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Point-of-Care Ultrasound for Obstructive Sleep Apnea Screening: Are We There Yet? A Systematic Review and Meta-analysis

Singh, Mandeep MD, MSc*,†,‡; Tuteja, Arvind MBBS*; Wong, David T. MD*; Goel, Akash MD*; Trivedi, Aditya BSc§; Tomlinson, George PhD; Chan, Vincent MD*

doi: 10.1213/ANE.0000000000004350
Respiration and Sleep Medicine
Free
SDC

BACKGROUND: Perioperative diagnosis of obstructive sleep apnea (OSA) has important resource implications as screening questionnaires are overly sensitive, and sleep studies are expensive and time-consuming. Ultrasound (US) is a portable, noninvasive tool potentially useful for airway evaluation and OSA screening in the perioperative period. The objective of this systematic review was to evaluate the correlation of surface US with OSA diagnosis and to determine whether a point-of-care ultrasound (PoCUS) for OSA screening may help with improved screening in perioperative period.

METHODS: A search of all electronic databases including Medline, Embase, and Cochrane Database of Systematic Reviews was conducted from database inception to September 2017. Inclusion criteria were observational cohort studies and randomized controlled trials of known or suspected OSA patients undergoing surface US assessment. Article screening, data extraction, and summarization were conducted by 2 independent reviewers with ability to resolve conflict with supervising authors. Diagnostic properties and association between US parameters (index test) and OSA diagnosis using sleep study (reference standard) were evaluated. The US parameters were divided into airway and nonairway parameters. A random-effects meta-analysis was planned, wherever applicable.

RESULTS: Of the initial 3865 screened articles, 21 studies (7 airway and 14 nonairway) evaluating 3339 patients were included. Majority of studies were conducted in the general population (49%), respirology (23%), and sleep clinics (12%). No study evaluated the use of US for OSA in perioperative setting. Majority of included studies had low risk of bias for reference standard and flow and timing. Airway US parameters having moderate–good correlation with moderate–severe OSA were distance between lingual arteries (DLAs > 30 mm; sensitivity, 0.67; specificity, 0.59; 1 study/66 patients); mean resting tongue thickness (>60 mm; sensitivity, 0.85; specificity, 0.59; 1 study/66 patients); tongue base thickness during Muller maneuver (MM; sensitivity, 0.59; specificity, 0.78; 1 study/66 patients); and a combination of neck circumference and retropalatal (RP) diameter shortening during MM (sensitivity, 1.0; specificity, 0.65; 1 study/104 patients). Nonairway US parameters having a low–moderate correlation with moderate–severe OSA were carotid intimal thickness (pooled correlation coefficient, 0.444; 95% confidence interval [CI], 0.320–0.553; P value = .000, 8 studies/727 patients) and plaque presence (sensitivity, 0.24–0.75; specificity, 0.13–1.0; 4 studies/1183 patients).

CONCLUSIONS: We found that a number of airway and nonairway parameters were identified with moderate to good correlation with OSA diagnosis in the general population. In future studies, it remains to be seen whether PoCUS screening for a combination of these parameters can address the pitfalls of OSA screening questionnaires.

From the *Department of Anesthesiology and Pain Management, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada

Toronto Sleep and Pulmonary Centre, Toronto, Ontario, Canada

Department of Anesthesiology and Pain Management, Women’s College Hospital, Toronto, Ontario, Canada

§Department of Chemistry, McMaster University, Hamilton, ON, Canada

Department of Medicine, University Health Network and Mt Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.

Published ahead of print 22 August 2019.

Accepted for publication June 24, 2019.

Funding: This work was funded by Department of Anesthesia and Pain Management, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada.

Conflicts of Interest: See Disclosures at the end of the article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website.

Reprints will not be available from the authors.

Address correspondence to Mandeep Singh, MD, MSc, Department of Anesthesiology and Pain Management, Toronto Western Hospital, University Health Network, University of Toronto, 399 Bathurst St, McL 2-405, Toronto, ON M5T 2S8, Canada. Address e-mail to mandeep.singh@uhn.ca.

See Editorial, p 1454

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GLOSSARY

AHI = apnea–hypopnea index; AUC = area under curve; BA = brachial artery; BMI = body mass index; CI = confidence interval; cIMT = carotid intimal media thickness; CPAP = continuous positive airway pressure; CT = computed tomography; CV = coefficient of variation; DLAs = distance between lingual arteries; HSS = habitual simple snoring; LPW = lateral pharyngeal wall; MM = Muller maneuver; MRI = magnetic resonance imaging; OR = odds ratio; NA = not applicable; NPV = negative predictive value; NR = not reported; OR = odds ratio; OSA = obstructive sleep apnea; OSAS = obstructive sleep apnea syndrome; PoCUS = point-of-care ultrasound; PPV = positive predictive value; PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-analysis; PSG = polysomnography; QUADAS = Quality Assessment of Diagnostic Accuracy Studies; RDI = respiratory disturbance index; ROC = receiver operating curve; RP = retropalatal; RR = relative risk Sao2 = oxygen saturation; SE = standard error; SFT = subcutaneous fat thickness; SROC = summary receiver operating curve; STARD = Standards for Reporting of Diagnostic Accuracy Studies; UA = upper airway; US = ultrasound

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder, characterized by repeated upper airway (UA) obstruction, hypoxemia, and associated with increased morbidity and mortality.1,2 OSA is considered an independent risk factor for postoperative cardiorespiratory complications3,4 and increased perioperative utilization of health care resources.5

OSA is characterized by repeated episodes of complete (apnea) or partial (hypopnea) closure of the UA in the presence of breathing effort during sleep. These episodes are accompanied by oxygen desaturation (Sao2) and hypoventilation of varying severity and terminated by cortical arousal to increase UA dilator activity and increase UA caliber.6–8 OSA severity is classified based on apnea–hypopnea index (AHI) as mild (AHI = 5–15/h), moderate (AHI > 15–30/h), or severe (AHI > 30/h).9

