Perception of sleep duration in adult patients with suspected obstructive sleep apnea.

PURPOSE: Discrepancies between subjective and objective measures of total sleep time (TST) are frequent among insomnia patients, but this issue remains scarcely investigated in obstructive sleep apnea (OSA). We aimed to evaluate if sleep perception is affected by the severity of OSA. METHODS: We performed a 3-month cross-sectional study of Brazilian adults undergoing overnight polysomnography (PSG). TST was objectively assessed from PSG and by a self-reported questionnaire (subjective measurement). Sleep perception index (SPI) was defined by the ratio of subjective and objective values. Diagnosis of OSA was based on an apnea/hypopnea index (AHI) ≥ 5.0/h, being its severity classified according to AHI thresholds: 5.0-14.9/h (mild OSA), 15.0-29.9/h (moderate OSA), and ≥ 30.0/h (severe OSA). RESULTS: Overall, 727 patients were included (58.0% males). A significant difference was found in SPI between non-OSA and OSA groups (p = 0.014). Mean SPI values significantly decreased as the OSA severity increased: without OSA (100.1 ± 40.9%), mild OSA (95.1 ± 24.6%), moderate OSA (93.5 ± 25.2%), and severe OSA (90.6 ± 28.2%), p = 0.036. Using logistic regression, increasing SPI was associated with a reduction in the likelihood of presenting any OSA (p = 0.018), moderate/severe OSA (p = 0.019), and severe OSA (p = 0.028). However, insomnia was not considered as an independent variable for the presence of any OSA, moderate/severe OSA, and severe OSA (all p-values > 0.05). CONCLUSION: In a clinical referral cohort, SPI significantly decreases with increasing OSA severity, but is not modified by the presence of insomnia symptoms.

Nasal versus oronasal mask in patients under auto-adjusting continuous positive airway pressure titration: a real-life study.

PURPOSE: Mask type (nasal versus oronasal) can affect the optimal pressure required to correct the apnea/hypopnea index (AHI) in obstructive sleep apnea (OSA) subjects treated with CPAP. Our objective was to evaluate if mask type influenced CPAP titration outcomes in OSA patients. METHODS: A retrospective study of individuals with a baseline AHI ≥ 15.0/h, who received an auto-adjusting CPAP titrating device (S9 AutoSet ResMed®) in a sleep-lab setting. The mask type oronasal (OM) or nasal (NM) was always selected by the patients. Optimal pressure requirements, leak, and residual AHI were compared based on mask type. RESULTS: Overall, 436 patients were included: 283 with NM (64.9%) and 153 with OM (35.1%). At baseline, NM and OM cohorts had similar AHI (p = 0.160). Patients allocated to the OM cohort had a higher 95th percentile pressure, a higher 95th percentile leak, and a higher residual AHI than those with a NM: pressure requirement: 12.9 cm H2O (IQR: 10.6-15.0) versus 10.7 cm H2O (IQR: 9.2-12.3); leak: 21.6 L/min (IQR: 9.6-37.2) versus 9.6 L/min (IQR: 3.6-19.2); and residual AHI: 4.9/h (IQR: 2.4-10.2) versus 2.2/h (IQR: 1.0-4.4), respectively (p < 0.001 for all). CONCLUSIONS: CPAP mask type based on individual preferences exerts profound effects on optimal CPAP pressures and efficacy. Patients titrated with OM showed higher pressure requirements, had higher a leak, and higher residual AHI when compared to NM, which may adversely impact treatment adherence and other health outcomes.

Obstructive Sleep Apnea Screening with a 4-Item Instrument, Named GOAL Questionnaire: Development, Validation and Comparative Study with No-Apnea, STOP-Bang, and NoSAS.

BACKGROUND: Obstructive sleep apnea (OSA) is a very prevalent disorder. Here, we aimed to develop and validate a practical questionnaire with yes-or-no answers, and to compare its performance with other well-validated instruments: No-Apnea, STOP-Bang, and NoSAS. METHODS: A cross-sectional study containing consecutively selected sleep-lab subjects underwent full polysomnography. A 4-item model, named GOAL questionnaire (gender, obesity, age, and loud snoring), was developed and subsequently validated, with item-scoring of 0-4 points (≥2 points indicating high risk for OSA). Discrimination was assessed by area under the curve (AUC), while predictive parameters were calculated using contingency tables. OSA severity was classified based on conventionally accepted apnea/hypopnea index thresholds: ≥5.0/h (OSA≥5), ≥15.0/h (OSA≥15), and ≥30.0/h (OSA≥30). RESULTS: Overall, 7377 adults were grouped into two large and independent cohorts: derivation (n = 3771) and validation (n = 3606). In the derivation cohort, screening of OSA≥5, OSA≥15, and OSA≥30 revealed that GOAL questionnaire achieved sensitivity ranging from 83.3% to 94.0% and specificity ranging from 62.4% to 38.5%. In the validation cohort, screening of OSA≥5, OSA≥15, and OSA≥30, corroborated validation steps with sensitivity ranging from 83.7% to 94.2% and specificity from 63.4% to 37.7%. In both cohorts, discriminatory ability of GOAL questionnaire for screening of OSA≥5, OSA≥15, and OSA≥30 was similar to No-Apnea, STOP-Bang or NoSAS. CONCLUSION: All four instruments had similar performance, leading to a possible greater practical implementation of the GOAL questionnaire, a simple instrument with only four parameters easily obtained during clinical evaluation.

