Averaging Times for Pulse Oximeter Measurements - A Review of Manuscripts Published in the Top Five Sleep Medicine Journals.

Purpose: Clinical management decisions often rely on a patient's SpO2 level and desaturation rate. Limitations include that measurements depend on the averaging time (AVT) used, which is particularly relevant to sleep medicine, but has yet received little attention. Methods: Cross-sectional review of studies reporting pulse oximeter saturation (SpO2) measurements published in 5 leading sleep medicine journals. All papers published between 2017 and 2023 reporting SpO2 measurements were screened regarding the AVT used. Results: Of 193 papers identified, 151 were included; of these, only 9 studies mentioned the AVT, 4 of these were published in one journal. The AVT ranged from zero (beat-to-beat-mode) to 10s, with 3s being used most often (33.3%), followed by 2s (22.2%). Conclusion: The AVT is only rarely mentioned in sleep medicine papers, despite its influence on sleep study results. Reported AVTs were heterogenous. Further research is warranted to set up guidelines for using or reporting the AVT.

Derivation and external validation of predictive models for invasive mechanical ventilation in intensive care unit patients with COVID-19.

BACKGROUND: This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. METHODS: A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database. RESULTS: Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07-2.05), 0.81 (0.72-0.90), 9.13 (3.29-28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2-9.8] versus 9.6 [6.8-12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%). CONCLUSIONS: In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. GOV IDENTIFIER: NCT05663528.

An efficient model for extracting respiratory and blood oxygen saturation data from photoplethysmogram signals by removing motion artifacts using heuristic-aided ensemble learning model.

Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models.

Combined Effects of Moderate Hypoxia and Sleep Restriction on Mental Workload.

Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using the Multi-Attribute Test Battery (MATB)-II with an additional auditory Oddball-like task. Seventeen healthy subjects were subjected in random order to three 12-min periods of increased MW level (low, medium, and high): sleep restriction (SR, <3 h of total sleep time (TST)) vs. habitual sleep (HS, >6 h TST), hypoxia (HY, 2 h, FIO2 = 13.6%, ~3500 m vs. normoxia, NO, FIO2 = 21%). Following each MW level, participants completed the NASA-TLX subjective MW scale. Increasing MW decreases performance on the MATB-II Tracking task (p = 0.001, MW difficulty main effect) and increases NASA-TLX (p = 0.001). In the combined HY/SR condition, MATB-II performance was lower, and the NASA-TLX score was higher compared with the NO/HS condition, while no effect of hypoxia alone was observed. In the accuracy of the auditory task, there is a significant interaction between hypoxia and MW difficulty (F(2-176) = 3.14, p = 0.04), with lower values at high MW under hypoxic conditions. Breathing rate, pupil size, and amplitude of pupil dilation response (PDR) to auditory stimuli are associated with increased MW. These parameters are the best predictors of increased MW, independently of physiological constraints. Adding ECG, SpO2, or electrodermal conductance does not improve model performance. In conclusion, hypoxia and sleep restriction have an additive effect on MW. Physiological and electrophysiological responses must be taken into account when designing a MW predictive model and cross-validation.

Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring.

Peripheral Capillary Oxygen Saturation (SpO2) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO2, this paper proposes a novel neural network model named "ITSCAN" based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual's SpO2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO2 estimator generates the corresponding SpO2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health.

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.