Various OSA phenotypes can be explained physiologically by a decreased UA dilator muscle tone during sleep, low arousal threshold, or high loop gain.10 However, the predominant feature is a narrow and collapsible UA anatomy determined by an interplay between redundant soft tissue, impaired genioglossus muscle tone and the bony confines of UA,11 amounting to two-thirds of the variation in the AHI.10,12 In patients with OSA, not only is the UA typically narrower and more collapsible while awake,8,13–17 it collapses readily during sleep as the UA dilator muscle activity diminishes at sleep onset.16,17 Identification of moderate–severe OSA is crucial to prevent potential life-threatening cardiopulmonary complications perioperatively. However, a large proportion of patients with OSA remain undiagnosed at the time of surgery.18 Current screening tools are mainly questionnaire based and are largely sensitive but not specific19,20 resulting in many false positives, unnecessary increased resource utilization, cost burden, and legal implications.5,21 Gold standard laboratory polysomnography (PSG) study is expensive and not widely available. Point-of-care ultrasound (PoCUS) is a readily available, portable, noninvasive tool that has been used for airway evaluation and may be useful for OSA screening. PoCUS applications involve a focused ultrasound (US) examination that aims at answering well-defined clinical questions to guide patient management and improve clinical outcomes.22–24 Focused airway assessment to diagnose OSA adds to an expanding list of well-established PoCUS applications for pulmonary,25,26 diaphragmatic,27,28 gastric assessment,29 fluid status, and hemodynamic instability.30,31 The objective of this systematic review was to evaluate the utility of surface US measurements for detection and assessment of OSA based on currently available literature and to determine whether a PoCUS tool may be utilized as a screening tool for OSA.

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METHODS

Search Strategy and Study Selection

The current review was designed and prepared according to recommended standards32 and reported as per the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.33–35 A PRISMA checklist is provided in Supplemental Digital Content 1, Appendix 1, http://links.lww.com/AA/C913. A review protocol was prepared and followed before commencing the review. Search strategy was designed according to the PRISMA guidelines and implemented with the help of an expert medical librarian. The search was conducted on August 6, 2016 and updated on September 25, 2017. The literature databases searched from database inception to September 25, 2017, including MEDLINE, ePub ahead of print, MEDLINE in-process, and other nonindexed citations, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science (Thomson Reuters), Scopus (Elsevier), ClinicalTrials.Gov, WHO ICTRP, ProQuest Digital Dissertations, and UHN OneSearch for books/book chapters.

A literature search was done for OSA and US/ultrasonography/sonography: limited to human, adults, English where possible. The search used the Medical Subject Heading keywords “obstructive sleep apnea” and “ultrasonography” or “ultrasound” or “sonography.” Also, the following text keywords were used for the literature search: “obstructive sleep apnea syndrome,” “sleep disordered breathing,” “obesity hypoventilation syndrome,” “apnea or apnoea,” “hypopnea or hypopnoea,” “radiology,” “magnetic resonance,” “x-ray,” “radiography,” “Doppler,” “radiological procedures,” “radiologist,” “ radiology department,” “radiology information systems,” “ computed tomography,” “tomography,” “spectroscopy,” “cephalometry,” “echography,” “imaging,” and “diagnostic imaging.”

Inclusion criteria were as follows: (1) observational studies or randomized controlled trials; (2) adult patients (>18 years old) with information available on OSA; (3) surface US imaging used for correlation with OSA diagnosis; and (4) all studies published in English. Exclusion criteria were as follows: (1) case reports; (2) review articles; (3) studies with no information on OSA status; (4) studies without ultrasonography; and (5) studies with ultrasonography but unrelated to OSA.

Studies were selected independently by 2 reviewers (A.G. and A. Tuteja) who screened the titles and abstracts to determine whether the studies met the eligibility criteria using the Covidence platform.36 Disagreements were resolved by consensus or by other authors (M.S. and V.C.). A citation search by manual review of references from primary or review articles was also performed. Corresponding authors were contacted via email to provide missing data.

The US upper airway parameter was classified according to the anatomical location, suprahyoid versus infrahyoid region, as described before,24 recognizing that the type of anatomical structures and US probes for examination can be quite different. Studies looking at other surface US parameters were classified separately.

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Data Extraction

The following information was collected from each study: author, year of publication, type of study, sample size of OSA and non-OSA group, age, sex, body mass index (BMI), OSA status of patients, OSA diagnosis modality, AHI, PSG data, sleep questionnaire data, US variables and parameters, type of sonography, scanner, and transducer, sonographer intra- and interrater variability, and US methodology for each of the parameters examined.

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Study Quality Assessment

We assessed risk of bias and generalizability using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for diagnostic tests. The QUADAS-2 tool comprises 4 domains: patient selection (appropriateness of patients for the study question, including study design), index test (the surface US measure), reference standard (sleep study diagnostic test), and flow and timing (eg, the index and reference tests were performed within a reasonable time frame of up to 1 year). All 4 domains were assessed for risk of bias, and the first 3 domains (patient selection, index test, and reference standard) were assessed for applicability by indicating a “low,” “high,” or “unclear” rating. In the QUADAS-2, “applicability” refers to whether certain aspects of an individual study matched the review question. The QUADAS-2 does not generate a comprehensive quality score, but rather an overall judgment of low, high, or unclear risk. To have an overall judgment of a low risk of bias or a low concern regarding applicability, a study needed to be low on all relevant domains. If a study received a high or unclear rating in ≥1 domains, then it was judged as being at risk of bias or having concerns regarding applicability. Reference standards were rated as low risk of bias if all parameters of PSG recording were utilized, as unclear risk if 1 or 2 parameters were missing, and as high risk if >2 parameters were missing.

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Data Analysis

Diagnostic properties of the various US parameters for OSA diagnosis and severity were extracted or calculated. The correlation coefficient between a specific airway or nonairway US parameter and OSA severity (AHI or oxygenation parameter) was extracted or calculated from the reported P value and sample size. Sensitivity and specificity of specific US parameter for a specific OSA severity cutoff (mild, moderate, or severe) were reported or calculated (if not reported) by construction of 2 × 2 tables directly from studies. Forest plots were constructed for (1) correlation coefficients between US parameters and OSA severity and (2) sensitivity and specificity of US parameters for diagnosing OSA. Pooled estimates based on DerSimonian and Laird random-effects models were calculated where appropriate. Heterogeneity was evaluated qualitatively and, where there were sufficient studies reporting on the same US parameter/OSA pairing, quantitatively with the I2 statistic. Publication bias was investigated using funnel plots and the Duval and Tweedie trim-and-fill approach, a method that first identifies potentially unpublished estimates based on funnel plot asymmetry and then includes these unpublished estimates in a revised the pooled value. Summary receiver operating curves (ROCs) were also generated where ≥3 studies reported sensitivity and specificity for the same US parameter/OSA combination. Analyses were conducted in Review Manager (RevMan, London, UK, v5.3),37 Comprehensive Meta-analysis (Biostat, Inc, Englewood, NJ),38 and R39 software tools (R Foundation for Statistical Computing, Vienna, Austria), as appropriate.

