METHODS: Children having home overnight oximetry for suspected OSA were identified over 12 months, and those with a normal result who went on to have polysomnography (PSG) were included. Oximetry, including PR-SD and PRI (rises of 8, 10 and 15 beats/min per hour), was analyzed using commercially available software. PR parameters were compared between those with OSA (obstructive apnoea-hypopnoea index (OAHI) >1 event/h) and those without OSA.
RESULTS: One hundred sixteen children had normal oximetry, of whom 93 (median age 4.5 years; 55 % M) had PSG. Fifty-seven of 93 (61 %) children had OSA (median OAHI 4.5 events/h, range 1.1-24). PR-SD was not different between the OSA and non-OSA groups (p = 0.87). PRI tended to be higher in those with OSA, but there was considerable overlap between the groups: PRI-8 (mean ± SD 58.5 ± 29.0/h in OSA group vs 48.6 ± 20.2/h in non-OSA group, p = 0.07), PRI-10 (45.1 ± 25.0 vs 36.2 ± 16.7, p = 0.06) and PRI-15 (24.4 ± 14.5 vs 18.9 ± 9.0, p = 0.04). A PRI-15 threshold of >35/h had specificity of 97 % for OSA.
CONCLUSION: The PRI-15 shows promise as an indicator of OSA in children with normal oximetry.
METHODOLOGY: This is a descriptive cross-sectional study at the Sleep Clinic, Department of Otorhinolaryngology-Head and Neck Surgery. Flexible nasopharyngolaryngoscopy was performed in seated erect and supine position. Retropalatal and retroglossal regions were continuously recorded during quiet breathing and Mueller's maneuver in both positions. Captured images were measured using Scion Image software and narrowing rate was calculated. Level of each site was classified based on Fujita classification and severity of obstruction using Sher scoring system for Mueller's maneuver.
RESULTS: A total of 59 patients participated in this study. Twenty-nine (49.2%) participants had type 1 (retropalatal) obstruction, 23 (38.9%) had type 2 (retropalatal and retroglossal), and seven (11.9%) in type 3 (retroglossal) obstruction. Fifty (84.7%) of the patients have severe obstruction at the retropalatal region in supine position (SRP) followed by 35 (59.3%) at retropalatal region in erect position (ERP), 27 (45.8%) at retroglossal region in supine position (SRG) and eight (13.5%) at retroglossal region in erect position (ERG). The average oxygen saturation showed significant association in ERP (P = 0.012) and SRP (P < 0.001), but not significant in ERG and SRG.
CONCLUSIONS: Videoendoscopy utilizing flexible nasopharyngolaryngoscopy and Scion Image software is reliable, minimally invasive, and useful as an office procedure in evaluating the multilevel obstruction of upper airway in OSA patients. The retropalatal region has more severe obstruction compared with retroglossal region either in erect or supine position.
OBJECTIVE: To localize and quantify geometric morphometric differences in facial soft tissue morphology in adults with and without OSA.
MATERIALS AND METHODS: Eighty adult Malays, consisting of 40 patients with OSA and 40 non-OSA controls, were studied. Both groups were evaluated by the attending physician and through ambulatory sleep studies. 3-D stereophotogrammetry was used to capture facial soft tissues of both groups. The 3-D mean OSA and control facial configurations were computed and subjected to principal components analysis (PCA) and finite-element morphometry (FEM).
RESULTS: The body mass index was significantly greater for the OSA group (32.3 kg/m(2) compared to 24.8 kg/m(2), p < 0.001). The neck circumference was greater for the OSA group (42.7 cm compared to 37.1 cm, p < 0.001). Using PCA, significant differences were found in facial shape between the two groups using the first two principal components, which accounted for 50% of the total shape change (p < 0.05). Using FEM, these differences were localized in the bucco-submandibular regions of the face predominantly, indicating an increase in volume of 7-22% (p < 0.05) for the OSA group.
CONCLUSION: Craniofacial obesity in the bucco-submandibular regions is associated with OSA and may provide valuable screening information for the identification of patients with undiagnosed OSA.
METHODS: A cross-sectional study was carried out in the sleep clinic. Standard forward-backward method was used for translation. Patients were required to answer a translated version of the questionnaire in Bahasa Malaysia and underwent a PSG study. Apnea-hypopnea index (AHI) of five and more was considered diagnostic. SBQ score was divided into two groups, less than 3 and 3 or more to determine its correlation with mild, moderate, or severe OSA. The reliability of the questionnaire was compared against that of the PSG result.
RESULTS: We recruited 134 patients with mean age of 41.22 ± 12.66 years old. 9.7% patients have low risk, 48.5% moderate risk, and 41.8% high risk of OSA by SBQ scoring. 28.4% of patients had mild, 33.6% had moderate, and 38.0% had severe OSA by PSG. The Bahasa Malaysia version had sensitivity, specificity, and positive and negative predictive value of 61.42, 71.05, and 84.06 and 41.54% respectively. When the score is higher, the probability increases for patients to have moderate or severe OSA. SBQ score showed moderate value of agreement to AHI.
CONCLUSIONS: The Bahasa Malaysia version of SBQ is a valid tool for the identification of OSA. It is useful to detect patients at risk for further investigation and management.