PURPOSE: This study aims to develop sleep apnea screening models with overnight SpO2 data, and to investigate the impact of the SpO2 data granularity on model performance. METHODS: A total of 7,718 SpO2 recordings from the SHHS and MESA datasets were used. Probabilistic ensemble machine learning was employed to predict sleep apnea status at three AHI cutoff points: ≥ 5, ≥ 15, and ≥ 30 events/hour. To investigate the impact of data granularity, SpO2 data were aggregated at 30, 60, and 300 s. RESULTS: Our models demonstrated good to excellent performance on internal test, with average area under the curve (AUC) values of 0.91, 0.93, and 0.96 for cutoffs ≥ 5, ≥ 15, and ≥ 30 at data granularity of 1 s, respectively. Both sensitivity (0.76, 0.84, 0.89) and specificity (0.87, 0.86, 0.90) ranged from good to excellent across three cutoffs. Positive predictive values (PPV) ranged from excellent to fair (0.97, 0.83, 0.66), and negative predictive values (NPV) ranged from low to excellent (0.43, 0.87, 0.98). Model performance on external test slightly dropped compared to internal test, but still achieved good to excellent AUC above 0.80 across all data granularity and all the three cutoffs. Data granularity of 300 s led to a reduction in performance metrics across all cutoffs. CONCLUSION: Our models demonstrated superior performance across all three AHI cutoff thresholds compared to existing large sleep apnea screening models, even when considering varying SpO2 data granularity. However, lower data granularity was associated with decreased screening performance, indicating a need for further research in this area.

Measures of overnight oxygen saturation to characterize sleep apnea severity and predict postoperative respiratory depression.

BACKGROUND: Sleep apnea syndrome, characterized by recurrent cessation (apnea) or reduction (hypopnea) of breathing during sleep, is a major risk factor for postoperative respiratory depression. Challenges in sleep apnea assessment have led to the proposal of alternative metrics derived from oxyhemoglobin saturation (SpO2), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO2 below 90% (CT90), as predictors of postoperative respiratory depression. However, their performance has been limited with area under the curve of 0.60 for ODI and 0.59 for CT90. Our objective was to propose novel features from preoperative overnight SpO2 which are correlated with sleep apnea severity and predictive of postoperative respiratory depression. METHODS: Preoperative SpO2 signals from 235 surgical patients were retrospectively analyzed to derive seven features to characterize the sleep apnea severity. The features included entropy and standard deviation of SpO2 signal; below average burden characterizing the area under the average SpO2; average, standard deviation, and entropy of desaturation burdens; and overall nocturnal desaturation burden. The association between the extracted features and sleep apnea severity was assessed using Pearson correlation analysis. Logistic regression was employed to evaluate the predictive performance of the features in identifying postoperative respiratory depression. RESULTS: Our findings indicated a similar performance of the proposed features to the conventional apnea-hypopnea index (AHI) for assessing sleep apnea severity, with average area under the curve ranging from 0.77 to 0.81. Notably, entropy and standard deviation of overnight SpO2 signal and below average burden showed comparable predictive capability to AHI but with minimal computational requirements and individuals' burden, making them promising for screening purposes. Our sex-based analysis revealed that compared to entropy and standard deviation, below average burden exhibited higher sensitivity in detecting respiratory depression in women than men. CONCLUSION: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.

Effects of Nasal Packing on Patients' Post-operative Vital Signs.

Background Since bilateral nasal packing entails nasal and airway obstruction, this practice consequently leads to oral breathing. The resulting hypoxemia may then negatively impact vital signs, including blood pressure (BP), blood oxygen saturation (SpO2), and heart rate (HR). These systemic effects have a detrimental effect on patients. Objective The objective of this study is to observe the effects of bilateral nasal packing on patients' post-operative vital signs. Materials and methods This prospective study was conducted in the department of otolaryngology - head and neck surgery over a six-month period. The study included 83 post-operative patients with nasal surgery, in which bilateral merocele nasal packing was performed. The patients' pulse oximetry, systolic and diastolic BP, and HR were recorded four times the night before and after surgery. A statistical analysis was performed, and the mean values, standard deviation, and range were calculated. A paired sample t-test was also applied. The results are presented in figures and tables. Results The mean age of the participants was 27.65 ± 10.72 years, and 56 (67.5%) were male. Septoplasty was the most common surgery performed, with 63 participants having undergone this procedure (75.9%). When the pre-operative mean values of systolic and diastolic BP, SpO2, and HR were compared with the post-operative mean values, when a bilateral nasal pack was in place, a significant increase was found in all, with a p-value of <0.001 in each. Conclusion Bilateral nasal packing affects patients' vital signs by significantly increasing diastolic and systolic BP and decreasing SpO2. The HR is also significantly increased when packing is in place.

