Point-of-Care Ultrasound for Obstructive Sleep Apnea Screening: Are We There Yet? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".