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RESULTS

Study Selection

Our initial electronic search identified 3865 articles, and after deduplication, and applying eligibility criteria, 69 articles were included for full-text screening and a total of 21 studies were included in the qualitative synthesis (Figure 1). Studies were excluded mainly for the following reasons (Figure 1): surface US not used (21), no abstract of interest (7), duplicate (7), clinical trial registration or case series (5), editorial (3), pediatric population (3), no OSA diagnosis (3), and same study population (1). The complete search strategy is provided as Supplemental Digital Content 2, Search Strategy, http://links.lww.com/AA/C926.

Figure 1.

Figure 1.

Table 1.

Table 1.

Of these 21 studies, 7 airway studies (n = 430) and 14 nonairway studies (n = 2909) evaluating 3339 patients were included (Table 1). The studies were conducted in Bulgaria, China, France, Israel, Italy, Hong Kong, Taiwan, Turkey, and the United States. Studied patients were recruited from sleep clinics (12%), respiratory clinics (23%), cardiology (6%), internal medicine (5%), otolaryngology clinics (5%), and from the general population (49%). None of the studies included patients in the perioperative setting or patients with any other forms of sleep-disordered breathing, such as central sleep apnea, or sleep-related hypoventilation syndromes.

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Quality of Included Studies

According to the QUADAS-2 tool, only 3 studies40,43,44 had low risk of bias and low concern regarding applicability. Risk of bias and applicability concerns were marked high for patient selection in 4 studies, where Altin et al47 included only men suspected to have OSA, 2 studies included patients with known diagnosis of OSA,45,52 and in the study by Meng et al55 where patients undergoing percutaneous coronary intervention, 1 week after acute coronary syndrome were included. Risk of bias and applicability concerns were marked high for index test for 1 study45 due to unclear US scanning technique (Supplemental Digital Content 3–4, Figure 1a, http://links.lww.com/AA/C896, Figure 1b, http://links.lww.com/AA/C897). Most of the studies adequately described the tests, number of patients, recruitment methods, and dropouts. Risk of bias for flow and timing was unclear in 6 studies,41,42,49,50,52,53,56 mainly due to inadequate information on the timing between the sleep study results and the US scan, and high in 1 study45 where simultaneous US and sleep study were performed in 1 setting with little information about feasibility. Applicability concerns were low in majority of the studies for patient selection and index test but unclear for reference standard in 2 studies due to limited information about the number of sleep study parameters used to classify OSA46,52 (Supplemental Digital Content 3–4, Figure 1a, http://links.lww.com/AA/C896, Figure 1b, http://links.lww.com/AA/C897). No included study used screening tools to identify OSA.

The interrater and intrarater variability for the use of US was reported in 2 airway studies43,44 and 1 nonairway study47 with moderate to good performance (Table 2; Supplemental Digital Content 5, Table 1, http://links.lww.com/AA/C898). None of the studies reported having used the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines58 in the article.

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Airway Parameters

Suprahyoid Region.

Tongue Parameters.
Table 2.

Table 2.

Tongue dimensions in relation to respiration and Muller maneuver (MM; a maneuver where the patient is requested to perform a forced inspiratory effort against an obstructed airway by closing the nose and mouth to induce UA collapse in the awake state) were described and listed in Table 2. Lahav et al41 examined the distance between lingual arteries (DLAs), tongue base width (coronal plane), and tongue base height (sagittal plane). For moderate to severe OSA (AHI > 15 events/h), a DLA cutoff of >30 mm had a sensitivity and specificity of 80% and 67%, respectively. Chen et al40 showed that compared to controls (AHI < 5), tongue base thickness in response to negative airway pressure during MM (odds ratio [OR] = 2.11; 95% confidence interval [CI], 1.15–3.87; P < .05) and difference between tongue base thickness with or without MM (OR = 2.47; 95% CI, 1.09–5.58; P < .05) were associated with OSA diagnosis (AHI ≥ 5). Liao et al42 found that out of tongue base width using DLA (30 mm), mean resting tongue base thickness (60 mm), and mean tongue base thickness during MM (63 mm), only resting tongue base thickness (cutoff >60 mm thickness) was found to be the sole predictor for severe OSA (OR = 5.18; 95% CI, 1.07–25.0; P = .04) on multivariable regression. Siegel et al45 found that UA obstruction using US was detected 5–10 seconds before the onset of apnea (full cessation of airflow) during simultaneous overnight PSG; however little description was provided about how the events were identified.

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Pharyngeal Parameters.

Liu et al43 found that US measurement of lateral pharyngeal wall (LPW) thickness had good correlations with magnetic resonance imaging (MRI) measurement (r = 0.78; P = .001) and the fair to moderate correlation with severity of OSA (r =0.37; P = .001). Moreover, LPW thickness was found to have a positive and independent correlation (r = 0.12; P = .002) with AHI after adjustment for age, sex, neck circumference, and BMI in this study. Shu et al44 performed dynamic assessment of pharyngeal parameters such as retropalatal (RP) and retroglossal diameters, during tidal breathing, forced inspiration, and MM. Multivariable analysis indicated that AHI was positively associated with percentage shortening of RP diameter during MM (OR = 1.09; 95% CI, 1.02–1.16; P = .008) and neck circumference (OR = 1.38; 95% CI, 1.14–1.62; P = .001).

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Infrahyoid Region.

Subcutaneous Fat Tissue.

Ugur et al46 measured subcutaneous fat tissue thickness (mm) at the level of the submandibular gland, thyroid isthmus, suprasternal notch, hyoid, and umbilicus by US and concluded that these measurements had no correlation with AHI.

