MATERIALS AND METHODS: This was a cross-sectional, single center study. A total of 110 subjects between 18 to 65 years of age and diagnosed with OSA following sleep study examinations were recruited. Exclusion criteria included seropositive Hepatitis B or Hepatitis C, and significant alcohol intake.
RESULT: The prevalence of NAFLD was 81.8%. The mean CIMT (0.08±0.03 vs 0.06±0.01 cm, p = 0.001), ICAM-1 (334.53±72.86 vs 265.46±102.92 ng/mL, p = 0.001) and Lp(a) (85.41±52.56 vs 23.55±23.66 nmol/L, p<0.001) were significantly higher in the NAFLD group compared to the non-NAFLD group. Comparisons between the different groups showed significantly increasing levels of CIMT, ICAM-1 and Lp(a), lowest within the non-NAFLD, followed by the NAFLD 1 and NAFLD 2+3 groups. There was a significant positive correlation between degree of steatosis and the severity of OSA (r = 0.453, p<0.001). Logistic regression analysis revealed that patients with apnea/hypopnea index (AHI) of >30 were 52.77 (CI 6.34, 439.14) times more likely to have NAFLD compared to those with mild AHI (p<0.001).
CONCLUSION: The prevalence of NAFLD is alarmingly high in this group of OSA patients. The degree of steatosis in patients with NAFLD was significantly correlated with severity of OSA, CIMT measurements, ICAM-1 and Lp(a). Our findings underscore screening for NAFLD in patients with OSA to ensure prompt risk stratification and management.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
METHODS: A cross-sectional study was carried out using a systematic random sampling method in hypertensive patients who attended two government primary care clinics in Sarawak. The STOP-Bang questionnaire was used to screen for OSA, and social-demographic data was captured with a questionnaire. Multiple logistic regressions were used to examine the determinants of the OSA.
RESULTS: A total of 410 patients were enrolled in this study. The mean age of study population patients was 56.4 years, with more than half being female. The mean blood pressure was 136/82. The prevalence of probable OSA among patients with hypertension was 54.4%. According to multiple logistic regression analyses, smoking (odds ratio [OR] 14.37, 95% confidence interval [CI] 3.335-61.947), retirees (OR 3.20, 95% CI 1.675-6.113), and being Chinese (OR 2.21, 95% CI 1.262-3.863) had a significant positive association with probable OSA.
CONCLUSIONS: Because of the high prevalence of probable OSA among patients with hypertension, primary care physicians should be more vigilant in identifying hypertensive patients with OSA risk. Early detection and intervention would reduce disease complications and healthcare costs.
METHODS: The patients included in this study were those recently diagnosed with OSA (AHI > 5) and given nCPAP therapy, as well as being referred to a sleep laboratory for an assessment of their sleep disordered breathing. Prior to the start of nCPAP therapy and polysomnography evaluation, patients were asked to complete the validated Quebec sleep questionnaire (QSQ), and their baseline measurements were recorded.
RESULTS: Among the study population, 14.41% (n = 31) had mild OSA with an apnea and hypopnea index of 5 to 14.9 events/h, while 26.97% (n = 58) had moderate OSA and 40% (n = 86) had severe OSA. The overall average apnea and hypopnea index of the study population was 30.24 ± 9.73 events/h; mild OSA patients had an average apnea and hypopnea index of 10.09 ± 2.65 events/h, moderate OSA patients had 21.48 ± 4.40 events/h, and severe OSA patients had 59.16 ± 22.14 events/h. A significant difference was observed between the scores before treatment and after 6 months of therapy in all domains of the QSQ QoL scores (P
MATERIALS AND METHODS: A total of 44 patients were included after evaluation for sleep disorders and were divided into four groups in accordance with apnea-hypopnea index (AHI). Pure tone audiometry (PTA), distortion product otoacoustic emission (DPOAE) and auditory brainstem response (ABR) were compared in commensurate with the severity of AHI. Polysomnography oximetry parameters of oxygen desaturation index, mean SPO2, minimum SPO2 and percent SPO2
METHODS: After institutional approval and written informed consent, patients received a brief remifentanil infusion during continuous monitoring of ventilation. We compared minute ventilation in 30 patients with moderate-to-severe obstructive sleep apnea diagnosed by polysomnography and 20 controls with no to mild obstructive sleep apnea per polysomnography. Effect site concentrations were estimated by a published pharmacologic model. We modeled minute ventilation as a function of effect site concentration and the estimated carbon dioxide. Obstructive sleep apnea status, body mass index, sex, age, use of continuous positive airway pressure, apnea/hypopnea events per hour of sleep, and minimum nocturnal oxygen saturation measured by pulse oximetry in polysomnography were tested as covariates for remifentanil effect site concentration at half-maximal depression of minute ventilation (Ce50) and included in the model if a threshold of 6.63 (P < 0.01) in the reduction of objective function was reached and improved model fit.
