Overnight pulse oximetry for sleep-disordered breathing in adults: a review

Inter-night variability of in-home, overnight pulse oximetry screening in an asymptomatic older adult population

PURPOSE

Obstructive sleep apnoea (OSA) is common, yet often undiagnosed. Self-administered, overnight pulse oximetry (OPO) could screen for OSA in asymptomatic, older populations. However, the inter-night variability of OPO in an asymptomatic, older population is unknown. We determined the inter-night variability of home OPO parameters in an older population and correlated with sleep questionnaires.

METHODS

Participants > 50 years without a diagnosis of OSA undertook home OPO for three consecutive nights and completed two sleep questionnaires (STOP-BANG (SBQ) and Epworth Sleepiness Score (ESS)). Analysis was performed with linear mixed models and Spearman's correlation coefficient.

RESULTS

There was no difference in oxygen desaturation index (ODI), MeanSpO2, MinimumSpO2, and time spent with SpO2 < 90% (T90) across two or three nights (P ≥ 0.282). However, the variability of all parameters across nights increased with the magnitude of departure from normal values (P ≤ 0.002). All OPO parameters were associated with age (P ≤ 0.034) and body mass index (P ≤ 0.049). There was a weak correlation between three OPO parameters and SBQ (absolute ρ = 0.22 to 0.32; P ≤ 0.021), but not ESS (P ≥ 0.254).

CONCLUSION

Inter-night variability of home OPO was minimal when values were near-normal in an older population. However, as values depart from normal, the inter-night variability increases, indicating the need for multiple night recordings. Low correlation to sleep questionnaires suggest the need for more robust OSA questionnaires in an asymptomatic population.

Development and Validation of a Nomogram Model to Predict Obstructive Sleep Apnea

Objectives: Polysomnography was class I test for who was suspected of obstructive sleep apnea (OSA) which would cost lots of time and money. This study aimed to develop a nomogram model mainly based on oxygen and blood routine indicators to predict OSA. Methods: We retrospectively analyzed 685 patients with suspected OSA at our hospital. Multivariate analysis was used to construct a nomogram. The performance of the nomogram was assessed using calibration and discrimination. Results: The multivariate analysis identified age, gender, body mass index, mean pulse oxygen saturation, percent nighttime with oxygen saturation less than 90%, red blood cell, hematocrit, and red blood cell distribution width SD as significant factors (P < .05). A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using a bootstrapping method. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index of 0.935 [95% confidence interval (CI), 0.917-0.954] according to the internal validation. Discrimination and calibration in the validation group were also good (C-index, 0.957; 95% CI, 0.930-0.984). Conclusion: The newly developed nomogram can effectively help physicians make better clinical decisions, which may save a lot of time and costs.

Risk factors for collisions attributed to microsleep-related behaviors while driving in professional truck drivers

Sleep-disordered breathing (SDB) is prevalent among professional drivers. Although SDB is a known risk factor for truck collisions attributed to microsleep-related behaviors at the wheel (TC-MRBs), the usefulness of overnight pulse oximetry for predicting TC-MRBs is debatable. This retrospective study assessed the association between overnight pulse oximetry parameters, the Epworth Sleepiness Scale (ESS), and TC-MRBs, confirmed by dashcam footage. This study included 108 matched professional truck drivers (TC-MRBs: N = 54; non-TC-MRBs: N = 54), with a mean age and body mass index of 41.9 ± 11.3 years and 23.0 ± 3.7 kg/m2, respectively. Night-time drivers, 4% oxygen desaturation index (ODI), and nadir oxygen saturation (SpO2) were associated with TC-MRBs (odds ratio [95% confidence interval]: 25.63 [5.88-111.77], p < 0.0001; 2.74 [1.02-7.33], p = 0.045; and 3.87 [1.04-14.39], p = 0.04, respectively). The area under the curve of 4% ODI and nadir SpO2 for TC-MRBs were 0.50 and 0.57, respectively. In conclusion, night-time driving, 4% ODI, and nadir SpO2 were significantly associated with TC-MRBs in professional truck drivers. However, the sensitivity of overnight pulse oximetry parameters to predict TC-MRBs in a real-world application was poor. Therefore, combining subjective and objective assessments such as dashcam video footage may be needed to achieve high accuracy for predicting TC-MRBs among professional truck drivers.

Proposal of a diagnostic algorithm based on the use of pulse oximetry in obstructive sleep apnea

PURPOSE

The aims of this study were to assess the cut-off values for oxygen desaturation index ≥ 3% (ODI3) to confirm obstructive sleep apnea (OSA) in subjects undergoing polysomnography (PSG) and home-based respiratory polygraphy (RP), and to propose an algorithm based on pulse oximetry (PO) for initial management of patients with suspected OSA.

METHODS

This was an observational, cross-sectional, retrospective study. ODI3 was used to classify subjects as healthy (no OSA = AHI < 5 or < 15 events/h) or unhealthy (OSA = AHI ≥ 5 or ≥ 15 events/h). On the PSG or experimental group (Exp-G), we determined ODI3 cut-off values with 100% specificity (Sp) for both OSA definitions. ODI3 values without false positives in the Exp-G were applied to a validation group (Val-G) to assess their performance. A strategy based on PO was proposed in patients with suspected OSA.

RESULTS

In Exp-G (PSG) 1141 patients and in Val-G (RP) 1141 patients were included. In Exp-G, ODI3 > 12 (OSA = AHI ≥ 5) had a sensitivity of 69.5% (CI95% 66.1-72.7) and Sp of 100% (CI95% 99-100), while an ODI3 ≥ 26 had a 53.8% sensitivity (CI95% 49.3-58.2) and Sp of 100% (CI95% 99.4-100) for AHI ≥ 15. A high pretest probability for OSA by Berlin questionaire (≥ 2 categories) had a lower diagnostic performance than by STOP-BANG questionnaire ≥ 5 points (AHI ≥ 5: 0.856 vs. 0.899, p < 0.001; AHI ≥ 15: 0.783 vs. 0.807, p 0.026).

CONCLUSION

We propose the initial use of PO at home in cases of moderate-to-high pretest probability of OSA. This algorithm considers PO as well as RP and PSG for more challenging cases or in case of doubt.