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Nonairway Parameters

We examined studies providing information on correlation with US-identified nonairway structures and OSA diagnosis based on AHI cutoffs or AHI as a measure of OSA severity as a continuous measure.

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Carotid Intimal Media Thickness.

A number of studies evaluated correlation of the carotid intimal media thickness (cIMT) with OSA diagnosis (Table 3; Supplemental Digital Content 5, Table 1, http://links.lww.com/AA/C898). Studies where no data could be extrapolated for either correlation or diagnostic property metrics were excluded. Ciccone et al52 studied the correlation between OSA duration and severity with cIMT US measurements.53 Altin et al47 found ultrasonographic evidence of increased atherosclerotic changes in both left and right common carotid arteries in OSA (P < .05). Andonova et al48 found that the presence of atherosclerotic plaques in common carotid artery was predictive of moderate OSA (sensitivity = 59%, specificity = 70%), and mean cIMT was positively correlated with AHI (r = +0.43; P < .05). Apaydin et al49 found that a higher cIMT was present in patients with OSA compared to habitual snorers. However, cIMT did not correlate with OSA severity. Wattanakit et al57 found a positive relationship between carotid plaque formation and cIMT with OSA severity on a univariate analysis; however, multivariate adjustment for demographic and metabolic factors attenuated with effect. Baguet et al50 showed that nocturnal mean Sao2 (<92%) was associated with cIMT and plaque formation, and minimal nocturnal desaturation (Sao2 < 80%) was associated with plaque formation.

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Other Parameters.

Table 3.

Table 3.

Liu et al60 found that mesenteric fat thickness had a positive association with the presence of moderate OSA (AHI > 15 events/h; OR = 7.18; 1.05–49.3; P value = not reported) for every 1-cm increase in mesenteric fat thickness and severe OSA (AHI > 30 events/h; OR = 7.45; 1.12–49.6; P value = not reported), after accounting for age, sex, BMI, neck circumference, preperitoneal, and subcutaneous fat thickness. In a follow-up study involving a larger sample size, they found that mesenteric fat thickness and AHI predicted metabolic syndrome only in men (OR = 1.02; 95% CI, 1.0–1.04; P = .027) for the increase of 1 event per hour, not in all patients (OR = 1.01; 95% CI, 1.0–1.03; P = .11) or in women (OR = 0.98; 95% CI, 0.95–1.01; P = .19).54 Chami et al51 evaluated US-identified brachial artery (BA) diameter by US and peripheral blood flow dynamics by flow-mediated dilation. A positive association was observed with increasing BA diameter and AHI, where the mean BA diameter (mm) was 4.5 (standard error [SE] = 0.11), 4.55 (0.07), 4.33 (0.04), 4.32 (0.04) for severe, moderate, mild, and no OSA, respectively (P < .05). However, no relation between OSA and flow-mediated dilation was identified.

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Correlation With AHI

Figure 2.

Figure 2.

Various airway and nonairway tools were examined for the strength of correlation with AHI as a continuous measure (Supplemental Digital Content 6, Figure 2, http://links.lww.com/AA/C899). A random-effects meta-analysis (8 studies, 727 patients) was performed to evaluate the pooled estimates for the correlation between cIMT and AHI, where the pooled correlation coefficient was 0.44 (95% CI, 0.320–0.553; Q value = 26.1; P value < .001; I2 = 73%; Figure 2). For the other OSA-related parameters, the data were insufficient to perform a meta-analysis, and summary measures were reported and assessed qualitatively (Supplemental Digital Content 6, Figure 2, http://links.lww.com/AA/C899). Airway measures such as DLAs, RP diameter and %RP diameter shortening during MM, lateral pharyngeal thickness, and UA length were found to have a moderate correlation with AHI (r values range between 0.37 and 0.624; Supplemental Digital Content 6, Figure 2, http://links.lww.com/AA/C899). The correlation between AHI and nonairway parameters such as mesenteric fat thickness and preperitoneal fat thickness was lower (r values range between 0.09 and 0.71; Supplemental Digital Content 6, Figure 2, http://links.lww.com/AA/C899).

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Heterogeneity and Publication Bias

There was significant heterogeneity in the US measures used for evaluating UA that limited the generation of pooled estimates. In the random-effects meta-analysis of the correlation between AHI and cIMT (see above), there was a moderate amount of heterogeneity (I2 = 73%). In a visual inspection of the funnel plot used to check for publication bias in this meta-analysis, studies were distributed symmetrically around the pooled estimate, suggesting no publication bias (Supplemental Digital Content 7, Figure 4, http://links.lww.com/AA/C900). The trim-and-fill method also suggested that there were no unpublished studies.

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Diagnostic Properties of Various US Tools

Figure 3.

Figure 3.

Wherever applicable, diagnostic properties of US tools were examined. Relevant cutoffs examined were AHI > 5, AHI ≥ 15, and AHI ≥ 30 events/h (Figure 3). Overall 8 studies, of which 3 airway41,42,44 and 5 nonairway parameter studies,47,48,52,57,59 were included due to availability of data. The significant airway parameters were tongue base width (DLA > 30 mm), resting tongue base thickness ≥60 mm, resting tongue base thickness during MM, and combination of neck circumference with %RP diameter shortening during MM. These parameters had a high sensitivity (80%–100%), but moderate specificity for moderate to severe OSA diagnosis. On the other hand, the specificity of cIMT thickness (≥0.9 mm) and plaque presence was high (80%–100%) and was found to have a low to moderate sensitivity (20%–50%) for moderate OSA. The data were inadequate to pool results and evaluate for summary estimates, and no meta-analysis was performed.61,62 However, graphical estimation of indirect comparison of the US parameters was conducted by generating ROCs wherever applicable for US parameters against OSA severity levels (Supplemental Digital Content 8, Figure 3, http://links.lww.com/AA/C901).62 Overall, findings indicated that the strength of association was highest for the combination of neck circumference and %RP shortening during MM (sensitivity = 1.0, 95% CI, 0.93–1.0; specificity = 0.65, 95% CI, 0.51–0.77). Although data from Ciccone et al52 indicated a good diagnostic profile for moderate to severe OSA for carotid plaque presence (sensitivity = 1.0, 95% CI, 0.93–1.0; specificity = 0.65, 95% CI, 0.51–0.77), the results should be read with caution as this study only included patients with OSA with no control group, with >70% on continuous positive airway pressure (CPAP) with varying compliance, thereby impacting the negative predictive value in this study.