Vital Signs as Predictors of Oxygen Requirements in COVID-19 Outpatients: The Development of a Streamlined Risk Score.

BACKGROUND: With COVID-19 becoming a common disease, primary care facilities such as clinics are required to efficiently triage patients at high risk of severe illness within the constraints of limited medical resources. However, existing COVID-19 severity risk scores require detailed medical history assessments, such as evaluating the severity of pneumonia via chest CT and accounting for past and comorbid conditions. Therefore, they may not be suitable for practical use in clinical settings with limited medical resources, including personnel and equipment. PURPOSE:  The goal is to identify key variables that predict the need for oxygen therapy in COVID-19 patients and develop a simplified clinical risk score based solely on vital signs to predict oxygen requirements. PATIENTS AND METHODS: A retrospective observational study of 584 outpatients with COVID-19 confirmed by polymerase chain reaction test visited Sasebo Chuo Hospital between April 28, 2022, and August 18, 2022. Analyses were conducted after adjustment for background factors of age and sex with propensity score matching. We used the Fisher test for nominal variables and the Kruskal-Wallis test for continuous variables. RESULTS: After adjusting for age and sex, several factors significantly correlated with the need for oxygen within seven days including body temperature (p < 0.001), respiratory rate (p = 0.007), SpO2 (p < 0.001), and the detection of pneumonia on CT scans (p = 0.032). The area under the receiver-operating characteristic curve for the risk score based on these vital signs and CT was 0.947 (95% confidence interval: 0.911-0.982). The risk score based solely on vital signs was 0.937 (0.900-0.974), demonstrating the ability to predict oxygen administration with no significant differences. CONCLUSIONS: Body temperature, advanced age, increased respiratory rate, decreased SpO2, and the presence of pneumonia on CT scans were significant predictors of oxygen need within seven days among the study participants. The risk score, based solely on vital signs, effectively and simply assesses the likelihood of requiring oxygen therapy within seven days with high accuracy. The risk score, which utilizes only age and vital signs and does not require a detailed patient history or CT scans, could streamline hospital referral processes for admissions.

Performance of a Wearable Ring in Controlled Hypoxia: A Prospective Observational Study.