A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity

Obstructive sleep apnea (OSA) is a sleep disorder caused by periodic airway obstructions and has been associated with numerous health consequences, which are thought to result from tissue hypoxia. However, challenges in the direct measurement of tissue-level oxygenation make it difficult to analyze the hypoxia exposure pattern in patients. Furthermore, current clinical practice relies on the apnea-hypopnea index (AHI) and pulse oximetry to assess OSA severity, both of which have limitations. To overcome this, we developed a clinically deployable mathematical model, which outputs tissue-level oxygenation. The model incorporates spatial pulmonary oxygen uptake, considers dissolved oxygen, and can use time-dependent patient inputs. It was applied to explore a series of breathing patterns that are clinically differentiated. Supporting previous studies, the result of this analysis indicated that the AHI is an unreliable indicator of hypoxia burden. As a proof of principle, polysomnography data from two patients was analyzed with this model. The model showed greater sensitivity to breathing in comparison with pulse oximetry and provided systemic venous oxygenation, which is absent from clinical measurements. In addition, the dissolved oxygen output was used to calculate hypoxia burden scores for each patient and compared to the clinical assessment, highlighting the importance of event length and cumulative impact of obstructions. Furthermore, an intra-patient statistical analysis was used to underscore the significance of closely occurring obstructive events and to highlight the utility of the model for quantitative data processing. Looking ahead, our model can be used with polysomnography data to predict hypoxic burden on the tissues and help guide patient treatment decisions.

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO₂, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO₂-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

Dynamic assessment of oropharynx with ultrasonography as a screening tool for obstructive sleep apnea

Ultrasonography is an easily available and portable tool to assess the dynamic changes in the upper airway and surrounding soft tissue. This study aimed to evaluate the utility of oropharynx ultrasonography as a screening tool for obstructive sleep apnea (OSA). The study sequentially enrolled overweight individuals (body mass index >25 kg/m² ) and subjected them to OSA screening tools (Berlin questionnaire, Epworth Sleepiness Scale and STOP-Bang scores), ultrasonography of the oropharynx followed by overnight polysomnography. A total of 30 healthy individuals were also recruited as controls. Detailed dynamic and static ultrasonography measurements of the oropharynx and surrounding tissue were done. The diagnostic ability of various ultrasonography parameters to detect OSA was determined using receiver operating characteristic curve analysis. A total of 63 subjects were enrolled, with 33 in the OSA group and 30 in non-OSA overweight group. All baseline characteristics were similar in the two groups. Except for the dynamic measurements of oropharynx (Retropalatal% change-inspiration, retropalatal% change-Muller manoeuvre, retroglossal% change-inspiration, and retroglossal% change-Muller manoeuvre) all other parameters were similar in the OSA and non-OSA overweight subjects. The area under the receiver operating characteristic curve was highest for retropalatal% change-inspiration: 0.989, followed by retropalatal% change-Muller manoeuvre: 0.988. Both were also significant predictors of OSA with odds ratios of 0.338 (p = 0.003; 95% confidence interval [CI] 0.164-0.696) and 0.346 (p = 0.018; 95% CI 0.143-0.837), respectively. Ultrasonography provides a near complete picture of the dynamic changes and collapsibility of the oropharynx and can be an effective tool in screening for OSA.

Phase II study comparing nasal pressure monitoring with capnography during invasive endoscopic procedures: a single-center, single-arm trial

Nasal pressure signal is commonly used to evaluate obstructive sleep apnea. This study aimed to assess its safety for respiratory monitoring during sedation. A total of 45 adult patients undergoing sedation with propofol and fentanyl for invasive endoscopic procedures were enrolled. While both nasal pressure and capnograph signals were continuously recorded, only the nasal pressure signal was displayed. The primary outcome was the incidence of oxygen desaturation below 90%. The secondary outcomes were the ability to predict the desaturation and incidence of harmful events and false alarms, defined as an apnea waveform lasting more than 3 min without desaturation. Of the 45 participants, 43 completed the study. At least one desaturation event occurred in 12 patients (27.9%; 95% confidence interval 15.3-43.7%). In these 12 patients, more than half of the desaturation events were predictable in 9 patients by capnography and 11 patients by nasal pressure monitoring (p = 0.59). In the 43 patients, false alarms were detected in 7 patients with capnography and 11 patients with nasal pressure monitoring (p = 0.427). Harmful events unrelated to nasal pressure monitoring occurred in 2 patients. Nasal pressure monitoring is safe and possibly useful for respiratory monitoring despite false alarms during sedation.

Relationships between brain tissue damage, oxygen desaturation, and disease severity in obstructive sleep apnea evaluated by diffusion tensor imaging

STUDY OBJECTIVES

Patients with obstructive sleep apnea (OSA) show brain injury in sites responsible for autonomic, cognitive, and respiratory functions. Brain changes in OSA may vary with disease severity as assessed by the apnea-hypopnea index (AHI), which does not provide information about the apnea depth and length in contrast to oxygen desaturation. Although significant associations with brain injury and AHI are known in OSA, it is unclear whether AHI or the extent of oxygen desaturations better correlate with brain damage. We evaluated associations between brain changes, AHI, and oxygen desaturation using diffusion tensor imaging-based measures.

METHODS

We acquired diffusion tensor imaging data from 19 patients with OSA using a 3.0-Tesla MRI scanner and calculated, normalized, and smoothed mean, axial, and radial diffusivity maps that were used for correlations between brain changes, oxygen desaturation, and AHI values.

RESULTS

Positive correlations with extent of injury (mean, axial, and radial diffusivity values) and AHI appeared in the frontal areas, cingulate and insula, amygdala, hippocampus, and basal pons, and negative associations emerged in the putamen, internal-capsule, globus-pallidus, and cerebellar cortices. Regional diffusivity values and oxygen desaturation showed positive correlations in the cingulate, frontal, putamen, and cerebellar sites, and negative relationships in several areas, including the occipital cortex.

CONCLUSIONS

Patients with OSA show negative and positive correlations, indicated by increased and decreased diffusivity values, resulting from chronic and acute changes in those areas. The extent of injury in OSA partially depends on the extent of AHI and oxygen desaturation, with the effects representing continued development from acute to chronic processes.

CITATION

Sahib A, Roy B, Kang D, Aysola RS, Wen E, Kumar R. Relationships between brain tissue damage, oxygen desaturation, and disease severity in obstructive sleep apnea evaluated by diffusion tensor imaging. J Clin Sleep Med. 2022;18(12):2713-2721.

Artifacts classification and apnea events detection in neck photoplethysmography signals

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP).