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DISCUSSION

To our knowledge, this is the first systematic review evaluating utility of surface US measurements for OSA diagnosis and correlation with its severity. Although a number of US determined airway and nonairway parameters were found to be associated with OSA diagnosis, there was significant heterogeneity and scarcity of well-designed studies to validate US as a useful OSA screening tool.

Many surgical patients with OSA remain undiagnosed at the time of surgery.18 The gold standard for OSA diagnosis is an overnight laboratory-based PSG; however, due to increased cost and resource burden, this could potentially impact timely diagnosis and treatment.63,64 Although portable sleep devices are gaining popularity and are less costlier than PSG, they are not suitable as a bedside, point-of-care tool in the preoperative setting. Patient questionnaires and scoring systems developed for OSA screening65–69 are largely sensitive but less specific19,20 with increased false positives leading to increased resource utilization and cost burden. US is a noninvasive, portable, and affordable clinical tool that is fast becoming a core skill set of physicians and health care providers.

Anatomical factors of the UA account for two-thirds of the variation in OSA severity.10,11 Past computed tomography (CT) and MRI studies of the UA have identified various anatomical risk factors for OSA including enlargement of the tongue,70,71 soft palate,72 adenotonsillar tissue,73 parapharyngeal fat pads,73 and LPWs70 in conjunction with retrognathia. Airway obstruction at the RP and retroglossal regions of the pharynx,74 an inferiorly displaced hyoid,75,76 increased UA length,77 increased pharyngeal length, and increased tongue dimensions76 have been linked to OSA. Although CT and MRI are excellent airway evaluation tools, they are costly and inaccessible, thus not practical for OSA diagnosis. US scanning protocol of the UA has been described in the suprahyoid and infrahyoid regions.78 Subsequent studies showed good correlation of US with CT-derived measured airway parameters,78,79 with good inter- and intraobserver reliability.80 Another study in OSA patients also successfully correlated the LPW thickness detected by US with MRI.43 US has the potential to study UA collapse, and predict the site of UA obstruction.81

In this review, we identified a combination of neck circumference and %RP diameter shortening during MM, tongue base thickness during MM, resting tongue base thickness, tongue base width (DLA > 30 mm), and LPW thickening to be useful US parameters for future exploration. Mueller’s Maneuver, performed by requesting the patient to perform a forced inspiratory effort against an obstructed airway by closing the nose and mouth, has been shown to be correlated with endoscopic findings of UA collapse.82 Shu et al44 proposed a prediction model combined with neck circumference and a percentage reduction in RP diameter during MM. Chen et al40 evaluated tongue base thickness during MM and difference between tongue base thickness with or without MM were independent predictors of OSA (AHI > 5 events/h). Using static and dynamic measures of airway, US has the potential in establishing the site of obstruction42–44,83 and potentially evaluate treatment effectiveness following CPAP or airway surgeries.

In addition, we found that US airway parameters had a high sensitivity for diagnosis of moderate to severe OSA, whereas surrogate metabolic sequelae of OSA such as carotid plaque formation and carotid intimal thickness were more specific (Table 2; Supplemental Digital Content 5, Table 1, http://links.lww.com/AA/C898). A combination of US airway parameters can likely increase diagnostic performance of this examination, but this needs to be evaluated in future studies. Several patient questionnaires and scoring systems have already incorporated nonairway parameters such as hypertension diagnosis.19,20,84,85 It remains to be seen how the incorporation of nonairway measures would increase both sensitivity and specificity of a PoCUS-OSA tool.

Our review has certain limitations, and the use of PoCUS in the perioperative period needs to be investigated further before becoming mainstream. Even if 21 studies were included in this systematic review, most of the results were based on only the small subset of studies. Although we successfully identified a number of abnormal airway and nonairway US parameters correlating with OSA severity, all of these were from the general population with increased heterogeneity thereby decreasing the generalizability and application in the perioperative setting. Patients with significant craniofacial abnormalities or previous neck surgeries were excluded from most studies, and utility of US in this patient population would need to be investigated. Nevertheless, our findings will stimulate further prospective research to evaluate the usefulness compared to current questionnaire-based OSA screening tools, as in the ongoing trial at our institution (NCT03361553).86 In addition, although AHI has for the longest time been an index to gauge severity of the condition, other parameters such as severity of Sao2 could be important parameters linked with postoperative complications.87,88 Furthermore, there is emerging knowledge to classify OSA patients based on the physiological response during breathing events, such as low arousal threshold or high loop gain that has important treatment implications in the perioperative setting.10–12 However, the equipment used for these measures is bulky and currently limited to the research setting. The PoCUS-OSA screening tool on the other hand, arguably, has the potential to circumvent this limitation due to the increased availability and portability in the perioperative setting.

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CONCLUSIONS

We identified a number of airway and nonairway US parameters having moderate to strong correlation with OSA that may be incorporated in a PoCUS-OSA screening tool. Among the airway parameters, a combination of neck circumference and %RP diameter shortening during MM, tongue base thickness during MM, resting tongue base thickness, tongue base width (DLA > 30 mm), and LPW thickening best predicted moderate to severe OSA diagnosis. Nonairway parameters including carotid plaque formation and carotid intimal thickening may be included in combination with symptoms and airway parameters to increase diagnostic performance (both sensitivity and specificity) of surface US. Although PoCUS is a potential tool for screening OSA, all past study data had significant heterogeneity and were obtained from studies conducted outside of the perioperative setting. This is a new exciting area of investigation, and future studies should build on this work to determine whether a perioperative PoCUS can further improve diagnostic accuracy of OSA questionnaire-based tools.

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ACKNOWLEDGMENTS

We would like to thank information specialist Marina Englesakis, BA (Hons), MLIS, at the University Health Network for the immense help in conducting the literature search and providing details of the search strategy, and Vivek Kumar, MBBS, MPH, DEM, research coordinator, Department of Anesthesiology, Toronto Western Hospital, University Health Network, for assisting with edits and submission.

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DISCLOSURES

Name: Mandeep Singh, MD, MSc.

Contribution: This author helped design the review, review the literature, and write the manuscript.

