A Brief Review of Non-invasive Monitoring of Respiratory Condition for Extubated Patients with or at Risk for Obstructive Sleep Apnea after Surgery

The evaluation of a non-invasive respiratory monitor in ards patients in supine and prone position

PURPOSE

The Prone positioning in addition to non invasive respiratory support is commonly used in patients with acute respiratory failure. The aim of this study was to assess the accuracy of an impedance-based non-invasive respiratory volume monitor (RVM) in supine and in prone position.

METHODS

In sedated, paralyzed and mechanically ventilated patients in volume-controlled mode with acute respiratory distress syndrome scheduled for prone positioning it was measured and compared non-invasively tidal volume and respiratory rate provided by the RVM in supine and, subsequently, in prone position, by maintaining unchanged the ventilatory setting.

RESULTS

Forty patients were enrolled. No significant difference was found between measurements in supine and in prone position either for tidal volume (p = 0.795; p = 0.302) nor for respiratory rate (p = 0.181; p = 0.604). Comparing supine vs. prone position, the bias and limits of agreements for respiratory rate were 0.12 bpm (-1.4 to 1.6) and 20 mL (-80 to 120) for tidal volume.

CONCLUSIONS

The RVM is accurate in assessing tidal volume and respiratory rate in prone compared to supine position. Therefore, the RVM could be applied in non-intubated patients with acute respiratory failure receiving prone positioning to monitor respiratory function.

Consensus Statements among European Sleep Surgery Experts on Snoring and Obstructive Sleep Apnea: Part 2 Decision-Making in Surgical Management and Peri-Operative Considerations

Background: Reaching consensus on decision-making in surgical management and peri-operative considerations regarding snoring and obstructive sleep apnea (OSA) among sleep surgeons is critical in the management of patients with such conditions, where there is a large degree of variability. Methods: A set of statements was developed based on the literature and circulated among eight panel members of European experts, utilizing the Delphi method. Responses were provided as agree and disagree on each statement, and the comments were used to assess the level of consensus and develop a revised version. The new version, with the level of consensus and anonymized comments, was sent to each panel member as the second round. This was repeated for a total of five rounds. Results: The final set included a total of 71 statements: 29 stand-alone and 11 with 42 sub-statements. On the 33 statements regarding decision-making in surgical management, there was 60.6%, 27.3%, and 6.1% consensus among all eight, seven, and six panelists, respectively. On the 38 statements regarding the peri-operative considerations, there was 55.3%, 18.4%, and 15.8% consensus among all eight, seven, and six panelists, respectively. Conclusions: These results indicate the need for an expanded review of the literature and discussion to enhance consensus among the sleep surgeons that consider surgical management in patients with snoring and OSA.

Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review

Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals

Due to the simplicity and convenience of PPG signal acquisition, the detection of the respiration rate based on the PPG signal is more suitable for dynamic monitoring than the impedance spirometry method, but it is challenging to achieve accurate predictions from low-signal-quality PPG signals, especially in intensive-care patients with weak PPG signals. The goal of this study was to construct a simple model for respiration rate estimation based on PPG signals using a machine-learning approach fusing signal quality metrics to improve the accuracy of estimation despite the low-signal-quality PPG signals. In this study, we propose a method based on the whale optimization algorithm (WOA) with a hybrid relation vector machine (HRVM) to construct a highly robust model considering signal quality factors to estimate RR from PPG signals in real time. To detect the performance of the proposed model, we simultaneously recorded PPG signals and impedance respiratory rates obtained from the BIDMC dataset. The results of the respiration rate prediction model proposed in this study showed that the MAE and RMSE were 0.71 and 0.99 breaths/min, respectively, in the training set, and 1.24 and 1.79 breaths/min, respectively, in the test set. Compared without taking signal quality factors into account, MAE and RMSE are reduced by 1.28 and 1.67 breaths/min, respectively, in the training set, and reduced by 0.62 and 0.65 breaths/min in the test set. Even in the nonnormal breathing range below 12 bpm and above 24 bpm, the MAE reached 2.68 and 4.28 breaths/min, respectively, and the RMSE reached 3.52 and 5.01 breaths/min, respectively. The results show that the model that considers the PPG signal quality and respiratory quality proposed in this study has obvious advantages and application potential in predicting the respiration rate to cope with the problem of low signal quality.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

Smart Recognition COVID-19 System to Predict Suspicious Persons Based on Face Features

The coronavirus (COVID-19) is identified at first in Wuhan in December 2019. The apparition of the COVID-19 virus is widely spread to concern all countries worldwide. The World Health Organization (WHO) on March 11 declare COVID-19 a pandemic. This Virus causes a serious infection of the respiratory system. Its high transmission constitutes great problems and challenges. The WHO proposes many actions to limit the spread of the virus such as quarantine and decrease or halt flights between states. The actions taken by states in airports are to detect suspicious persons with COVID-19. We aimed to provide a Computer-Aided Diagnosis (CAD) framework to predict suspicious COVID-19 person. This prediction identifies suspicious persons who suffer from shortness breath which is the main symptom of this disease. Extract shortness breath anomaly through the estimated heart rate from face based-video is the main contribution of the present paper. We developed a Smart Recognition COVID-19 (SRC) system to estimate the breath score. In conclusion, our study achieves an accurate breath score. The error is about 1 breath per minute. The proposed solution is of great importance because it helps managers in the airport to predict suspicious COVID-19 passengers.