RESULTS: Our model described the observed minute ventilation with reasonable accuracy (22% median absolute error). We estimated a remifentanil Ce50 of 2.20 ng · ml (95% CI, 2.09 to 2.33). The estimated value for Ce50 was 2.1 ng · ml (95% CI, 1.9 to 2.3) in patients without obstructive sleep apnea and 2.3 ng · ml (95% CI, 2.2 to 2.5) in patients with obstructive sleep apnea, a statistically nonsignificant difference (P = 0.081). None of the tested covariates demonstrated a significant effect on Ce50. Likelihood profiling with the model including obstructive sleep apnea suggested that the effect of obstructive sleep apnea on remifentanil Ce50 was less than 5%.
CONCLUSIONS: Obstructive sleep apnea status, apnea/hypopnea events per hour of sleep, or minimum nocturnal oxygen saturation measured by pulse oximetry did not influence the sensitivity to remifentanil-induced ventilatory depression in awake patients receiving a remifentanil infusion of 0.2 μg · kg of ideal body weight per minute.
METHODS: PubMed, Cochrane CENTRAL, and EMBASE were searched for randomized trials comparing pharmacologic or surgical obesity interventions to usual care, placebo, or no treatment in adults with OSA. The association between percentage weight loss and AHI change between randomization and last follow-up was evaluated using meta-regression.
PROSPERO: CRD42022378853.
RESULTS: Ten eligible trials (n = 854 patients) were included. Four (n = 211) assessed bariatric surgery, and 6 (n = 643) assessed pharmacologic interventions over a median follow-up of 13 months (interquartile range 6-26 months). The linear best estimate of the change in AHI is 0.45 events per hour (95% Confidence Interval 0.18 to 0.73 events per hour) for every 1% body weight lost.
CONCLUSIONS: Weight loss caused by medication or surgery caused a proportionate improvement of the AHI. Providers could consider extrapolating from this relationship when advising patients of the expected effects of other pharmacologic or surgical interventions without direct evidence in OSA.
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: This was a multicenter prospective cohort study involving patients with cardiovascular risk factors who were undergoing major noncardiac surgery. Patients underwent home sleep apnea testing. All patients completed the STOP-Bang questionnaire. The predictive parameters of STOP-Bang scores were calculated against the apnea-hypopnea index.
RESULTS: From 4 ethnic groups 1,205 patients (666 Chinese, 161 Indian, 195 Malay, and 183 Caucasian) were included in the study. The mean BMI ranged from 25 ± 4 to 30 ± 6 kg/m² and mean age ranged from 64 ± 8 to 71 ± 10 years. For the Chinese and Indian patients, diagnostic parameters are presented using BMI threshold of 27.5 kg/m² with the area under curve to predict moderate-to-severe OSA being 0.709 (0.665-0.753) and 0.722 (0.635-0.808), respectively. For the Malay and Caucasian, diagnostic parameters are presented using BMI threshold of 35 kg/m² with the area under curve for predicting moderate-to-severe OSA being 0.645 (0.572-0.720) and 0.657 (0.578-0.736), respectively. Balancing the sensitivity and specificity, the optimal STOP-Bang thresholds for the Chinese, Indian, Malay, and Caucasian groups were determined to be 4 or greater.
CONCLUSIONS: For predicting moderate-to-severe OSA, we recommend BMI threshold of 27.5 kg/m² for Chinese and Indian patients and 35 kg/m² for Malay and Caucasian patients. The optimal STOP-Bang threshold for the Chinese, Indian, Malay and Caucasian groups is 4 or greater.
CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Postoperative Vascular Events in Unrecognized Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/study/NCT01494181; Identifier: NCT01494181.
METHODS: This is a cross-sectional study involving 27 patients with symptoms of OSAS seen at a tertiary institutional center and 25 normal controls performed between June 2015 and June 2016. All patients and controls underwent a polysomnography (PSG) test and were diagnosed with OSAS based on the apnea-hypopnea index (AHI). Patients are those with OSAS symptoms and had AHI > 5, whereas controls are staffs from the ophthalmology clinic without clinical criteria for OSAS and had PSG result of AHI
DESIGN AND SETTINGS: This was a cross-sectional study to examine the association between OSA parameters and IR using homeostasis model assessment (HOMA) on patients who underwent polysomnogram (PSG) in a tertiary center between March 2011 and March 2012 (1 year).
PATIENTS AND METHODS: A total of 62 patients underwent PSG within the study period, of which 16 patients were excluded due to abnormal fasting blood sugar. Information on patients' medical illnesses, medications, and Epworth sleepiness scale (ESS) was obtained. Patients' body mass index (BMI), neck circumference, and waist circumference (WC) were measured. Blood samples were collected after 8 hours of fasting to measure HOMA-IR value. Overnight PSG was performed for all patients. Data was recorded and analyzed using SPSS, version 12.0 (SPSS Inc, Chicago, USA).
RESULTS: The prevalence of IR in OSA patients was 64.3%. There was significant correlation between OSA parameters (apnea-hypopnea index, ESS, BMI, and WC) and HOMA-IR with correlation coefficient of 0.529, 0.224, 0.261, and 0.354, respectively.
CONCLUSION: A linear correlation exists between OSA parameters and IR concluding a definite causal link between OSA and IR. IR screening is recommended in severe OSA patients.