Pediatric pulse oximetry-based OSA screening at different thresholds of the apnea-hypopnea index with an expression of uncertainty for inconclusive classifications

Pulse oximetry in pediatric care: Balancing advantages and limitations

BACKGROUND

Pulse oximetry has become a cornerstone technology in healthcare, providing non-invasive monitoring of oxygen saturation levels and pulse rate. Despite its widespread use, the technology has inherent limitations and challenges that must be addressed to ensure accurate and reliable patient care.

AIM

To comprehensively evaluate the advantages, limitations, and challenges of pulse oximetry in clinical practice, as well as to propose recommendations for optimizing its use.

METHODS

A systematic literature review was conducted to identify studies related to pulse oximetry and its applications in various clinical settings. Relevant articles were selected based on predefined inclusion and exclusion criteria, and data were synthesized to provide a comprehensive overview of the topic.

RESULTS

Pulse oximetry offers numerous advantages, including non-invasiveness, real-time feedback, portability, and cost-effectiveness. However, several limitations and challenges were identified, including motion artifacts, poor peripheral perfusion, ambient light interference, and patient-specific factors such as skin pigmentation and hemoglobin variants. Recommendations for optimizing pulse oximetry use include technological advancements, education and training initiatives, quality assurance protocols, and interdisciplinary collaboration.

CONCLUSION

Pulse oximetry is crucial in modern healthcare, offering invaluable insights into patients’ oxygenation status. Despite its limitations, pulse oximetry remains an indispensable tool for monitoring patients in diverse clinical settings. By implementing the recommendations outlined in this review, healthcare providers can enhance the effectiveness, accessibility, and safety of pulse oximetry monitoring, ultimately improving patient outcomes and quality of care.

Diagnostic accuracy of portable sleep monitors in pediatric sleep apnea: A systematic review

In recent years, a plethora of new type III and IV portable sleep monitors (PSM) have been developed, although evidence regarding their diagnostic accuracy for use in children remains heterogeneous. This study systematically reviews the literature addressing the diagnostic accuracies of type III and IV PSM for pediatric sleep apnea. Publications indexed in Medline, Embase, or Web of Science were reviewed using the PRISMA framework. Of 1054 studies, 62 fulfilled the inclusion criteria. Of the studies evaluating oximetry-based type IV PSM, one (6.25 %) demonstrated a balanced set of high (≥80 %) sensitivities and specificities for the diagnosis of any pediatric sleep apnea, while five studies (27.8 %) showed similar accuracies for moderate-to-severe sleep apnea. For non-oximetry-based type IV PSM, two studies (40 %) reported a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. Type III PSM repeatedly demonstrated higher diagnostic accuracies, with six studies (66.7 %) reporting a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. This review highlights the potential of type III PSM to detect moderate-to-severe pediatric sleep apnea, although current evidence is limited to support the stand-alone use of type IV PSM for the diagnosis of sleep apnea in most children.

Screening for obstructive sleep apnea (OSA) in children and adolescents with obesity: A scoping review of national and international pediatric obesity and pediatric OSA management guidelines

Obstructive sleep apnea (OSA) is a prevalent complication that affects up to 60% of children and adolescents with obesity. It is associated with poorer cardiometabolic outcomes and neurocognitive deficits. Appropriate screening and intervention for OSA are crucial in the management of children with obesity. We performed a scoping review of international and national pediatric obesity (n = 30) and pediatric OSA (n = 10) management guidelines to evaluate the recommendations on OSA screening in pediatric obesity. Sixteen (53%) of the pediatric obesity guidelines had incorporated OSA screening to varying extents, with no consistent recommendations on when and how to screen for OSA, and subsequent management of OSA in children with obesity. We provide our recommendations that are based on the strength and certainty of evidence presented. These include a clinical-based screening for OSA in all children with body mass index (BMI) ≥ 85th percentile or those with rapid BMI gain (upward crossing of 2 BMI percentiles) and the use of overnight polysomnography to confirm the diagnosis of OSA in those with high clinical suspicion. We discuss further management of OSA unique to children with obesity. An appropriate screening strategy for OSA would facilitate timely intervention that has been shown to improve cardiometabolic and neurocognitive outcomes.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Objective

The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.