A novel, simple, and accurate pulse oximetry indicator for screening adult obstructive sleep apnea

OBJECTIVE

The objective of the study was to develop a multiparametric oximetry indicator (IMp-SpO2) to diagnose obstructive sleep apnea in adults.

MATERIAL AND METHOD

This was an observational, retrospective study of diagnostic accuracy. We included adults who had had a diagnostic polysomnography with few artifacts and a total sleep time of at least 180 min in the sleep laboratory. Obstructive sleep apnea (OSA) was defined as an apnea-hypopnea index (AHI) ≥ 5. The database was randomly divided into an experimental (Exp-G) and validation (Val-G) group. The program calculated several parameters of oxygen saturation variability (Par-VarSpO2): (a) oxygen desaturation index (ODI ≥ 3, 4%) and (b) 90, 95, and 97.5 percentiles of both the number of oxygen desaturations ≥ 3 and 4% (P90-97.5 OD3/4 W5-60) and SpO2 standard deviations in moving windows from 5 to 60 min (P90-P97.5 SDSpO2 W5-10). Area under the ROC curve (AUC-ROC), sensitivity, specificity, positive/negative likelihood ratios, and accuracy were calculated.

RESULTS

Of 1141 adults included in the study, experimental (571) and validation group (570) were similar (women 47% vs 45%, BMI 27.5 kg/m² vs 27.2 kg/m², and AHI 11.7 vs 12, p NS). The IMp-SpO2 developed in the experimental group consisted of a combination of 10 parameters of oxygen saturation variability. The presence of at least one IMp-SpO2 variable had a high diagnostic performance for OSA (sensitivity/specificity/accuracy: Exp-G: 92.8/94/93.2%; Val-G: 93/95.2/93.7%). The IMp-SpO2 AUC-ROC was higher (Exp-G 0.934, Val-G 0.941) than most of the Par-VarSpO2 (0.898-0.929, p < 0.05).

CONCLUSION

The IMp-SpO2 showed a > 90% accuracy for OSA diagnosis in adults.

Reliability of STOP-Bang questionnaire and pulse oximetry as predictors of OSA - a retrospective study

OBJECTIVE

To verify the reliability of a STOP-Bang questionnaire and objective blood oxygen concentration (SpO2) estimation by pulse oximetry as an indicator of patients' vulnerability to OSA, by correlating data of these two tests with that of the "gold standard" all-night polysomnography.

METHODS

STOP-Bang score and pulse oximetry value (SpO2) for each patient were tabulated against the total sleep AHI score (obtained from subsequent all-night polysomnography) and analyzed to evaluate the diagnostic accuracy of the STOP-Bang questionnaire and pulse oximetry.

RESULTS

With sensitivity and specificity scores of 91.2% and 88.6%, respectively, positive predictive value 90.5%, negative predictive value 40.2%, the twin diagnostic test (STOP-Bang and pulse oximetry) was found to be highly congruent with the polysomnography (PSG), achieving a diagnostic accuracy of 85%.

CONCLUSION

Dental chairside screening by STOP-Bang questionnaire and pulse oximetry would be a good option, especially where logistic and economic constraints impede all-night polysomnography.

Association of longitudinal changes in quality of life with comorbidities and exacerbations in patients with severe asthma

BACKGROUND

Quality of life (QoL) assessment is important in the management of severe asthma, and comorbidities and/or exacerbations may affect longitudinal QoL. However, there are few reports on the longitudinal assessment of QoL in patients with asthma over multiple years and its related factors. This study aimed to clarify the relationship of longitudinal changes in QoL with comorbidities and/or exacerbations during a prolonged observation period in patients with severe asthma.

METHODS

A total of 105 subjects who participated in the Hokkaido-based Investigative Cohort Analysis for Refractory Asthma (Hi-CARAT) with a six-year follow-up were analyzed. QoL was assessed annually, using the Standardized Asthma Quality of Life Questionnaire, and the subjects were divided into three groups: (1) persistently good QoL, (2) persistently poor QoL, and (3) fluctuating QoL. Assessed comorbidities comprised depression, gastroesophageal reflux disease, and excessive daytime sleepiness (EDS), a key symptom of obstructive sleep apnea.

RESULTS

Of 105 subjects with severe asthma, 53 (50%) were classified in the persistently good QoL group, 10 (10%) in the persistently poor QoL group, and 42 (40%) in the fluctuating QoL group. The persistently poor QoL group was associated with shorter time to hospitalization due to exacerbation and the presence of multiple comorbidities. In addition, the presence of EDS was an independent contributor to the fluctuating QoL group compared to the persistently good QoL group.

CONCLUSIONS

The presence of multiple comorbidities and hospitalization due to exacerbation contribute to longitudinal changes in QoL in patients with severe asthma.

Waist-To-Hip Ratio Predicts Abnormal Overnight Oximetry in Men Independent of Body Mass Index

Background: Ambulatory overnight oximetry (OXI) has emerged as a cost-effective initial test for sleep disordered breathing. Obesity is closely associated with obstructive sleep apnea (OSA); however, whether body mass index (BMI) or waist-to-hip ratio (WHR) predicts abnormal overnight OXI remains unknown. Methods: We performed a retrospective cross-sectional study of 393 men seen in the Executive Health Program at Mayo Clinic in Rochester, Minnesota who underwent ambulatory overnight OXI ordered by preventive medicine physicians between January 1, 2004 through December 31, 2010. We compared participant/spouse-reported symptoms (sleepiness, snoring), physician indications for OXI (obesity, fatigue), Epworth Sleepiness Scale scores, anthropomorphic measurements (WHR, BMI), and comorbid medical conditions (hypertension, diabetes) with OXI results. Results: 295 of the 393 men who completed OXI had abnormal results. During multivariate analysis, the strongest independent predictor of abnormal OXI for men was WHR (≥1.0, OR = 5.59) followed by BMI (≥30.0 kg/m2, OR = 2.75), age (≥55 yrs, OR = 2.06), and the presence of snoring (OR = 1.91, P < 0.05 for all). A strong association was observed between WHR and abnormal OXI in obese (BMI ≥ 30.0 kg/m2, OR = 6.28) and non-obese (BMI < 29.9 kg/m2, OR = 6.42, P < 0.01 for both) men. Furthermore, 88 men with abnormal OXI underwent polysomnography with 91% being subsequently diagnosed with OSA. Conclusions: In ambulatory, predominantly middle-aged men undergoing preventive services evaluation many physician indications for OXI were not predictors of abnormal results; however, WHR strongly predicted abnormal OXI in obese and non-obese men. As such, we suggest middle-aged men who snore and have a WHR ≥1.0 should be directly referred to a sleep clinic for polysomnography.

