transgenic mouse models of lung fibrosis based on disease-associated, loss-of-function mutations in Surfactant Protein-C. Previously identified imaging biomarkers (tidal volume, signal coefficient of variation, etc.) were calculated semi-automatically from these data, with an objectively-defined high signal volume identified as the most robust metric. Beyond quantifying disease dynamics, we discuss common pitfalls encountered in preclinical lung MRI and present systematic approaches to identify and mitigate these challenges. While the experimental results and specific pedagogical examples are confined to lung fibrosis, the tools and approaches presented should be broadly useful to quantify structural lung disease in a wide range of mouse models.

Monitoring Symptoms of Infectious Diseases: Perspectives for Printed Wearable Sensors

Infectious diseases possess a serious threat to the world's population, economies, and healthcare systems. In this review, we cover the infectious diseases that are most likely to cause a pandemic according to the WHO (World Health Organization). The list includes COVID-19, Crimean-Congo Hemorrhagic Fever (CCHF), Ebola Virus Disease (EBOV), Marburg Virus Disease (MARV), Lassa Hemorrhagic Fever (LHF), Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), Nipah Virus diseases (NiV), and Rift Valley fever (RVF). This review also investigates research trends in infectious diseases by analyzing published research history on each disease from 2000-2020 in PubMed. A comprehensive review of sensor printing methods including flexographic printing, gravure printing, inkjet printing, and screen printing is conducted to provide guidelines for the best method depending on the printing scale, resolution, design modification ability, and other requirements. Printed sensors for respiratory rate, heart rate, oxygen saturation, body temperature, and blood pressure are reviewed for the possibility of being used for disease symptom monitoring. Printed wearable sensors are of great potential for continuous monitoring of vital signs in patients and the quarantined as tools for epidemiological screening.

Design and construction of a simplified, gas-driven, pressure-controlled emergency ventilator

INTRODUCTION

Due to the COVID-19 crisis or any other mass casualty situation it might be necessary to give artificial ventilation to many affected patients. Contrarily, the worldwide availability of emergency ventilators is still a shortage, especially in developing countries.

METHODS

Modes of artificial ventilation were compared and the most safe, easy to use, and lung protecting principle was optimized to fit all requirements of both emergency ventilation and cost-effective mass production.

RESULTS

The presented research results describe a simplified device for a pressure-controlled ventilation which works without electricity according to a known principle. Just pressurized gas and a patient connection is required. The device enables the control of basic ventilator parameters such as peak inspiratory pressure, positive end-expiratory pressure and the ventilation frequency. Further, the device is semiadaptive to the patient's lung stiffness and automatically maintains minute volume through frequency adjustment. The machine can be manufactured by turning, milling and drilling and needs purchased components with costs less than 100 USD. A sterilization and thus a reuse is possible.

DISCUSSION

The presented development does not describe a ready-to-purchase ventilator, it rather outlines a refined working principle for emergency ventilation and its easiest methods of production with a minimum of requirements. The presented research aims on providing an open-source guideline for production of an emergency ventilator using worldwide available methods and thus should inspire local researchers to do a reverse engineering and eventually to put it into operation following country-specific regulations. For long-term ventilation exceeding emergency purposes, a monitoring of alarms for disconnection and violation of desired ventilator parameters should be established. The ventilator is limited to a fixed ratio between PIP and PEEP. Moreover, the ventilation frequency depends on two parameters, which needs some training. Nevertheless, the ventilator provides basic features to enable an emergency ventilation with minimal prerequisites.

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease that causes long-term breathing problems. The reliable monitoring of respiratory rate (RR) is very important for the treatment and management of COPD. Based on inkjet printing technology, we have developed a stretchable and wearable sensor that can accurately measure RR on normal subjects. Currently, there is a lack of comprehensive evaluation of stretchable sensors in the monitoring of RR on COPD patients. We aimed to investigate the measurement accuracy of our sensor on COPD patients. Methodology: Thirty-five patients (Mean ± SD of age: 55.25 ± 13.76 years) in different stages of COPD were recruited. The measurement accuracy of our inkjet-printed (IJPT) sensor was evaluated at different body postures (i.e., standing, sitting at 90°, and lying at 45°) on COPD patients. The RR recorded by the IJPT sensor was compared with that recorded by the reference e-Health sensor using paired T-test and Wilcoxon signed-rank test. Analysis of variation (ANOVA) was performed to investigate if there was any significant effect of individual difference or posture on the measurement error. Statistical significance was defined as p-value less than 0.05. Results: There was no significant difference between the RR measurements collected by the IJPT sensor and the e-Health reference sensor overall and in three postures (p > 0.05 in paired T-tests and Wilcoxon signed-rank tests). The sitting posture had the least measurement error of −0.0542 ± 1.451 bpm. There was no significant effect of posture or individual difference on the measurement error or relative measurement error (p > 0.05 in ANOVA). Conclusion: The IJPT sensor can accurately measure the RR of COPD patients at different body postures, which provides the possibility for reliable monitoring of RR on COPD patients.

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.

Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

Nursing Students' Knowledge, Competence and Attitudes towards Vital Signs Monitoring during Clinical Practice

AIM AND OBJECTIVE

This study assessed the perceived knowledge and competence, and the attitude of Saudi nursing students towards vital signs monitoring for detecting patient deterioration during clinical rotation. It also examined the predictors of students' attitudes.

BACKGROUND

One of the most important uses of vital signs monitoring is the early detection of deterioration. Vital signs monitoring is one of the most frequently assigned tasks to students during clinical rotation. However, the attitudes of nursing students towards vital signs monitoring for detecting clinical deterioration remain unexplored.

DESIGN

Quantitative, cross-sectional design.

METHOD

A convenience sample of 529 baccalaureate nursing students in two universities in Saudi Arabia was surveyed using the V-scale from October 2019-December 2019. A multivariate multiple regression was implemented to examine the multivariate effect of the predictor variables on the five subscales of the V-scale. This study adhered to the STROBE checklist.

RESULTS

The overall attitudes of the students towards VS monitoring can be interpreted as poor to modest. The highest mean was reported in the subscale 'communication'. The subscales 'workload', 'key indicators' and 'technology' received low mean scores. The university, age, gender, academic year level and perceived knowledge had significant multivariate effects on the five subscales of the V-scale.

CONCLUSIONS

The Saudi nursing students had poor attitudes towards vital signs monitoring, specifically towards the use of technology in vital signs monitoring, the workload associated with vital signs monitoring and vital signs as key indicators of patient deterioration.

RELEVANCE TO CLINICAL PRACTICE

The findings reveal the need to improve the curricular content and training of nursing students regarding vital signs and the physiological indicators of clinical deterioration. This study also identified areas that require improvement to ensure positive attitudes among students.

Associated Factors With Acute Transfusion Reaction From Hospital Online Reporting Events: A Retrospective Cohort Study

In our hospital’s hemovigilance system, a Wi-Fi–based vital signs monitor that automatically transmits data to ensure patient safety has been implemented. We derived the potential clinical characteristics for subsequent association of acute transfusion reactions (ATRs) using the hospital information system database.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.