Conflicts of Interest: M. Singh has received peer-reviewed research funding for the Canadian Anesthesiologists’ Society (CAS), Society of Anesthesiology and Sleep Medicine, Ontario Ministry of Health and Long-Term Care. M. Singh currently holds the CAS Career Scientist Grant and a Merit-award from the Department of Anesthesiology and Pain Medicine, University of Toronto to support academic time.

Name: Arvind Tuteja, MBBS.

Contribution: This author helped design the review, review the literature, and write the manuscript.

Conflicts of Interest: None.

Name: David T. Wong, MD.

Contribution: This author helped review the literature and write the manuscript.

Conflicts of Interest: None.

Name: Akash Goel, MD.

Contribution: This author helped design the review, review the literature, and write the manuscript.

Conflicts of Interest: None.

Name: Aditya Trivedi, BSc.

Contribution: This author helped review the literature and write the manuscript.

Conflicts of Interest: None.

Name: George Tomlinson, PhD.

Contribution: This author helped review the literature and write the manuscript.

Conflicts of Interest: None.

Name: Vincent Chan, MD.

Contribution: This author helped review the literature and write the manuscript.

Conflicts of Interest: V. Chan received an honorarium from B. Braun, Aspen Pharma, and SonoSite and was on the medical advisory board of Smiths Medical.

This manuscript was handled by: David Hillman, MD.