Getting More from the Sleep Recording

Sleep studies have typically followed criteria established many decades ago, but emerging technologies allow signal analyses that go far beyond the scoring rules for manual analysis of sleep recordings. These technologies may apply to the analysis of signals obtained in standard polysomnography in addition to novel signals more recently developed that provide both direct and indirect measures of sleep and breathing in the ambulatory setting. Automated analysis of signals such as electroencephalogram and oxygen saturation, in addition to heart rate and rhythm, provides a wealth of additional information on sleep and breathing disturbances and their potential for comorbidity.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Obstructive sleep apnoea/hypopnoea syndrome: relationship with obesity and management in obese patients

Obstructive sleep apnoea/hypopnoea syndrome (OSAHS) is a disease characterised by upper airway obstruction during sleep, quite frequent in the general population, even if underestimated. Snoring, sleep apnoea and diurnal hypersomnia are common in these patients. Central obesity plays a key role: it reduces the size and changes the conformation of the upper airways, besides preventing lung expansion, with consequent reduction of lung volumes. Furthermore, obese people are also resistant to leptin, which physiologically stimulates ventilation; as a result, this causes scarce awakening during apnoea. OSAHS diagnosis is based on the combination of clinical parameters, such as apnoea/hypopnoea index (AHI), medical history, physical examination and Mallampati score. The first objective reference method to identify OSAHS is polysomnography followed by sleep endoscopy. Therapy provides in the first instance reduction of body weight, followed by continuous positive airway pressure (CPAP), which still remains the treatment of choice in most patients, mandibular advancement devices (MAD) and finally otolaryngology or maxillofacial surgery. Among surgical techniques, central is barbed reposition pharyngoplasty (BRP), used in the field of multilevel surgery.

A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea

This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen's kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.

Associations between sleep apnea and advanced brain aging in a large-scale population study

Advanced brain aging is commonly regarded as a risk factor for neurodegenerative diseases, for example, Alzheimer's dementia, and it was suggested that sleep disorders such as obstructive sleep apnea (OSA) are significantly contributing factors to these neurodegenerative processes. To determine the association between OSA and advanced brain aging, we investigated the specific effect of two indices quantifying OSA, namely the apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI), on brain age, a score quantifying age-related brain patterns in 169 brain regions, using magnetic resonance imaging and overnight polysomnography data from 690 participants (48.8% women, mean age 52.5 ± 13.4 years) of the Study of Health in Pomerania. We additionally investigated the mediating effect of subclinical inflammation parameters on these associations via a causal mediation analysis. AHI and ODI were both positively associated with brain age (AHI std. effect [95% CI]: 0.07 [0.03; 0.12], p-value: 0.002; ODI std. effect [95% CI]: 0.09 [0.04; 0.13], p-value: < 0.0003). The effects remained stable in the presence of various confounders such as diabetes and were partially mediated by the white blood cell count, indicating a subclinical inflammation process. Our results reveal an association between OSA and brain age, indicating subtle but widespread age-related changes in regional brain structures, in one of the largest general population studies to date, warranting further examination of OSA in the prevention of neurodegenerative diseases.

Effects of nocturnal complete denture usage on cardiorespiratory parameters: A pilot study

STATEMENT OF PROBLEM

Sleeping without conventional complete dentures (CCDs) has been stated by some to induce negative effects on the cardiorespiratory functions of edentulous patients with obstructive sleep apnea (OSA), although others have reported the exact opposite. Therefore, a consensus on nocturnal CCD usage is lacking.

PURPOSE

The purpose of this clinical study was to assess the effects of nocturnal denture usage on cardiorespiratory stability by using pulse oximetry (PO).

MATERIALS AND METHODS

Thirty CCD wearers were enrolled in the study. The first nocturnal pulse oximetry (FNPO) recordings were made on 3 different nights while the participants were sleeping without dentures (WOD). Oxygen desaturation index (ODI) and other PO parameters of the participants, including total respiratory event (TRE), basal SpO₂ (BSpO₂), time≤88 (T88), average low SpO₂ (ALSpO₂), total pulse event (TPE), average pulse rate (APR), and heart rate variability index (HRVI), were processed and the obtained data were recorded as WOD condition values. According to the ODI scores, the OSA status of the participants was grouped as normal (ODI<5), mild (5<ODI<15), moderate (15<ODI<30), or severe (ODI>30). Complete dentures were fabricated by an experienced prosthodontist and a dental laboratory technician by following conventional procedures. At the end of the first month of the follow-up period, the second nocturnal PO recordings (SNPO) were made on 3 different nights while the participants slept wearing dentures (WID), and the data obtained were recorded as WID condition values. The comparison of mean PO values obtained from WOD and WID were analyzed with the Wilcoxon signed- rank test (α=.05).

RESULTS

Significant differences were found between WOD and WID values in terms of TRE (P=.01), ODI (P=.001), ALSpO2 (P=.006), TPE (P=.001), and HRVI (P=.001) parameters. The significance of the improvements in the WID condition increased with the severity of OSA.

CONCLUSIONS

Improvements were observed in substantial cardiorespiratory parameters such as the ODI and HRVI of the participants wearing dentures nocturnally.

Furniture-Integrated Respiration Sensors by Notched Transmission Lines

Non-invasive respiration sensors integrated into furniture can be invisible to the user and greatly enhance comfort and convenience to facilitate many applications. Current sensors often require user cooperation or fitting, which discourages frequent usage. We present a new respiration sensor integrated into a bed or a chair by modifying a radio-frequency (RF) coaxial cable structure with a designed notch. The lung motion is coupled to the electromagnetic leakage at the notch through near-field coherent sensing (NCS). The sensors, covered with fabrics and positioned under the abdomen and thorax, can capture the respiratory waveforms and derive the breath rate. The heart rate can also be evaluated in the same setup with proper filtering. The sensor design can tolerate large position variation to accommodate user uncertainties. Various voluntary exercises of normal, deep, fast, held and blocked breathing were measured under different postures of supine, recumbent and sitting by the carrier frequency range between 900MHz and 2.4GHz. The breath rate from 10 participants compare well with the synchronous commercial chest-belt sensors in all breathing routines.