A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea

BACKGROUND

Obstructive sleep apnea (OSA), the most common upper-airway disease, is closely associated with the risk of cardiovascular diseases. However, the early screening of OSA is a main challenge, relying on polysomnography (PSG) or home sleep apnea test (HSAT) in hospitals. Photoplethysmography (PPG) has been developed as a novel technology for screening of OSA, while the validation of PPG-based smart devices is limited compared to that for PSG or HSAT devices.

OBJECTIVE

This study aimed to investigate the feasibility and validity of a PPG-based smartwatch in the screening of OSA.

METHODS

A total of 119 patients were recruited from the Chinese People's Liberty Army General Hospital (Beijing, China). Among them, 20 patients were assessed for a whole-night sleep study by a smartwatch and PSG simultaneously, as well as 82 cases by a smartwatch and HSAT simultaneously. Using PSG or HSAT as the "gold standard", we compared the accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and positive likelihood ratio (+LR) or negative likelihood ratio (-LR) at three apnea hypopnea index (AHI) levels: AHI≥5, AHI≥15, and AHI≥30.

RESULTS

A total of 17/119 patients were excluded from the study due to the poor quality of PPG signals. Among the remaining cases, 83 patients were diagnosed with OSA. Compared to HSAT device, the accuracy, sensitivity, and specificity of the PPG-based smartwatch in predicting moderate-to-severe OSA patients (AHI≥15) were 87.9%, 89.7%, and 86.0%, respectively. Compared to PSG device, the accuracy, sensitivity, and specificity of the PPG-based smartwatch in predicting OSA in patients (AHI≥5) were 81.1%, 76.5%, and 100%, respectively.

CONCLUSION

The PPG-based smartwatch outperformed in terms of detecting OSA; nevertheless, validation in a large-scale population is imperative.

TRIAL REGISTRATION

Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Variations in respiratory rate do not reflect changes in tidal volume or minute ventilation after major abdominal surgery

Monitoring of postoperative pulmonary function usually includes respiratory rate and oxygen saturation measurements. We hypothesized that changes in postoperative respiratory rate do not correlate with changes in tidal volume or minute ventilation. In addition, we hypothesized that variability of minute ventilation and tidal volume is larger than variability of respiratory rate. Respiratory rate and changes in tidal volume and in minute ventilation were continuously measured in 27 patients during 24 h following elective abdominal surgery, using an impedance-based non-invasive respiratory volume monitor (ExSpiron, Respiratory Motion, Waltham, MA, US). Coefficients of variation were used as a measure for variability of respiratory rate, tidal volume and minute ventilation. Data of 38,149 measurements were analyzed. We found no correlation between respiratory rate and tidal volume or minute ventilation (r² = 0.02 and 0.01). Mean respiratory rate increased within the first 24 h after abdominal surgery from 13.9 ± 2.5 to 16.2 ± 2.4 breaths/min (p = 0.008), while tidal volume and minute ventilation remained unchanged (p = 0.90 and p = 0.18). Of interest, variability of respiratory rate (0.21 ± 0.06) was significantly smaller than variability of tidal volume (0.37 ± 0.12, p < 0.001) and minute ventilation (0.41 ± 0.12, p < 0.001). Changes in postoperative respiratory rate do not allow conclusions about changes in tidal volume or minute ventilation. We suggest that postoperative alveolar hypoventilation may not be recognized by monitoring respiratory rate alone. Variability of respiratory rate is smaller than variability in tidal volume and minute ventilation, suggesting that adaptations of alveolar ventilation to metabolic needs may be predominately achieved by variations in tidal volume.

Non-invasive continuous respiratory monitoring using temperature-based sensors

Respiratory rate (RR) is a key vital sign that has been traditionally employed in the clinical assessment of patients and in the prevention of respiratory compromise. Despite its relevance, current practice for monitoring RR in non-intubated patients strongly relies on visual counting, which delivers an intermittent and error-prone assessment of the respiratory status. Here, we present a novel non-invasive respiratory monitor that continuously measures the RR in human subjects. The respiratory activity of the user is inferred by sensing the thermal transfer between the breathing airflow and a temperature sensor placed between the nose and the mouth. The performance of the respiratory monitor is assessed through respiratory experiments performed on healthy subjects. Under spontaneous breathing, the mean RR difference between our respiratory monitor and visual counting was 0.4 breaths per minute (BPM), with a 95% confidence interval equal to [- 0.5, 1.3] BPM. The robustness of the respiratory sensor to the position is assessed by studying the signal-to-noise ratio in different locations on the upper lip, displaying a markedly better performance than traditional thermal sensors used for respiratory airflow measurements.

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

Life-threatening perioperative anesthetic complications: major issues surrounding perioperative morbidity and mortality

Perioperative morbidity and mortality related to anesthesia involves multiple factors. Patient characteristics and comorbidities play a role in many of these events, highlighting the importance of preoperative screening. While optimization of patient comorbidities is not always possible, having data regarding those comorbidities can prove life-saving. Equipment and medication considerations also enter into untoward outcomes such as anesthetic interventions outside of the traditional operating room where resources are sometimes lacking and haste creates errors. Ultimately, when surgeons and anesthesiologists cooperate in patient care, communicating concisely but thoroughly, patients are more likely to do well. The language of surgeons is that of diagnosis requiring a surgical intervention, while anesthesiologists are discussing patient comorbidities impacted by anesthetic medications, positive pressure ventilation, neuraxial techniques, ramifications of patient positioning, effects of opiates and so on. Because all of the considerations combine in determining outcomes, it is incumbent on both surgeons and anesthesiologists to understand those elements leading to severe morbid events as well as death. This review touches on many of the most important factors.