Influence of Neck Circumference and Body Mass Index on Obstructive Sleep Apnoea Severity in Males

The Predictive Role of Subcutaneous Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea

Simple Summary

Although several methods are used to diagnose obstructive sleep apnoea (OSA), the disorder is still underdiagnosed, leading to public healthcare problems. The main aim of the present study was to analyse the role of artificial intelligence in OSA diagnostics and obstruction localisation and, moreover, the role of subcutaneous adipose tissue in OSA pathophysiology. The significance of the present investigation is that using US in OSA diagnostics and obstruction location, an additional opportunity besides standard procedures (i.e., drug-induced sleep endoscopy or polygraphy) is presented, which is vital due to the high number of undiagnosed cases. Applying the algorithm, including artificial intelligence, the presence of obstructions and its localisation, can be determined with high precision. This can be essential in therapy planning or preoperative patient preparation.

Abstract

Introduction: Our aim was to investigate the applicability of artificial intelligence in predicting obstructive sleep apnoea (OSA) and upper airway obstruction using ultrasound (US) measurements of subcutaneous adipose tissues (SAT) in the regions of the neck, chest and abdomen. Methods: One hundred patients were divided into mild (32), moderately severe-severe (32) OSA and non-OSA (36), according to the results of the polysomnography. These patients were examined using anthropometric measurements and US of SAT and drug-induced sleep endoscopy. Results: Using SAT US and anthropometric parameters, oropharyngeal obstruction could be predicted in 64% and tongue-based obstruction in 72%. In predicting oropharyngeal obstruction, BMI, abdominal and hip circumferences, submental SAT and SAT above the second intercostal space on the left were identified as essential parameters. Furthermore, tongue-based obstruction was predicted mainly by height, SAT measured 2 cm above the umbilicus and submental SAT. The OSA prediction was successful in 97% using the parameters mentioned above. Moreover, other parameters, such as US-based SAT, with SAT measured 2 cm above the umbilicus and both-sided SAT above the second intercostal spaces as the most important ones. Discussion: Based on our results, several categories of OSA can be predicted using artificial intelligence with high precision by using SAT and anthropometric parameters.

Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: A bivariate meta-analysis

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder; however, it remains underdiagnosed and undertreated. Although screening tools such as the Berlin questionnaire (BQ), STOP-BANG questionnaire (SBQ), STOP questionnaire (STOP), and Epworth sleepiness scale (ESS) are widely used for OSA, the findings regarding their diagnostic accuracy are controversial. Therefore, this meta-analysis investigated and compared the summary sensitivity, specificity, and diagnostic odds ratio (DOR) among the BQ, SBQ, STOP, and ESS according to the severity of OSA. Electronic databases, namely the Embase, PubMed, PsycINFO, ProQuest dissertations and theses A&I databases, and China knowledge resource integrated database, were searched from their inception to July 15, 2016. We included studies examining the sensitivity and specificity of the BQ, SBQ, STOP, and ESS against the apnea-hypopnea index (AHI) or respiratory disturbance index (RDI). The revised quality assessment of diagnostic accuracy studies was used to evaluate the methodological quality of studies. A random-effects bivariate model was used to estimate the summary sensitivity, specificity, and DOR of the tools. We identified 108 studies including a total of 47 989 participants. The summary estimates were calculated for the BQ, SBQ, STOP, and ESS in detecting mild (AHI/RDI ≥ 5 events/h), moderate (AHI/RDI ≥ 15 events/h), and severe OSA (AHI/RDI ≥ 30 events/h). The performance levels of the BQ, SBQ, STOP, and ESS in detecting OSA of various severity levels are outlined as follows: for mild OSA, the pooled sensitivity levels were 76%, 88%, 87%, and 54%; pooled specificity levels were 59%, 42%, 42%, and 65%; and pooled DORs were 4.30, 5.13, 4.85, and 2.18, respectively. For moderate OSA, the pooled sensitivity levels were 77%, 90%, 89%, and 47%; pooled specificity levels were 44%, 36%, 32%, and 621%; and pooled DORs were 2.68, 5.05, 3.71, and 1.45, respectively. For severe OSA, the pooled sensitivity levels were 84%, 93%, 90%, and 58%; pooled specificity levels were 38%, 35%, 28%, and 60%; and pooled DORs were 3.10, 6.51, 3.37, and 2.10, respectively. Therefore, for mild, moderate, and severe OSA, the pooled sensitivity and DOR of the SBQ were significantly higher than those of other screening tools (P < .05); however, the specificity of the SBQ was lower than that of the ESS (P < .05). Moreover, age, sex, body mass index, study sample size, study populations, presence of comorbidities, PSG or portable monitoring performance, and risk of bias in the domains of the index test and reference standard were significant moderators of sensitivity and specificity (P < .05). Compared with the BQ, STOP, and ESS, the SBQ is a more accurate tool for detecting mild, moderate, and severe OSA. Sleep specialists should use the SBQ to conduct patient interviews for the early diagnosis of OSA in clinical settings, particularly in resource-poor countries and sleep clinics where PSG is unavailable.