BACKGROUND: Over recent years, technological advances in wearables have allowed for continuous home monitoring of heart rate and oxygen saturation. These devices have primarily been used for sports and general wellness and may not be suitable for medical decision-making, especially in saturations below 90% and in patients with dark skin color. Wearable clinical-grade saturation of peripheral oxygen (SpO2) monitoring can be of great value to patients with chronic diseases, enabling them and their clinicians to better manage their condition with reliable real-time and trend data. OBJECTIVE: This study aimed to determine the SpO2 accuracy of a wearable ring pulse oximeter compared with arterial oxygen saturation (SaO2) in a controlled hypoxia study based on the International Organization for Standardization (ISO) 80601-2-61:2019 standard over the range of 70%-100% SaO2 in volunteers with a broad range of skin color (Fitzpatrick I to VI) during nonmotion conditions. In parallel, accuracy was compared with a calibrated clinical-grade reference pulse oximeter (Masimo Radical-7). Acceptable medical device accuracy was defined as a maximum of 4% root mean square error (RMSE) per the ISO 80601-2-61 standard and a maximum of 3.5% RMSE per the US Food and Drug Administration guidance. METHODS: We performed a single-center, blinded hypoxia study of the test device in 11 healthy volunteers at the Hypoxia Research Laboratory, University of California at San Francisco, under the direction of Philip Bickler, MD, PhD, and John Feiner, MD. Each volunteer was connected to a breathing apparatus for the administration of a hypoxic gas mixture. To facilitate frequent blood gas sampling, a radial arterial cannula was placed on either wrist of each participant. One test device was placed on the index finger and another test device was placed on the fingertip. SaO2 analysis was performed using an ABL-90 multi-wavelength oximeter. RESULTS: For the 11 participants included in the analysis, there were 236, 258, and 313 SaO2-SpO2 data pairs for the test device placed on the finger, the test device placed on the fingertip, and the reference device, respectively. The RMSE of the test device for all participants was 2.1% for either finger or fingertip placement, while the Masimo Radical-7 reference pulse oximeter RMSE was 2.8%, exceeding the standard (4% or less) and the Food and Drug Administration guidance (3.5% or less). Accuracy of SaO2-SpO2 paired data from the 4 participants with dark skin in the study was separately analyzed for both test device placements and the reference device. The test and reference devices exceeded the minimum accuracy requirements for a medical device with RMSE at 1.8% (finger) and 1.6% (fingertip) and for the reference device at 2.9%. CONCLUSIONS: The wearable ring meets an acceptable standard of accuracy for clinical-grade SpO2 under nonmotion conditions without regard to skin color. TRIAL REGISTRATION: ClinicalTrials.gov NCT05920278; https://clinicaltrials.gov/study/NCT05920278.

Evaluation of a Lung Ultrasound Score in Hospitalized Adult Patients with COVID-19 in Barcelona, Spain.

Background/Objectives: During the COVID-19 pandemic and the burden on hospital resources, the rapid categorization of high-risk COVID-19 patients became essential, and lung ultrasound (LUS) emerged as an alternative to chest computed tomography, offering speed, non-ionizing, repeatable, and bedside assessments. Various LUS score systems have been used, yet there is no consensus on an optimal severity cut-off. We assessed the performance of a 12-zone LUS score to identify adult COVID-19 patients with severe lung involvement using oxygen saturation (SpO2)/fractional inspired oxygen (FiO2) ratio as a reference standard to define the best cut-off for predicting adverse outcomes. Methods: We conducted a single-centre prospective study (August 2020-April 2021) at Hospital del Mar, Barcelona, Spain. Upon admission to the general ward or intensive care unit (ICU), clinicians performed LUS in adult patients with confirmed COVID-19 pneumonia. Severe lung involvement was defined as a SpO2/FiO2 ratio <315. The LUS score ranged from 0 to 36 based on the aeration patterns. Results: 248 patients were included. The admission LUS score showed moderate performance in identifying a SpO2/FiO2 ratio <315 (area under the ROC curve: 0.71; 95%CI 0.64-0.77). After adjustment for COVID-19 risk factors, an admission LUS score ≥17 was associated with an increased risk of in-hospital death (OR 5.31; 95%CI: 1.38-20.4), ICU admission (OR 3.50; 95%CI: 1.37-8.94) and need for IMV (OR 3.31; 95%CI: 1.19-9.13). Conclusions: Although the admission LUS score had limited performance in identifying severe lung involvement, a cut-off ≥17 score was associated with an increased risk of adverse outcomes. and could play a role in the rapid categorization of COVID-19 pneumonia patients, anticipating the need for advanced care.

Síndrome de distrés respiratorio agudo en la pos-pandemia: una nueva definición global con extensión a regiones de menos recursos / Post-Pandemic Acute Respiratory Distress Syndrome: A New Global Definition with Extension to Lower-Resource Regions