Increased heart rate with sleep disordered breathing in hypertrophic cardiomyopathy

BACKGROUND

Current guidelines recommend medications with rate control properties for symptomatic patients with hypertrophic cardiomyopathy (HCM) based on the rationale that lowering heart rate (HR) improves their symptoms. Whether sleep disordered breathing (SDB) is associated with increased HR in HCM patients is not known.

METHOD

We diagnosed uncontrolled SDB (oxygen desaturation index ≥5) in consecutive echocardiographically confirmed HCM patients seen at Mayo Clinic, Rochester, and analyzed their HR as recorded by a 24-h Holter monitor. We compared mean, minimum, maximum HR between those with vs without SDB. In a pilot subanalysis of HCM patients with SDB who also underwent subsequent diagnostic polysomnography (PSG), we analyzed RR interval changes coinciding with obstructive sleep apnea and hypopnea episodes.

RESULTS

Of the 230 HCM patients included in this study (age 54 ± 16 years; 138 male; LVOT pressure gradient at rest 45 ± 39 mmHg), 115 (50%) patients had SDB. HCM patients with SDB were recorded to have higher mean HR (71 vs. 67 bpm; p = .002, adjusted p = .001), and this difference was most pronounced during night hours of 10 PM to 5 AM (61 vs. 67 bpm; p < .001). In the pilot analysis of the available PSG data, the release of obstructive sleep apneas and hypopneas coincided with fluctuation of HR.

CONCLUSIONS

SDB is independently associated with higher mean HR in patients with HCM, and this difference is most significant during sleep. Treatment of SDB, which is readily available, should be tested as a complementary modality to the currently recommended pharmacotherapy aimed at lowering HR in patients with symptomatic HCM.

Prevalence of sleep disorders in chronic obstructive pulmonary disease and utility of global sleep assessment questionnaire: An observational case-control study

INTRODUCTION:

Although sleep disorders in chronic obstructive pulmonary disease (COPD) are common, no study has comprehensively evaluated sleep disorders in COPD, and there are no screening tools available for COPD patients. Global sleep assessment questionnaire (GSAQ) is one of the best screening tools for the general population.

AIMS AND OBJECTIVES:

The aims and objectives of the study were to find (i) the prevalence of sleep disorders in COPD based on GSAQ score; (ii) if the GSAQ score in COPD is high compared to healthy adult population; (iii) the prevalence of insomnia, obstructive sleep apnea (OSA), restless leg syndrome (RLS), and depression in COPD patients; and (iv) the sensitivity and specificity of GSAQ.

METHODS:

This was a prospective case–control study where GSAQ is administered to 100 stable COPD and 50 healthy individuals. The presence of sleep disorder in COPD was further confirmed based on the diagnostic criteria and polysomnography.

RESULTS:

GSAQ was positive in 68% of the COPD patients compared to 16% of the matched healthy adults (P < 0.001). The sleep disturbance was confirmed in 66 COPD patients. Insomnia, OSA, RLS, depression/anxiety, and overlap of two or more disorder were seen in 39, 13, 30, 22, and 31 patients, respectively. The overall sensitivity/specificity of GSAQ for sleep disturbances in COPD was 90.9%/70.58%, respectively. The sensitivity/specificity of GSAQ for insomnia, OSA, RLS, and anxiety/depression was 87/75%, 77/67%, 90/80%, and 91/65%, respectively.

CONCLUSION:

Sleep disorders in COPD patients are significantly high. GSAQ is a good screening tool for detecting the presence of disturbed sleep in COPD. Overlap of two or more sleep disorders is common in COPD.

Usefulness of Home Overnight Pulse Oximetry in Patients with Suspected Sleep-Disordered Breathing

Methods

Prospective study conducted in a university hospital. Subjects with a clinical suspicion of SAHS were included. All of them underwent home polygraphy and oximetry on the same night. A correlation was made between the apnea-hypopnea index (AHI) and the oximetry variables. The variable with the highest diagnostic value was calculated using the area under the curve (AUC), and the best cut-off point for discriminating between patients with SAHS and severe SAHS was identified.

Results

One hundred and four subjects were included; 73 were men (70%); mean age was 52 ± 10.1 years; body mass index was 30 ± 4.1, and AHI = 29 ± 23.2/h. A correlation was observed between the AHI and oximetry variables, particularly ODI3 (r = 0.850; P < 0.001) and ODI4 (r = 0.912; P < 0.001). For an AHI ≥ 10/h, the ODI3 had an AUC = 0.941 (95% confidence interval (CI) = 0.899–0.982) and the ODI4, an AUC = 0.984 (95% CI = 0.964–1), with the ODI4 having the best cut-off point (5.4/h). Similarly, for an AHI ≥ 30/h, the ODI4 had an AUC = 0.922 (95% CI = 0.859–0.986), with the best cut-off point being 10.5/h.

Conclusion

Nocturnal oximetry is useful for diagnosing and evaluating the severity of SAHS. The ODI4 variable was most closely correlated with AHI for both diagnosis.

Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review

Obstructive sleep apnoea (OSA) is a growing and serious worldwide health problem with significant health and socioeconomic consequences. Current diagnostic testing strategies are limited by cost, access to resources and over reliance on one measure, namely the apnoea-hypopnoea frequency per hour (AHI). Recent evidence supports moving away from the AHI as the principle measure of OSA severity towards a more personalised approach to OSA diagnosis and treatment that includes phenotypic and biological traits. Novel advances in technology include the use of signals such as heart rate variability (HRV), oximetry and peripheral arterial tonometry (PAT) as alternative or additional measures. Ubiquitous use of smartphones and developments in wearable technology have also led to increased availability of applications and devices to facilitate home screening of at-risk populations, although current evidence indicates relatively poor accuracy in comparison with the traditional gold standard polysomnography (PSG). In this review, we evaluate the current strategies for diagnosing OSA in the context of their limitations, potential physiological targets as alternatives to AHI and the role of novel technology in OSA. We also evaluate the current evidence for using newer technologies in OSA diagnosis, the physiological targets such as smartphone applications and wearable technology. Future developments in OSA diagnosis and assessment will likely focus increasingly on systemic effects of sleep disordered breathing (SDB) such as changes in nocturnal oxygen and blood pressure (BP); and may also include other factors such as circulating biomarkers. These developments will likely require a re-evaluation of the diagnostic and grading criteria for clinically significant OSA.