Patients and methods

This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.

Results

Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.

Conclusions

This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea

Diagnosis of obstructive sleep apnea (OSA) in children with sleep-disordered breathing (SDB) requires hospital-based, overnight level I polysomnography (PSG). Obtaining a level I PSG can be challenging for children and their caregivers due to the costs, barriers to access, and associated discomfort. Less burdensome methods that approximate pediatric PSG data are needed. The goal of this review is to evaluate and discuss alternatives for evaluating pediatric SDB. To date, wearable devices, single-channel recordings, and home-based PSG have not been validated as suitable replacements for PSG. However, they may play a role in risk stratification or as screening tools for pediatric OSA. Further studies are needed to determine if the combined use of these metrics could predict OSA.

Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature

PURPOSE

Obstructive sleep apnea (OSA) is a serious type of obstructive sleep-disordered breathing (SDB) that can cause a series of adverse effects on children's cardiovascular, growth, cognition, etc. The gold standard for diagnosis is polysomnography (PGS), which is used to assess the prevalence of OSA by obtaining the apnea-hypopnea index (AHI), but this diagnosis method is expensive and needs to be performed in a specialized laboratory, making it difficult to be of benefit to children with suspected OSA on a large scale. Our goal was to use a machine learning method to identify children with OSA of varying severity using data on children's nighttime heart rate and blood oxygen data.

METHODS

This study included 3139 children who received diagnostic PSG with suspected OSA. Age, sex, BMI, 3 % oxygen depletion index (ODI), average nighttime heart rate and fastest heart rate were used as predictive features. Data sets were established with AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10 as the diagnostic criteria for mild, moderate and severe OSA, and the samples of each data set were randomly divided into a training set and a test set at a ratio of 8:2. An OSA diagnostic model was established based on the XGBoost algorithm, and the ability of the machine learning model to diagnose OSA children with different severities was evaluated through different classification ability evaluation indicators. As a comparison, traditional classifier Logistic Regression was used to perform the same diagnostic task. The SHAP algorithm was used to evaluate the role of these features in the classification task.

RESULTS

We established a diagnostic model of OSA in children based on the XGBoost algorithm. On the test set, the AUCs of the model for diagnosing mild, moderate, and severe OSA were 0.95, 0.88, and 0.88, respectively, and the classification accuracy was 90.45 %, 85.67 %, and 89.81 %, respectively, perform better than Logistic Regression classifiers. ODI is the most important feature in all classification tasks, and a higher fastest heart rate and ODI make the model tend to classify samples as positive. A high BMI value caused the model to tend to classify samples as positive in the mild and moderate classification tasks and as negative in the classification task with severe OSA.

CONCLUSION

Using heart rate and blood oxygen data as the main features, a machine learning diagnostic model based on the XGBoost algorithm can accurately identify children with OSA at different severities. This diagnostic modality reduces the number of signals and the complexity of the diagnostic process compared to PSG, which could benefit children with suspected OSA who do not have the opportunity to receive a diagnostic PSG and provide a diagnostic priority reference for children awaiting a diagnostic PSG.

Development and validation of moderate to severe obstructive sleep apnea screening test (ColTon) in a pediatric population

OBJECTIVE

Development and validation of a machine learning algorithm to predict moderate to severe obstructive sleep apnea syndrome (OSAS) in otherwise healthy children.

DESIGN

Multivariable logistic regression and cforest algorithm of a large cross-sectional data set of children with sleep-disordered breathing.

SETTING

An university pediatric sleep centre.

PARTICIPANTS

Children underwent clinical examination, acoustic rhinometry and pharyngometry, and surveying through parental sleep questionnaires, allowing the recording of 14 predictors that have been associated with OSAS. The dataset was nonrandomly split into a training (development) versus test (external validation) set (2:1 ratio) based on the time of the polysomnography. We followed the TRIPOD checklist.