El síndrome de dificultad respiratoria aguda (SDRA), inicialmente descrito en 1967, se caracteriza por insuficiencia respiratoria aguda con hipoxemia profunda, disminución de la distensibilidad pulmonar e infiltrados bilaterales en la Rx de tórax. En 2012 la definición de Berlín estableció tres categorías con base en la hipoxemia (SDRA leve, moderado y grave), precisando aspectos temporales y permitiendo el diagnóstico con ventilación no invasiva. La pandemia de COVID-19 llevó a reconsiderar la definición, enfocándose en el monitoreo continuo de la oxigenación y la oxigenoterapia de alto flujo. En 2021 se propuso una nueva definición global de SDRA, basada en la definición de Berlín, pero incluyendo una categoría para pacientes no intubados, permitiendo el uso de saturación periférica de oxígeno medida con oximetría de pulso/fracción inspirada de oxígeno (SpO2/FiO2) y la ecografía pulmonar para el diagnóstico, y sin ningún requerimiento de soporte especial de la oxigenación en regiones con recursos limitados. Aunque persisten debates, la evolución continua busca adaptarse a las necesidades clínicas y epidemiológicas, y personalizar tratamientos. (AU)

Acute respiratory distress syndrome (ARDS), first described in 1967, is characterized by acute respiratory failure causing profound hypoxemia, decreased pulmonary compliance, and bilateral CXR infiltrates. After several descriptions, the Berlin definition was adopted in 2012, which established three categories of severity according to hypoxemia (mild, moderate and severe), specified temporal aspects for diagnosis, and incorporated the use of non-invasive ventilation. The COVID-19 pandemic led to changes in ARDS management, focusing on continuous monitoring of oxygenation and on utilization of high-flow oxygen therapy and lung ultrasound. In 2021, a New Global Definition based on the Berlin definition of ARDS was proposed, which included a category for non-intubated patients, considered the use of SpO2, and established no particular requirement for oxygenation support in regions with limited resources. Although debates persist, the continuous evolution seeks to adapt to clinical and epidemiological needs, and to the search of personalized treatments. (AU)

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements.

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Effects of no mask, a surgical mask and a fabric buff on peripheral oxygenation saturation during moderate intensity exercise: a randomised cross-over study.

Background: Mask-wearing caused significant reductions in coronavirus disease 2019 (COVID-19) transmission. We aimed to determine whether face mask-wearing during exercise caused reductions in peripheral oxygen saturation (SpO2) and whether it affected secondary physiological measures [end-tidal carbon dioxide (EtCO2), respiratory rate (RR), heart rate (HR), expired breath temperature (EBT)]. Subjective measurements included ratings of perceived exertion (RPE), ratings of perceived breathlessness (RPB), and symptomology. Methods: A randomised cross-over trial examined no mask (NM), surgical mask (SM) and a buff mask (BM). Thirty participants (30-45 years) cycled at 60% power output for 30 min in three exercise sessions, 24 h apart, within 6 days. Each session recorded all measures at resting baseline (T0), 9 min (T1), 18 min (T2), and 27 min (T3). Dependent statistical tests determined significant differences between masks and time-points. Results: SpO2 decreased for SM and BM between T0 compared to T1, T2 and T3 (all P<0.005). BM caused significant reductions at T1 and T2 compared to NM (P<0.001 and P=0.018). Significant changes in EtCO2 and EBT occurred throughout exercise and between exercise stages for all mask conditions (P<0.001). As expected for moderate intensity exercise, RR and HR were significantly higher during exercise compared to T0 (P<0.001). RPB significantly increased for each condition at each time point (P<0.001). RPE was not significant between mask conditions at any exercise stage. Conclusions: SM and BM caused a mild but sustained reduction in SpO2 at commencement of exercise, which did not worsen throughout short (<30 min) moderate intensity exercise. Level of perception was similar, suggesting healthy people can wear masks during moderate exercise and activities of daily living.

Clinical factors associated with racial differences in the prevalence of occult hypoxemia: a retrospective case-control study.