Validation of Oximetry for Diagnosing Obstructive Sleep Apnea in a Clinical Setting

A large epidemiological study using oximetry to analyze obstructive sleep apnea (OSA) and metabolic comorbidities was performed in Japan; however, reliability and validity of oximetry in the Japanese population remains poorly understood. In this study, oximetry data from the epidemiological study were compared with data from clinically performed polysomnography (PSG) and out-of-center sleep testing (OCST) in epidemiological study participants who later attended our outpatient units. The oxygen desaturation index (ODI) from oximetry showed a moderate positive relationship (correlation coefficient r = 0.561, p < 0.001) with apnea/hypopnea data from PSG/OCST. The area under the receiver operating characteristic curve showed moderate accuracy of this method in the detection of moderate-to-severe or severe OSA. However, the optimal ODI thresholds to detect moderate-to-severe OSA and severe OSA were the same (ODI > 20.1). Oximetry may be a useful tool for screening moderate-to-severe or severe sleep apnea. However, it may be difficult to set an appropriate threshold to distinguish between moderate and severe sleep apnea by oximetry alone.

Nocturnal oxygen desaturation in the late third trimester of uncomplicated pregnancy for prediction of late-onset gestational hypertension

AIM

This study aimed to determine whether 1-night screening of oxygen desaturation in women with uncomplicated pregnancy about 1 month before the due date is useful to predict late-onset gestational hypertension (GH) after 37 weeks of gestation.

METHODS

We recruited 102 women with uncomplicated pregnancy between 34 and 36 weeks of gestation. These women then completed the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Berlin Questionnaire for obstructive sleep apnea, and recorded their oxygen saturation (SpO₂ ) and physical acceleration profiles during nocturnal sleep for 1 night at home using a portable pulse oximeter and actigraph, respectively. Thereafter, their blood pressure was monitored weekly until delivery.

RESULTS

Pulse oximetry data between 34 and 36 weeks of gestation revealed that three women had an oxygen desaturation index (ODI) ≥5.0 and seven had a minimum SpO₂ < 90%. During follow-up until delivery, two women with an ODI ≥5.0 and a minimum SaO₂ <90% developed GH at 37 weeks of gestation. Among clinical measures at recruitment, body mass index (BMI) and pulse oximetry measures appeared available for the prediction of GH. The positive predictive values (95% confidence intervals) of the criteria using these measures were 0.67 (0.26-0.67) for ODI ≥5.0, 0.29 (0.10-0.35) for minimum SpO₂ <90%, and 0.07 (0.02-0.07) for BMI ≥25.0.

CONCLUSION

For the prediction of late-onset GH after 37 weeks of gestation, pulse oximetry measures about 1 month before the due date are particularly useful because of their high positive predictive values.

Source-Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift

Pulse oximetry enables oxygen saturation estimation (SpO2) non-invasively in real time with few components and modest processing power. With the advent of affordable development kits dedicated to the monitoring of biosignals, capabilities once reserved to hospitals and high-end research laboratories are becoming accessible for rapid prototyping. While one may think that medical-grade equipment differs greatly in quality, surprisingly, we found that the performance requirements are not widely different from available consumer-grade components, especially regarding the photodetection module in pulse oximetry. This study investigates how the use of candidate light sources and photodetectors for the development of a custom SpO2 monitoring system can lead to inaccuracies when using the standard computational model for oxygen saturation without calibration. Following the optical characterization of selected light sources, we compare the extracted parameters to the key features in their respective datasheet. We then quantify the wavelength shift caused by spectral pairing of light sources in association with photodetectors. Finally, using the widely used approximation, we report the resulting absolute error in SpO2 estimation and show that it can lead up to 8% of the critical 90–100% saturation window.

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO₂) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO₂ raw data. CHAT-baseline dataset, composed of 453 SpO₂ recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO₂ signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.

A Wearable RF Sensor for Monitoring Respiratory Patterns

We present a non-invasive approach for continuous monitoring of respiration dynamics using a wearable radio-frequency (RF) sensor based on near-field coherent sensing. A continuous-wave RF signal at 1.8 GHz is generated by a software-defined radio, with both transmitter (Tx) and receiver (Rx) antennas placed close to the xiphoid process. The experimental prototype of the mobile sensor can modulate the internal organ motion in the near-field region of the Tx antenna and is then received by the nearby Rx antenna to be demodulated and sampled. Through peak detection, we have identified inhalation and exhalation peaks of each breath cycle to estimate the breath rate and the lung volume. The extracted respiratory parameters are compared with the conventional chest belts data for various simulated respiratory conditions including voluntary deep, fast-shallow and slow-shallow breathing. We also characterized simulated central sleep apneas, Cheyne-Stokes, Biot's, ataxic and coughing conditions. To accurately identify obstructive apnea, we presented a two-sensor approach that can capture paradoxical movement of thorax and abdomen. The on-line recognition of these respiratory patterns can be employed not only to continuously monitor patients with chronic respiratory disorders but also to provide real-time feedback for future therapeutic purposes.

Sensors Capabilities, Performance, and Use of Consumer Sleep Technology

Sleep is crucial for the proper functioning of bodily systems and for cognitive and emotional processing. Evidence indicates that sleep is vital for health, well-being, mood, and performance. Consumer sleep technologies (CSTs), such as multisensory wearable devices, have brought attention to sleep and there is growing interest in using CSTs in research and clinical applications. This article reviews how CSTs can process information about sleep, physiology, and environment. The growing number of sensors in wearable devices and the meaning of the data collected are reviewed. CSTs have the potential to provide opportunities to measure sleep and sleep-related physiology on a large scale.

Wearable Devices for Precision Medicine and Health State Monitoring

Wearable technologies will play an important role in advancing precision medicine by enabling measurement of clinically-relevant parameters describing an individual's health state. The lifestyle and fitness markets have provided the driving force for the development of a broad range of wearable technologies that can be adapted for use in healthcare. Here we review existing technologies currently used for measurement of the four primary vital signs: temperature, heart rate, respiration rate, and blood pressure, along with physical activity, sweat, and emotion. We review the relevant physiology that defines the measurement needs and evaluate the different methods of signal transduction and measurement modalities for the use of wearables in healthcare.

Diagnostic performance of nocturnal oximetry in the detection of obstructive sleep apnea syndrome: a Brazilian study

OBJECTIVE

Due to the increasing prevalence of obstructive sleep apnea (OSA), more practical diagnostic methods than polysomnography (PSG) have become necessary. This research aims to analyze the performance of nocturnal oximetry (NO) in the diagnosis of OSA.