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REFERENCES

1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177:1006–1014.
2. Durán J, Esnaola S, Rubio R, Iztueta A. Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. Am J Respir Crit Care Med. 2001;163:685–689.
3. Mutter TC, Chateau D, Moffatt M, Ramsey C, Roos LL, Kryger M. A matched cohort study of postoperative outcomes in obstructive sleep apnea: could preoperative diagnosis and treatment prevent complications? Anesthesiology. 2014;121:707–718.
4. Memtsoudis SG, Besculides MC, Mazumdar M. A rude awakening–the perioperative sleep apnea epidemic. N Engl J Med. 2013;368:2352–2353.
5. Memtsoudis SG, Stundner O, Rasul R, et al. The impact of sleep apnea on postoperative utilization of resources and adverse outcomes. Anesth Analg. 2014;118:407–418.
6. Remmers JE, deGroot WJ, Sauerland EK, Anch AM. Pathogenesis of upper airway occlusion during sleep. J Appl Physiol Respir Environ Exerc Physiol. 1978;44:931–938.
7. Mezzanotte WS, Tangel DJ, White DP. Waking genioglossal electromyogram in sleep apnea patients versus normal controls (a neuromuscular compensatory mechanism). J Clin Invest. 1992;89:1571–1579.
8. Strollo PJ Jr, Rogers RM. Obstructive sleep apnea. N Engl J Med. 1996;334:99–104.
9. Iber C, Cheeson A, Quan SF, Ancoli-Israel S. The AASM Manual for the Scoring of Sleep and Associated Events, Rules, Terminology and Technical Specifications. 2007.Westchester, IL: American Academy of Sleep Medicine
10. Subramani Y, Singh M, Wong J, Kushida CA, Malhotra A, Chung F. Understanding phenotypes of obstructive sleep apnea: applications in anesthesia, surgery, and perioperative medicine. Anesth Analg. 2017;124:179–191.
11. Eckert DJ. Phenotypic approaches to obstructive sleep apnoea - New pathways for targeted therapy. Sleep Med Rev. 2018;37:45–59.
12. Eckert DJ, Younes MK. Arousal from sleep: implications for obstructive sleep apnea pathogenesis and treatment. J Appl Physiol (1985). 2014;116:302–313.
13. Bradley TD, Brown IG, Grossman RF, et al. Pharyngeal size in snorers, nonsnorers, and patients with obstructive sleep apnea. N Engl J Med. 1986;315:1327–1331.
14. Brown IG, Bradley TD, Phillipson EA, Zamel N, Hoffstein V. Pharyngeal compliance in snoring subjects with and without obstructive sleep apnea. Am Rev Respir Dis. 1985;132:211–215.
15. Stauffer JL, Zwillich CW, Cadieux RJ, et al. Pharyngeal size and resistance in obstructive sleep apnea. Am Rev Respir Dis. 1987;136:623–627.
16. Horner RL. Neural control of the upper airway: integrative physiological mechanisms and relevance for sleep disordered breathing. Compr Physiol. 2012;2:479–535.
17. Horner RL, Hughes SW, Malhotra A. State-dependent and reflex drives to the upper airway: basic physiology with clinical implications. J Appl Physiol (1985). 2014;116:325–336.
18. Singh M, Liao P, Kobah S, Wijeysundera DN, Shapiro C, Chung F. Proportion of surgical patients with undiagnosed obstructive sleep apnoea. Br J Anaesth. 2013;110:629–636.
19. Ramachandran SK, Josephs LA. A meta-analysis of clinical screening tests for obstructive sleep apnea. Anesthesiology. 2009;110:928–939.
20. Nagappa M, Liao P, Wong J, et al. Validation of the STOP-bang questionnaire as a screening tool for obstructive sleep apnea among different populations: a systematic review and meta-analysis. PLoS One. 2015;10:e0143697.
21. Fouladpour N, Jesudoss R, Bolden N, Shaman Z, Auckley D. Perioperative complications in obstructive sleep apnea patients undergoing surgery: a review of the legal literature. Anesth Analg. 2016;122:145–151.
22. Ramsingh D, Rinehart J, Kain Z, et al. Impact assessment of perioperative point-of-care ultrasound training on anesthesiology residents. Anesthesiology. 2015;123:670–682.
23. De Marchi L, Meineri M. POCUS in perioperative medicine: a North American perspective. Crit Ultrasound J. 2017;9:19.
24. Skubas NJ. Teaching whole body point-of-care ultrasound: advancing the skills of tomorrow’s anesthesiologists. Anesthesiology. 2015;123:499–500.
25. Ding W, Shen Y, Yang J, He X, Zhang M. Diagnosis of pneumothorax by radiography and ultrasonography: a meta-analysis. Chest. 2011;140:859–866.
26. Lichtenstein DA, Mezière GA. Relevance of lung ultrasound in the diagnosis of acute respiratory failure: the BLUE protocol. Chest. 2008;134:117–125.
27. Gerscovich EO, Cronan M, McGahan JP, Jain K, Jones CD, McDonald C. Ultrasonographic evaluation of diaphragmatic motion. J Ultrasound Med. 2001;20:597–604.
28. Boussuges A, Gole Y, Blanc P. Diaphragmatic motion studied by M-mode ultrasonography. Chest. 2009;135:391–400.
29. Perlas A, Mitsakakis N, Liu L, et al. Validation of a mathematical model for ultrasound assessment of gastric volume by gastroscopic examination. Anesth Analg. 2013;116:357–363.
30. Jensen MB, Sloth E, Larsen KM, Schmidt MB. Transthoracic echocardiography for cardiopulmonary monitoring in intensive care. Eur J Anaesthesiol. 2004;21:700–707.
31. Bøtker MT, Vang ML, Grøfte T, Sloth E, Frederiksen CA. Routine pre-operative focused ultrasonography by anesthesiologists in patients undergoing urgent surgical procedures. Acta Anaesthesiol Scand. 2014;58:807–814.
32. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Deeks JJ, Bossuyt PM, Gatsonis C. Analysing and presenting results. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 1.0. 2010. The Cochrane Collaboration, Available at: http://srdta.cochrane.org/. Accessed August 1, 2019.
33. Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.
34. Stewart DM, Larissa S, Mike C, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.
35. McGrath TA, Alabousi M, Skidmore B, et al. Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review. Syst Rev. 2017;6:194.
36. Covidence Systematic Review SoftwareMelbourne, Australia: Veritas Health Innovation. Available at: www.covidence.org. Accessed August 5, 2019.
37. Review Manager (RevMan) [Computer program]. Version 5.3. 2014.Copenhagen, Denmark: The Nordic Cochrane Centre, The Cochrane Collaboration
38. Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive Meta-Analysis (Version 3.0) [Computer software]. Englewood, NJ: Biostat. Available at: http://www.meta-analysis.com. Accessed August 5, 2019.
39. R CoreTeam. R: A Language and Environment for Statistical Computing. 2013.Vienna, Austria: R Foundation for Statistical Computing
40. Chen JW, Chang CH, Wang SJ, Chang YT, Huang CC. Submental ultrasound measurement of dynamic tongue base thickness in patients with obstructive sleep apnea. Ultrasound Med Biol. 2014;40:2590–2598.
41. Lahav Y, Rosenzweig E, Heyman Z, Doljansky J, Green A, Dagan Y. Tongue base ultrasound: a diagnostic tool for predicting obstructive sleep apnea. Ann Otol Rhinol Laryngol. 2009;118:179–184.
42. Liao L-J, Cho T-Y, Cheng P-W, Wang C-T, Lo W-C, Huang T-W. Submental ultrasonography in diagnosing severe obstructive sleep apnea syndrome. J Med Ultrasound. 2016;24:107–111.
43. Liu KH, Chu WC, To KW, et al. Sonographic measurement of lateral parapharyngeal wall thickness in patients with obstructive sleep apnea. Sleep. 2007;30:1503–1508.
44. Shu C-C, Lee P, Lin J-W, et al. The use of sub-mental ultrasonography for identifying patients with severe obstructive sleep apnea. Yung W, ed. PLoS One. 2013;8:e62848.
45. Siegel H, Sonies BC, Graham B, et al. Obstructive sleep apnea: a study by simultaneous polysomnography and ultrasonic imaging. Neurology. 2000;54:1872.
46. Ugur KS, Ark N, Kurtaran H, et al. Subcutaneous fat tissue thickness of the anterior neck and umbilicus in patients with obstructive sleep apnea. Otolaryngol Head Neck Surg. 2011;145:505–510.
47. Altin R, Ozdemir H, Mahmutyazicioğlu K, et al. Evaluation of carotid artery wall thickness with high-resolution sonography in obstructive sleep apnea syndrome. J Clin Ultrasound. 2005;33:80–86.
