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Respiration and Sleep Medicine

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*

Author Information
doi: 10.1213/ANE.0000000000004350


See Editorial, p 1454


  • Question: To what extent has previously published literature evaluated the use of surface ultrasound (US) measurement to diagnose and screen for obstructive sleep apnea (OSA), and whether a point-of-care ultrasound (PoCUS) tool can be used to address pitfalls of available screening questionnaires?
  • Findings: In this systematic review, we identified a set of airway and nonairway US parameters that have fair to good correlation with OSA diagnosis in the general population but not in the perioperative setting.
  • Meaning: Use of PoCUS is an exciting area of research in the perioperative setting, and future studies should aim to systematically validate this set of airway and nonairway parameters and to determine whether surface US can screen for OSA and address pitfalls of OSA screening questionnaires.


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.


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, 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.

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.

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.

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.


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,

Figure 1.
Figure 1.:
PRISMA flowchart. OSA indicates obstructive sleep apnea; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analysis.
Table 1.
Table 1.:
Table of Demographics

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.

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,, Figure 1b, 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,, Figure 1b, 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, None of the studies reported having used the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines58 in the article.

Airway Parameters

Suprahyoid Region.

Tongue Parameters.
Table 2.
Table 2.:
Summary of Findings: Airway Ultrasound Parameters

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.

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).

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.

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.

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, 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.

Other Parameters.

Table 3.
Table 3.:
Ultrasound Scanning Technique Table

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.

Correlation With AHI

Figure 2.
Figure 2.:
Meta-analysis of correlation between cIMT and AHI. AHI indicates apnea–hypopnea index; CI, confidence interval; cIMT, carotid intimal media thickness.

Various airway and nonairway tools were examined for the strength of correlation with AHI as a continuous measure (Supplemental Digital Content 6, Figure 2, 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, 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, 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,

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, The trim-and-fill method also suggested that there were no unpublished studies.

Diagnostic Properties of Various US Tools

Figure 3.
Figure 3.:
Diagnostic properties of the relevant US measures based on various AHI cutoffs. AHI indicates apnea–hypopnea index; CI, confidence interval; FN, false negative; FP, false positive; MM, Muller maneuver; NC, neck circumference; OSA, obstructive sleep apnea; RP diameter, retropalatal diameter; TN, true negative; TP, true positive.

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, 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.


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, 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.


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.


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.


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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