RESULTS

We included 336 children in the analysis: 220 in the training set (median age [25th-75th percentile]: 10.6 years [7.4; 13.5], z-score of BMI: 1.96 [0.73; 2.50], 89 girls) and 116 in the test set (10.3 years [7.8; 13.0], z-score of BMI: 1.89 [0.61; 2.46], 51 girls). The prevalence of moderate to severe OSAS was 106/336 (32%). A machine learning algorithm using the cforest with pharyngeal collapsibility (pharyngeal volume reduction from sitting to supine position measured by pharyngometry) and tonsillar hypertrophy (Brodsky scale), constituting the ColTon index, as predictors yielded an area under the curve of 0.89, 95% confidence interval [0.85-0.93]. The ColTon index had an accuracy of 76%, sensitivity of 63%, specificity of 81%, negative predictive value of 84%, and positive predictive value of 59% on the validation set.

CONCLUSION

A cforest classifier allows valid predictions for moderate to severe OSAS in mostly obese, otherwise healthy children.

Nocturnal oximetry parameters as predictors of sleep apnea severity in resource-limited settings

Nocturnal oximetry is an alternative modality for evaluating obstructive sleep apnea syndrome (OSAS) severity when polysomnography is not available. The Oxygen Desaturation (≥3%) Index (ODI3) and McGill Oximetry Score (MOS) are used as predictors of moderate-to-severe OSAS (apnea-hypopnea index-AHI >5 episodes/h), an indication for adenotonsillectomy. We hypothesised that ODI3 is a better predictive parameter for AHI >5 episodes/h than the MOS. All polysomnograms performed in otherwise healthy, snoring children with tonsillar hypertrophy in a tertiary hospital (November 2014 to May 2019) were analysed. The ODI3 and MOS were derived from the oximetry channel of each polysomnogram. Logistic regression was applied to assess associations of ODI3 or MOS (predictors) with an AHI >5 episodes/h (primary outcome). Receiver operating characteristic (ROC) curves and areas under ROC curves were used to compare the ODI3 and MOS as predictors of moderate-to-severe OSAS. The optimal cut-off value for each oximetry parameter was determined using Youden's index. Polysomnograms of 112 children (median [interquartile range] age 6.1 [3.9-9.1] years; 35.7% overweight) were analysed. Moderate-to-severe OSAS prevalence was 49.1%. The ODI3 and MOS were significant predictors of moderate-to-severe OSAS after adjustment for overweight, sex, and age (odds ratio [OR] 1.34, 95% confidence interval [CI] 1.19-1.51); and OR 4.10, 95% CI 2.06-8.15, respectively; p < 0.001 for both). Area under the ROC curve was higher for the ODI3 than for MOS (0.903 [95% CI 0.842-0.964] versus 0.745 [95% CI 0.668-0.821]; p < 0.001). Optimal cut-off values for the ODI3 and MOS were ≥4.3 episodes/h and ≥2, respectively. The ODI3 emerges as preferable or at least a complementary oximetry parameter to MOS for detecting moderate-to-severe OSAS in snoring children when polysomnography is not available.

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis

BACKGROUND

Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice.

OBJECTIVE

To assess the reliability of machine-learning-based methods to detect pediatric OSA.

DATA SOURCES

Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references.

ELIGIBILITY CRITERIA

Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard.

APPRAISAL AND SYNTHESIS METHODS

Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I ² ) was evaluated, and publication bias was corrected (trim and fill).

RESULTS

Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.

Use of facial stereophotogrammetry as a screening tool for pediatric obstructive sleep apnea by dental specialists

STUDY OBJECTIVES

To evaluate facial 3-dimensional (3D) stereophotogrammetry's effectiveness as a screening tool for pediatric obstructive sleep apnea (OSA) when used by dental specialists.

METHODS

One hundred forty-four participants aged 2-17 years, including children fully diagnosed with pediatric OSA through nocturnal polysomnography or at high-risk or low-risk of pediatric OSA, participated in this study. 3D stereophotogrammetry, Craniofacial Index, and Pediatric Sleep Questionnaire were obtained from all participants. Ten dental specialists with interest in pediatric sleep breathing disorders classified OSA severity twice, once based only on 3D stereophotogrammetry and then based on 3D stereophotogrammetry, Craniofacial Index, and Pediatric Sleep Questionnaire. Intrarater and interrater reliability and diagnostic accuracy of pediatric OSA classification were calculated. A cluster analysis was performed to identify potential homogeneous pediatric OSA groups based on their craniofacial features classified through the Craniofacial Index .