METHODS

In this cross-sectional study, we analyzed 41 variables provided by the oximetry of all PSG performed by the LabSono of University Hospital Gaffrée and Guinle, a total of 83 exams. We evaluated the correlation coefficients (Spearman) between these data and the Apnea/Hypopnea Index (AHI) and then calculated the diagnostics performances, by the area under the curve (ROC) (AUC), of the best correlated variables and their respective cutoffs, in the identification of an AHI ≥ 15/h.

RESULTS

Virtually all oximetric data showed good correlations with AHI, except for some temporal data. We chose 5 of them and calculated their diagnostic performances. T < 90% shows AUC of 0.904 (0.835-0.972) and, at cutoff > 19 min, a sensitivity (Sens.) of 75.68% and specificity (Spec.) of 95.65%. DO3/10Total, AUC 0.936 (0.888-0.989), and at the cutoff > 51 has Sens. 97.3% and Spec. 76.09%. ODI3/10/h has AUC 0.932 (0.884-0.988), at the cutoff > 7/h, Sens. 97.3% and Spec. 78.26%. DO4/5Total AUC 0.932 (0.882-0.981), at the cutoff > 64 has Sens. 86.49% and Spec. 82.61% and ODI4/5/h has AUC 0.930 (0.880-0.981), the cutoff > 5.69/h Sens. 97.3% and Spec. 73.91%.

CONCLUSIONS

Our study concludes that NO is accurate in identifying AHI > 15/h, and provides reliable information on PSG replacement, which could make the diagnosis of OSA cheaper and more comfortable.

The uses of overnight pulse oximetry

Overnight pulse oximetry (OPO) has proven to be an effective and beneficial technique to determine the cardiorespiratory status of patients in both the inpatient and outpatient settings. It is a cheap, safe, reliable, simple, and accurate method of patient monitoring as compared to the expensive and labor-intensive method of multichannel polysomnography for detecting sleep-disordered breathing. It provides accurate information about patient's oxygenation status and also helps in monitoring the response to continuous positive airway pressure and in the surgical treatment of obstructive sleep apnea (OSA). Nocturnal hypoxemia portends a poor prognosis in patients of chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and neuromuscular diseases. OPO can help its early detection and management.

Observational study on the influence of averaging time on oximetry results in infants and children

AIM

Oximetry values are influenced by the averaging time (AT) used. We aimed to evaluate the effect of different ATs on number, duration, mean single event and total integral of desaturations in preterm infants and children to convert between parameters obtained with different ATs.

METHODS

In a prospective observational study, 49 children underwent sleep laboratory-based polysomnography and 15 preterm infants were studied in the intensive care unit. Their raw red-to-infrared-saturation-data were reprocessed using seven different ATs (3-16 seconds). Desaturation thresholds were <80% (infants) and <90% (children), conversion formulas and their median percentage errors were calculated.

RESULTS

We found a linear relationship between the logarithms of the ATs and those of the desaturation parameters, leading to a conversion formula with different exponents. Based on this relationship, the number of desaturations decreased from AT = 3s to AT = 16s by factor 0.28 (children) and 0.18 (infants); total oxygen saturation integral decreased by factor 0.72 (children) and 0.48 (infants). The desaturation duration increased by factor 1.89 (children) and 3.34 (infants).

CONCLUSION

The number and total integral decreased, but the duration and mean single event integral increased with increasing AT. These changes were stronger in infants. Conversion formulas may facilitate comparisons between studies using different averaging times.

Individuals With Scleroderma May Have Increased Risk of Sleep-Disordered Breathing

STUDY OBJECTIVES

Scleroderma is associated with abnormal skin thickening, interstitial lung disease, pulmonary hypertension, and abnormalities of the upper airway. These changes can cause cardiopulmonary complications, potentially including sleep-disordered breathing. The objective of this study is to examine the risk of sleep-disordered breathing in patients with scleroderma.

METHODS

We retrospectively identified patients with documented scleroderma. We abstracted data from their electronic health records, including findings from antibody tests, serial pulmonary function tests, transthoracic echocardiography, high-resolution computed tomography, and overnight forehead oximetry.

RESULTS

We identified 171 patients with scleroderma. Mean age at the time of initial consult was 56.5 years (range, 18-96 years), and 150 (86.7%) were women. Scleroderma was categorized as limited disease for 108 (62.4%), diffuse disease for 59 (34.1%), and mixed connective tissue disease for 6 (3.5%). Fifty-four patients (31.2%) had abnormal overnight forehead oximetry results, defined as an oxygen desaturation index greater than 5 or a baseline mean arterial oxygen saturation level less than 90%.

CONCLUSIONS

Cardiopulmonary complications are common in patients with scleroderma, one of which may be sleep-disordered breathing. In our cohort, approximately one-third of individuals with scleroderma had evidence of sleep-disordered breathing. Moreover, the rate of sleep-disordered breathing in our population of scleroderma patients was twice the rate of pulmonary hypertension and was approximately the same as the rate of interstitial lung disease. Future prospective studies are needed to further assess the role of sleep-disordered breathing in scleroderma clinical outcomes.

Sleep-wake stages classification using heart rate signals from pulse oximetry

The most important index of obstructive sleep apnea/hypopnea syndrome (OSAHS) is the apnea/hyponea index (AHI). The AHI is the number of apnea/hypopnea events per hour of sleep. Algorithms for the screening of OSAHS from pulse oximetry estimate an approximation to AHI counting the desaturation events without consider the sleep stage of the patient. This paper presents an automatic system to determine if a patient is awake or asleep using heart rate (HR) signals provided by pulse oximetry. In this study, 70 features are estimated using entropy and complexity measures, frequency domain and time-scale domain methods, and classical statistics. The dimension of feature space is reduced from 70 to 40 using three different schemes based on forward feature selection with support vector machine and feature importance with random forest. The algorithms were designed, trained and tested with 5000 patients from the Sleep Heart Health Study database. In the test stage, 10-fold cross validation method was applied obtaining performances up to 85.2% accuracy, 88.3% specificity, 79.0% sensitivity, 67.0% positive predictive value, and 91.3% negative predictive value. The results are encouraging, showing the possibility of using HR signals obtained from the same oximeter to determine the sleep stage of the patient, and thus potentially improving the estimation of AHI based on only pulse oximetry.