48. Andonova S, Petkova D, Bocheva Y. Intima-media thickness of the carotid artery in OSAS patients. Perspect Med. 2012;1:160–163.
49. Apaydin M, Ayik SO, Akhan G, Peker S, Uluc E. Carotid intima-media thickness increase in patients with habitual simple snoring and obstructive sleep apnea syndrome is associated with metabolic syndrome. J Clin Ultrasound. 2013;41:290–296.
50. Baguet JP, Hammer L, Lévy P, et al. The severity of oxygen desaturation is predictive of carotid wall thickening and plaque occurrence. Chest. 2005;128:3407–3412.
51. Chami HA, Keyes MJ, Vita JA, et al. Brachial artery diameter, blood flow and flow-mediated dilation in sleep-disordered breathing. Vasc Med. 2009;14:351–360.
52. Ciccone MM, Scicchitano P, Mitacchione G, et al. Is there a correlation between OSAS duration/severity and carotid intima-media thickness? Respir Med. 2012;106:740–746.
53. Ciccone MM, Scicchitano P, Zito A, et al. Correlation between inflammatory markers of atherosclerosis and carotid intima-media thickness in obstructive sleep apnea. Molecules. 2014;19:1651–1662.
54. Liu KH, Chu WC, To KW, et al. Mesenteric fat thickness is associated with metabolic syndrome independently of apnoea-hypopnoea index in subjects with obstructive sleep apnoea. Respirology. 2016;21:533–540.
55. Meng S, Fang L, Wang CQ, Wang LS, Chen MT, Huang XH. Impact of obstructive sleep apnoea on clinical characteristics and outcomes in patients with acute coronary syndrome following percutaneous coronary intervention. J Int Med Res. 2009;37:1343–1353.
56. Minoguchi K, Yokoe T, Tazaki T, et al. Increased carotid intima-media thickness and serum inflammatory markers in obstructive sleep apnea. Am J Respir Crit Care Med. 2005;172:625–630.
57. Wattanakit K, Boland L, Punjabi NM, Shahar E. Relation of sleep-disordered breathing to carotid plaque and intima-media thickness. Atherosclerosis. 2008;197:125–131.
58. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.
59. Apaydin M, Ayik SO, Varer M, Akhan G, Peker S, Uluc E. Carotid intima-media thickness increase in sleep disorders. Neuroradiology. 2013;55:S115.
60. Liu KH, Chu WC, To KW, et al. Mesenteric fat thickness is associated with increased risk of obstructive sleep apnoea. Respirology. 2014;19:92–97.
61. Takwoingi Y, Riley RD, Deeks JJ. Meta-analysis of diagnostic accuracy studies in mental health. Evid Based Ment Health. 2015;18:103–109.
62. Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM; Cochrane Diagnostic Test Accuracy Working Group. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149:889–897.
63. Rotenberg B, George C, Sullivan K, Wong E. Wait times for sleep apnea care in ontario: a multidisciplinary assessment. Can Respir J. 2010;17:170–174.
64. Fleetham J, Ayas N, Bradley D, et al.; Canadian Thoracic Society Sleep Disordered Breathing Committee. Canadian Thoracic Society 2011 guideline update: diagnosis and treatment of sleep disordered breathing. Can Respir J. 2011;18:25–47.
65. Gross JB, Bachenberg KL, Benumof JL, et al.; American Society of Anesthesiologists Task Force on Perioperative Management. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology. 2006;104:1081–1093; quiz 1117.
66. Chung F, Yegneswaran B, Liao P, et al. STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology. 2008;108:812–821.
67. Netzer NC, Hoegel JJ, Loube D, et al.; Sleep in Primary Care International Study Group. Prevalence of symptoms and risk of sleep apnea in primary care. Chest. 2003;124:1406–1414.
68. Ramachandran SK, Kheterpal S, Consens F, et al. Derivation and validation of a simple perioperative sleep apnea prediction score. Anesth Analg. 2010;110:1007–1015.
69. American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: an updated report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology. 2014;120:268–286.
70. Schwab RJ, Pasirstein M, Pierson R, et al. Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging. Am J Respir Crit Care Med. 2003;168:522–530.
71. Iida-Kondo C, Yoshino N, Kurabayashi T, Mataki S, Hasegawa M, Kurosaki N. Comparison of tongue volume/oral cavity volume ratio between obstructive sleep apnea syndrome patients and normal adults using magnetic resonance imaging. J Med Dent Sci. 2006;53:119–126.
72. Lowe AA, Fleetham JA, Adachi S, Ryan CF. Cephalometric and computed tomographic predictors of obstructive sleep apnea severity. Am J Orthod Dentofacial Orthop. 1995;107:589–595.
73. Schwab RJ, Kim C, Bagchi S, et al. Understanding the anatomic basis for obstructive sleep apnea syndrome in adolescents. Am J Respir Crit Care Med. 2015;191:1295–1309.
74. Barrera JE. Sleep magnetic resonance imaging: dynamic characteristics of the airway during sleep in obstructive sleep apnea syndrome. Laryngoscope. 2011;121:1327–1335.
75. Genta PR, Schorr F, Eckert DJ, et al. Upper airway collapsibility is associated with obesity and hyoid position. Sleep. 2014;37:1673–1678.
76. Kirkness JP, Sowho M, Murano E. The interplay between tongue tissue volume, hyoid position, and airway patency. Sleep. 2014;37:1585–1586.
77. Segal Y, Malhotra A, Pillar G. Upper airway length may be associated with the severity of obstructive sleep apnea syndrome. Sleep Breath. 2008;12:311–316.
78. Singh M, Chin KJ, Chan VW, Wong DT, Prasad GA, Yu E. Use of sonography for airway assessment: an observational study. J Ultrasound Med. 2010;29:79–85.
79. Prasad A, Yu E, Wong DT, Karkhanis R, Gullane P, Chan VW. Comparison of sonography and computed tomography as imaging tools for assessment of airway structures. J Ultrasound Med. 2011;30:965–972.
80. Abdallah FW, Yu E, Cholvisudhi P, et al. Is ultrasound a valid and reliable imaging modality for airway evaluation?: an observational computed tomographic validation study using submandibular scanning of the mouth and oropharynx. J Ultrasound Med. 2017;36:49–59.
81. Isaiah A, Mezrich R, Wolf J. Ultrasonographic detection of airway obstruction in a model of obstructive sleep apnea. Ultrasound Int Open. 2017;3:E34–E42.
82. Terris DJ, Hanasono MM, Liu YC. Reliability of the Muller maneuver and its association with sleep-disordered breathing. Laryngoscope. 2000;110:1819–1823.
83. Wang HC, Shu CC, Lee PL, Cheng SL, Yu CJ, Yang PC. Submental ultrasonography in diagnosis of obstructive sleep apnea. Am J Respir Crit Care Med. 2009;179:1.
84. Gali B, Whalen FX, Schroeder DR, Gay PC, Plevak DJ. Identification of patients at risk for postoperative respiratory complications using a preoperative obstructive sleep apnea screening tool and postanesthesia care assessment. Anesthesiology. 2009;110:869–877.
85. Chung F, Yegneswaran B, Liao P, et al. Validation of the Berlin Questionnaire and American Society of Anesthesiologists checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology. 2008;108:822–830.
86. Singh M, Chan V. Obstructive sleep apnea airway evaluation. Available at: https://clinicaltrials.gov/ct2/show/NCT03361553. Accessed February 25, 2018.
87. Chung F, Liao P, Yegneswaran B, Shapiro CM, Kang W. Postoperative changes in sleep-disordered breathing and sleep architecture in patients with obstructive sleep apnea. Anesthesiology. 2014;120:287–298.
88. Chung F, Liao P, Elsaid H, Islam S, Shapiro CM, Sun Y. Oxygen desaturation index from nocturnal oximetry: a sensitive and specific tool to detect sleep-disordered breathing in surgical patients. Anesth Analg. 2012;114:993–1000.
89. Drager LF, Bortolotto LA, Maki-Nunes C, et al. The incremental role of obstructive sleep apnoea on markers of atherosclerosis in patients with metabolic syndrome. Atherosclerosis. 2010;208:490–495.
90. Schulz R, Seeger W, Fegbeutel C, et al. Changes in extracranial arteries in obstructive sleep apnoea. Eur Respir J. 2005;25:69–74.
91. Yun C-H, Jung K-H, Chu K, et al. Increased circulating endothelial microparticles and carotid atherosclerosis in obstructive sleep apnea. J Clin Neurol. 2010;6:89–98.

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