Background: Recent studies showed that Black patients more often have falsely normal oxygen saturation on pulse oximetry compared to White patients. However, whether the racial differences in occult hypoxemia are mediated by other clinical differences is unknown. Methods: We conducted a retrospective case-control study utilizing two large ICU databases (eICU and MIMIC-IV). We defined occult hypoxemia as oxygen saturation on pulse oximetry within 92-98% despite oxygen saturation on arterial blood gas below 90%. We assessed associations of commonly measured clinical factors with occult hypoxemia using multivariable logistic regression and conducted mediation analysis of the racial effect. Results: Among 24,641 patients, there were 1,855 occult hypoxemia cases and 23,786 controls. In both datasets, Black patients were more likely to have occult hypoxemia (unadjusted odds ratio 1.66 [95%-CI: 1.41-1.95] in eICU and 2.00 [95%-CI: 1.22-3.14] in MIMIC-IV). In multivariable models, higher respiratory rate, PaCO2 and creatinine as well as lower hemoglobin were associated with increased odds of occult hypoxemia. Differences in the commonly measured clinical markers accounted for 9.2% and 44.4% of the racial effect on occult hypoxemia in eICU and MIMIC-IV, respectively. Conclusion: Clinical differences, in addition to skin tone, might mediate some of the racial differences in occult hypoxemia.

Post-pandemic acute respiratory distress syndrome: A New Global Definition with extension to lower-resource regions.

Acute respiratory distress syndrome (ARDS), first described in 1967, is characterized by acute respiratory failure causing profound hypoxemia, decreased pulmonary compliance, and bilateral CXR infiltrates. After several descriptions, the Berlin definition was adopted in 2012, which established three categories of severity according to hypoxemia (mild, moderate and severe), specified temporal aspects for diagnosis, and incorporated the use of non-invasive ventilation. The COVID-19 pandemic led to changes in ARDS management, focusing on continuous monitoring of oxygenation and on utilization of high-flow oxygen therapy and lung ultrasound. In 2021, a New Global Definition based on the Berlin definition of ARDS was proposed, which included a category for non-intubated patients, considered the use of SpO2, and established no particular requirement for oxygenation support in regions with limited resources. Although debates persist, the continuous evolution seeks to adapt to clinical and epidemiological needs, and to the search of personalized treatments.

Association between SpO2 and the risk of death in elderly T2DM patients with cerebral infarction: a retrospective cohort study.

Objective: This study aimed to evaluate the SpO2 (transcutaneous oxygen saturation) -mortality link in elderly T2DM (diabetes mellitus type 2) patients with cerebral infarction and identify their optimal SpO2 range. Methods: In this investigation, we employed a comprehensive approach. Initially, we screened the MIMIC-IV database, identifying elderly T2DM patients with cerebral infarction, utilizing specific ICD-9 and ICD-10 codes. We then harnessed the power of restricted cubic splines to craft a visual representation of the correlation between SpO2 and 1-year mortality. To enhance our analysis, we harnessed Cox multivariate regression, allowing us to compute adjusted hazard ratios (HR) accompanied by 95% confidence intervals (CIs). Additionally, we crafted Cumulative Mortality Curve analyses, augmenting our study by engaging in rigorous subgroup analyses, stratifying our observations based on pertinent covariates. Results: In this study, 448 elderly T2DM patients with cerebral infarction were included. Within 1-year post-discharge, 161 patients (35.94%) succumbed. Employing Restricted Cubic Spline analysis, a statistically significant U-shaped non-linear relationship between admission ICU SpO2 levels and 1-year mortality was observed (P-value < 0.05). Further analysis indicated that both low and high SpO2 levels increased the mortality risk. Cox multivariate regression analysis, adjusting for potential confounding factors, confirmed the association of low (≤94.5%) and high SpO2 levels (96.5-98.5%) with elevated 1-year mortality risk, particularly notably high SpO2 levels (>98.5%) [HR = 2.06, 95% CI: 1.29-3.29, P-value = 0.002]. The cumulative mortality curves revealed the following SpO2 subgroups from high to low cumulative mortality at the 365th day: normal levels (94.5% < SpO2 ≤ 96.5%), low levels (SpO2 ≤ 94.5%), high levels (96.5% < SpO2 ≤ 98.5%), and notably high levels (>98.5%). Subgroup analysis demonstrated no significant interaction between SpO2 and grouping variables, including Sex, Age, Congestive heart failure, Temperature, and ICU length of stay (LOS-ICU; P-values for interaction were >0.05). Conclusions: Striking an optimal balance is paramount, as fixating solely on lower SpO2 limits or neglecting high SpO2 levels may contribute to increased mortality rates. To mitigate mortality risk in elderly T2DM patients with cerebral infarction, we recommend maintaining SpO2 levels within the range of 94.5-96.5%.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