Comparison of nocturnal pulse oximetry with polysomnography in children with sleep disordered breathing

PURPOSE

Polysomnography (PSG) is the gold standard in the diagnosis of obstructive sleep apnea (OSA). However, due to high cost and limited availability, this is difficult to access and often delayed. To evaluate the reliability of overnight oximetry as a screening tool for OSA diagnosis.

METHOD

All children suspected of OSA who underwent an overnight oximetry and subsequent PSG between January 2014 and April 2016 were studied retrospectively. The "McGill oximetry scoring" was compared with OSA diagnosis as per the American Association of Sleep Medicine.

RESULTS

A total of 110 patients had both oximetry and PSG. Sixty-one children (56%) had normal oximetry, whereas 30 (27%) had McGill grade 2 and 19 (17%) had McGill 3 and 4. Sixty-four percent (64%) of children with normal oximetry had a normal PSG. Seventy percent (70%) of children with McGill 2 had either a normal or mild OSA on PSG. All the children with McGill 3 and 4 had moderate/severe OSA by PSG. The overall sensitivity and specificity of oximetry in identifying OSA were 63% and 78%, respectively, and the positive and negative predictive values (PPV and NPV) were 78% and 64%, respectively. The sensitivity and specificity of McGill 3 and 4 in diagnosing moderate/severe OSA on PSG were 59% and 100%, respectively, and the PPV and NPV were 100% and 78%, respectively.

CONCLUSION

Overnight oximetry provides satisfactory diagnostic performance in detecting moderate and severe OSA; however, a normal or McGill 2 score does not rule out OSA and a PSG is required for diagnosis.

The STOP-Bang Questionnaire as a Screening Tool for Obstructive Sleep Apnea in Pregnancy

STUDY OBJECTIVES

We examined the validity of the STOP-Bang questionnaire and a modified STOP-Bang questionnaire to screen for obstructive sleep apnea (OSA) in women with obesity during the second trimester of pregnancy.

METHODS

Ninety-nine pregnant women age 18 years or older with body mass index ≥ 40 kg/m² completed the STOP-Bang questionnaire during their second trimester. The number of oxygen desaturation events (≥ 4% from baseline) was measured using overnight pulse oximetry, with OSA defined as ≥ 5 events/h. A Modified STOP-Bang score was derived by replacing the "Tired" item with Epworth Sleepiness Scale score ≥ 10. Seven candidate models were compared using information theoretic criteria: STOP-Bang, Modified STOP-Bang, and individual STOP-Bang items (Snore, Tired, Observed to stop breathing, high blood Pressure and Neck circumference). We used penalized logistic regression and negative binomial regression to derive predicted probabilities of having OSA and the predicted total event counts.

RESULTS

The predicted probability of meeting oximetry criteria for OSA increased with higher STOP-Bang scores, from < 10% for a score < 3 to 68% with a score of 6. The total number of disordered breathing events was 1.26 (95% confidence interval 1.06 to 1.50) times greater for a 1-unit increase in STOP-Bang. Of the candidate models, the best relative fit was the Snore item followed by STOP-Bang score (essentially equivalent). The predicted probability of having OSA was 5.0% for no snoring and 26.4% for snoring.

CONCLUSIONS

STOP-Bang has been shown to be a useful screening tool for OSA in pregnant women with obesity; however, the snoring question alone might be a simpler, effective predictor.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Title: STOPBANG As A Screening Tool for Obstructive Sleep Apnoea in Pregnancy; URL: https://clinicaltrials.gov/ct2/show/NCT02542488; Identifier: NCT02542488.

Overnight pulse oximetry for obstructive sleep apnea screening among patients with snoring in primary care setting: Clinical case report

This clinical case report illustrated a typical patient presented with snoring in the primary care. He was screened positive for obstructive sleep apnea (OSA) by overnight pulse oximetry and then referred to respiratory specialist care. With early confirmation and treatment of OSA, symptoms and comorbidity associated with OSA can be improved and risk for cardiovascular complication can be prevented. Among 264 cohort patients presented primarily with snoring in one primary care clinic of Hong Kong, 175 patients (66.2%) were screened to have OSA. About 56.0% (98/175), 26.2% (46/175), and 17.8% (31/175) were classified as mild, moderate, and severe OSA, respectively. In view of high prevalence of OSA among snorers, and OSA-related exacerbation in comorbid conditions and increased risk of cardiovascular complication, there is a need to stimulate OSA screening among snoring patients in primary care.

Hypothalamic-Pituitary-Gonadal Activity in Paradoxical and Psychophysiological Insomnia

Background:

Although insomnia is a sex-dimorphic disorder, there is limited knowledge about the association between sex hormones and insomnia. In the present study, the level of hypothalamus–pituitary–gonadal (HPG) axis activity was investigated in patients with insomnia by measuring serum levels of luteinizing hormone (LH), follicle stimulating hormone (FSH), 17α-Hydroxyprogesterone, testosterone, progesterone, estradiol, dehydroepiandrosterone sulfate, and sex hormone-binding globulin.

Methods:

Numbers of 19 patients; including 13 females (68.40%) with paradox insomnia (32–53 years; 43.20 ± 6.40) and 17 patients; including 8 females (47.05%) with psychophysiological insomnia (14–62 years; 38.40 ± 16.30) were recruited. Seventeen aged-matched normal sleeper consisted of 13 males (26–59 years; 40.70 ± 10) consisted of 13 males (76.50%) were also recruited as control group. Insomnia was diagnosed by a sleep clinician according to the International Classification of Sleep Disorders-Second Edition criteria and an overnight polysomnography (PSG). A volume of 5 ml of venous blood samples were collected, prepared, and stored at 8 AM under standard condition. Serum levels of hormones were measured using enzyme-linked immunosorbent assay kits. Data were analyzed by Chi-square and ANCOVA. The associations between PSG and biochemical parameters were evaluated using multiple linear regression analysis.

Results:

There were no significant differences in all biochemical analyses between two insomnia subgroups (paradoxical and psychophysiological insomnia) and normal sleepers. Testosterone was positively related to maximum pulse transit time (PTT). Moreover, both LH and FSH were positively related to wake index and diastolic blood pressure.

Conclusion:

Although there were no significant differences in all HPG's hormones between groups, both LH and FSH were associated with wake index and diastolic blood pressure. Moreover, testosterone was positively related to PTT.