RESULTS

Intrarater and interrater agreement suggested a poor reproducibility when only 3D facial stereophotogrammetry was used and when all tools were assessed simultaneously. Sensitivity and specificity varied among clinicians, indicating a low screening ability for both 3D facial stereophotogrammetry, ranging from 0.36-0.90 and 0.10-0.70 and all tools ranging from 0.53-1.0 and 0.01-0.49, respectively. A high arched palate and reversed or increased overjet contributed to explaining how participating dental clinicians classified pediatric OSA.

CONCLUSIONS

3D stereophotogrammetry-based facial analysis does not seem predictive for pediatric OSA screening, alone or combined with the Pediatric Sleep Questionnaire and Craniofacial Index when used by dental specialists interested in sleep-disordered breathing. Some craniofacial traits, more specifically significant sagittal overjet discrepancies and an arched palate, seem to influence participating dental specialists' classification.

CITATION

Fernandes Fagundes NC, Carlyle T, Dalci O, et al. Use of facial stereophotogrammetry as a screening tool for pediatric obstructive sleep apnea by dental specialists. J Clin Sleep Med. 2022;18(1):57-66.

Sleep Measurement in Children-Are We on the Right Track?

Sleep plays a critical role in the development of healthy children. Detecting sleep and sleep disorders and the effectiveness of interventions for improving sleep in children require valid sleep measures. Assessment of sleep in children, in particular infants and young children, can be a quite challenging task. Many subjective and objective methods are available to evaluate various aspects of sleep in childhood, each with their strengths and limitations. None can, however, replace the importance of thorough clinical interview with detailed history and clinical examination by a sleep specialist.

Alternatives to surgery in children with mild OSA

Precision medicine requires coordinated and integrated evidence-based combinatorial approaches so that diagnosis and treatment can be tailored to the individual patient. In this context, the treatment approach to mild obstructive sleep apnea (OSA) is fraught with substantial debate as to what is mild OSA, and as to what constitutes appropriate treatment. As such, it is necessary to first establish a proposed consensus of what criteria need to be employed to reach the diagnosis of mild OSA, and then examine the circumstances under which treatment is indicated, and if so, whether and when anti-inflammatory therapy (AIT), rapid maxillary expansion (RME), and/or myofunctional therapy (MFT) may be indicated.

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.

Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis

Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train a MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.

Pediatric pulmonology year in review 2019: Sleep medicine

Pediatric Pulmonology publishes original research, review articles as well as case reports on a wide variety of pediatric respiratory disorders. In this article, we summarize the past year's publications in sleep medicine and we review selected literature from other journals within this field. Articles highlighted are topics on risk factors of sleep-disordered breathing, diagnosis, and treatment of obstructive sleep apnea as well as the utility of polysomnography in various complex conditions.

Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses

Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown.

Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE.

Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE.

Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO₂) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO₂ signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens's kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea-hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO₂ was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO₂ enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.

Preoperative identification of children at high risk of obstructive sleep apnea

Obstructive sleep apnea is a common childhood disorder which can lead to serious health problems if left untreated. Enlarged adenoid and tonsils are the commonest causes, and adenotonsillectomy is the recommended first line of treatment. Obstructive sleep apnea poses as an anesthetic challenge, and it is a well-known risk factor for perioperative adverse events. The presence and severity of an obstructive sleep apnea diagnosis will influence anesthesia, pain management, and level of monitoring in recovery period. Preoperative obstructive sleep apnea assessment is necessary, and anesthetists are ideally placed to do so. Currently, there is no standardized approach to the best method of preoperative screening for obstructive sleep apnea. Focused history, clinical assessments, and knowledge regarding the strengths and limitations of available obstructive sleep apnea assessment tools will help recognize a child with obstructive sleep apnea in the preoperative setting.