Repeated short cold-water immersions are sufficient to habituate to the cold, but do not lead to adaptations during exercise in normobaric hypoxia.

We sought to assess the effects of repeated cold-water immersions (CWI) on respiratory, metabolic, and sympathoadrenal responses to graded exercise in hypoxia. Sixteen (2 female) participants (age: 21.2 ± 1.3 years; body fat: 12.3 ± 7.7%; body surface area 1.87 ± 0.16 m2, VO2peak: 48.7 ± 7.9 mL/kg/min) underwent 6 CWI in 12.0 ± 1.2 °C. Each CWI was 5 min, twice daily, separated by ≥4 h, for three consecutive days, during which metabolic data were collected. The day before and after the repeated CWI intervention, participants ran in normobaric hypoxia (FIO2 = 0.135) for 4 min at 25%, 40%, 60%, and 75% of their sea level peak oxygen consumption (VO2peak). CWI had no effect on VO2 (p > 0.05), but reduced the VE (CWI #1: 27.1 ± 17.8 versus CWI #6: 19.9 ± 12.1 L/min) (p < 0.01), VT (CWI #1: 1.3 ± 0.4 vs CWI #6: 1.1 ± 0.4 L) (p < 0.01), and VE:VO2 (CWI #1: 53.5 ± 24.1 vs CWI #6: 41.6 ± 20.5) (p < 0.01) during subsequent CWI. Further, post exercise plasma epinephrine was lower after CWI compared to before (103.3 ± 43.1; 73.4 ± 34.6 pg/mL) (p = 0.03), with no change in pre-exercising values (75.4 ± 30.7; 72.5 ± 25.9 pg/mL). While these changes were noteworthy, it is important to acknowledge there were no changes in pulmonary (VE, VT, and VE:VO2) or metabolic (VO2, SmO2, and SpO2) variables across multiple hypoxic exercise workloads following repeated CWI. CWI habituated participants to cold water, but this did not lead to adaptations during exercise in normobaric hypoxia.

CL-SPO2Net: Contrastive Learning Spatiotemporal Attention Network for Non-Contact Video-Based SpO2 Estimation.

Video-based peripheral oxygen saturation (SpO2) estimation, utilizing solely RGB cameras, offers a non-contact approach to measuring blood oxygen levels. Previous studies set a stable and unchanging environment as the premise for non-contact blood oxygen estimation. Additionally, they utilized a small amount of labeled data for system training and learning. However, it is challenging to train optimal model parameters with a small dataset. The accuracy of blood oxygen detection is easily affected by ambient light and subject movement. To address these issues, this paper proposes a contrastive learning spatiotemporal attention network (CL-SPO2Net), an innovative semi-supervised network for video-based SpO2 estimation. Spatiotemporal similarities in remote photoplethysmography (rPPG) signals were found in video segments containing facial or hand regions. Subsequently, integrating deep neural networks with machine learning expertise enabled the estimation of SpO2. The method had good feasibility in the case of small-scale labeled datasets, with the mean absolute error between the camera and the reference pulse oximeter of 0.85% in the stable environment, 1.13% with lighting fluctuations, and 1.20% in the facial rotation situation.