METHODS: This retrospective study included a total of 941 surgical patients who had a pre-operative home sleep study. The pre-operative CBC was extracted from the electronic patient records. Patients were stratified according to their AHI scores, into mild (AHI ≥ 5 -
METHODS: A prospective cross-sectional study was performed among young doctors less than 40 years old, working at King Chulalongkorn Memorial Hospital, Bangkok, Thailand, and Hospital Kuala Lumpur, Kuala Lumpur, Malaysia, using questionnaires and home sleep apnea testing (Apnealink™Plus). The primary objective of this study was to evaluate the prevalence of OSA (apnea-hypopnea index (AHI) ≥5). The secondary objectives were to evaluate the prevalence of obstructive sleep apnea syndrome (OSAS) defined by AHI ≥5 + excessive daytime sleepiness (EDS), sleep deprivation (the difference of weekend (non-workdays) and weekday (workdays) wake-up time of at least 2 h), EDS (Epworth Sleepiness Scale score ≥10), tiredness, and perception of inadequate sleep as well as to identify their predictors.
RESULTS: Total of 52 subjects completed the study. Mean age and mean body mass index (BMI) were 31.3 ± 4 and 23.3 ± 3.6, respectively. The prevalence of OSA and OSAS were 40.4 and 5.8 %, respectively. One third of OSA subjects were at least moderate OSA. Prevalence of sleep deprivation, EDS, tiredness, and perception of inadequate sleep were 44.2, 15.4, 65.4, and 61.5 %, respectively. History of snoring, being male, and perception of inadequate sleep were significant predictors for OSA with the odds ratio of 34.5 (p = 0.016, 95 % CI = 1.92-619.15), 18.8 (p = 0.001, 95 % CI = 3.10-113.41), and 7.4 (p = 0.037, 95 % CI = 1.13-48.30), respectively. Only observed apnea was a significant predictor for OSAS with odds ratio of 30.7 (p = 0.012, 95 % CI = 2.12-442.6). Number of naps per week was a significant predictor for EDS with the odds ratio of 1.78 (p = 0.007, 95 % CI = 1.17-2.71). OSA and total number of call days per month were significant predictors for tiredness with the odds ratio of 4.8 (p = 0.036, 95 % CI = 1.11-20.72) and 1.3 (p = 0.050, 95 % CI = 1.0004-1.61), respectively. OSA was the only significant predictor for perception of inadequate sleep with the odd ratios of 4.5 (p = 0.022, 95 % CI = 1.24-16.59).
CONCLUSIONS: Our results demonstrated relatively high prevalence of OSA and OSAS among young doctors. Snoring, being male, and perception of inadequate sleep were significant predictors for OSA. Observed apnea was a significant predictor for OSAS. OSA was a significant predictor for tiredness and perception of inadequate sleep.
METHODS: A cross-sectional survey was conducted among physicians who were currently working in primary care clinics in the capital state of Kuala Lumpur. The validated "Obstructive Sleep Apnea Knowledge and Attitudes Questionnaire" (OSAKA) and nine additional practice questions were used as the survey instrument.
RESULTS: Of 207 physicians queried, the response rate was 100%. The mean (± SD) total knowledge score was 11.6 (± 2.8) (range 1-18). The majority of respondents had a positive attitude towards the importance of OSA but lacked confidence in managing OSA. Primary care doctors' most common practice for patients with suspected OSA was referral to the ear, nose, and throat (ENT) clinic.
CONCLUSIONS: The study shows that primary care doctors demonstrated adequate knowledge about OSA and were aware of the importance of OSA as a core clinical problem. However, only a minority felt confident in managing patients with OSA. The results of the study may encourage improvement of primary care doctors' efforts to prevent and manage OSA.
METHODS: Two blinded reviewers searched PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases, then selected and graded the risk of bias of observational studies of adults (≥ 18 years) comparing the diagnostic performance of AI algorithms using craniofacial photographs, versus conventional OSA diagnostic criteria (i.e. apnea-hypopnea index [AHI]). Studies were excluded if they detected apneic events without diagnosing OSA. AI models evaluated with a random split test set or k-fold cross-validation were included in a Bayesian bivariate meta-analysis.
RESULTS: From 5,147 records, 6 studies were included, containing 10 AI models trained/tested on 1,417/983 participants. The risk of bias was low. AI trained on craniofacial photographs achieved a pooled 84.9% sensitivity (95% credible interval [95% CrI]: 77.1-90.7%) and 71.2% specificity (95% CrI: 60.7-81.4%). Bayesian meta-regression identified deep learning (convolutional neural networks) as the most accurate AI algorithm (91.1% sensitivity, 79.2% specificity) comparable to home sleep apnea tests. AHI cutoffs, OSA prevalence, feature engineering, input data, camera type and informativeness of Bayesian prior did not alter diagnostic accuracy. There was no substantial publication bias.
CONCLUSION: AI trained on craniofacial photographs have high diagnostic accuracy and should be considered as a low-cost OSA screening tool. Future work focused on deep learning using smartphone images could improve the feasibility of this approach in primary care.