Predicting Obstructive Sleep Apnea in Patients with Insomnia: A Comparative Study with Four Screening Instruments.

PURPOSE: Obstructive sleep apnea (OSA) and insomnia are very prevalent disorders, especially in sleep-lab setting, and insomnia may be the presenting complaint of OSA. Here, we aimed to validate No-Apnea as screening tool for OSA in patients with self-reported insomnia complaints and to compare its performance with other models. METHODS: This cross-sectional study involved evaluation of No-Apnea as well as STOP-Bang, NoSAS and Epworth Sleepiness Scale (ESS) in subjects with insomnia being evaluated with full in-lab polysomnography. Discrimination was assessed by area under the curve (AUC), while predictive parameters were calculated by contingency tables. OSA severity was classified based on the apnea/hypopnea index: ≥ 5.0/h as any OSA (OSA≥5), ≥ 15.0/h as moderate/severe OSA (OSA≥15), and ≥ 30.0/h as severe OSA (OSA≥30). RESULTS: Overall, 2591 patients with a clinical diagnosis of insomnia were included. Diagnosis of OSA≥5, OSA≥15, and OSA≥30 was of 76.3%, 53.1%, and 32.6%, respectively. At all levels of OSA severity, No-Apnea had sensitivity ranging from 84.5 to 94.1% and specificity ranging from 58.2 to 35.1%. For screening of OSA≥5, OSA≥15, and OSA≥30, discriminatory ability (AUC) of No-Apnea was: 0.790 [95% confidence interval (CI) 0.770-0.810], 0.758 (95% CI 0.740-0.777), and 0.753 (95% CI 0.734-0.772), respectively. Based on AUCs, No-Apnea, STOP-Bang, and NoSAS performed similar at all levels of OSA severity. The ESS did not present satisfactory discrimination as OSA screening model. CONCLUSIONS: In a large sample of patients with insomnia, No-Apnea, STOP-Bang, and NoSAS, but not ESS, enable satisfactory and similar discrimination at all levels of OSA severity.

Comparative performance of screening instruments for obstructive sleep apnea in morbidly obese patients referred to a sleep laboratory: a prospective cross-sectional study.

PURPOSE: Obstructive sleep apnea (OSA) is very common occurrence among morbidly obese patients. Our main objectives were to validate the No-Apnea, a 2-item screening tool, in morbidly obese patients and compare its performance with three other instruments: STOP-Bang questionnaire, NoSAS score, and Epworth Sleepiness Scale (ESS). METHODS: A cross-sectional analysis of morbidly obese patients (body mass index [BMI] ≥ 35.0 kg/m2) grouped into two independent samples: bariatric surgery patients (BS) and non-bariatric surgery patients (NBS). All patients underwent overnight polysomnography. Discriminatory ability was assessed by area under the curve (AUC). OSA severity was defined by apnea/hypopnea index cut-off points: ≥ 5.0/h (OSA≥5), ≥ 15.0/h (OSA≥15), and ≥ 30.0/h (OSA≥30). RESULTS: A total of 1017 subjects (40.4% in BS cohort and 59.6% in NBS cohort) were evaluated. In the BS cohort, No-Apnea had similar discrimination to STOP-Bang and NoSAS for predicting OSA≥5 (p = 0.979 and p = 0.358, respectively), OSA≥15 (p = 0.158 and p = 0.399, respectively), and OSA≥30 (p = 0.388 and p = 0.903, respectively). In the NBS cohort, No-Apnea had similar discrimination to STOP-Bang and NoSAS for predicting OSA≥5 (p = 0.528 and p = 0.428, respectively), OSA≥15 (p = 0.825 and p = 0.108, respectively), and OSA≥30 (p = 0.458 and p = 0.186, respectively). Moreover, No-Apnea performed significantly better than ESS in both BS and NBS cohorts (p < 0.001). CONCLUSIONS: No-Apnea is a useful and practical tool for screening of OSA in morbidly obese patients, with non-inferior performance to STOP-Bang questionnaire and NoSAS score.

Fractional Exhaled Nitric Oxide Measurements and Screening of Obstructive Sleep Apnea in a Sleep-Laboratory Setting: A Cross-Sectional Study.

PURPOSE: Obstructive sleep apnea (OSA) is a common condition characterized by repetitive collapse of the upper airways and intermittent oxygen desaturation, which may lead to airway inflammation. Here, we explored whether fractional exhaled nitric oxide (FeNO) levels provide a non-invasive screening tool of OSA. METHODS: Over a 3-month period, FeNO levels were measured in consecutive non-smoking patients referred for a sleep laboratory. All patients underwent full polysomnography. OSA severity was classified based on the apnea/hypopnea index: ≥ 5.0/h as any OSA, ≥ 15.0/h as moderate/severe OSA, and ≥ 30.0/h as severe OSA. FeNO was measured by a portable device (NIOX-MINO®; Aerocrine AB, Solna, Sweden) and expressed as parts per billion (ppb). Discrimination by area under the curve (AUC) and binary logistic regression were performed. RESULTS: A total of 229 subjects were evaluated. Mean FeNO values were similar among subjects without OSA or with OSA: 16.9 ± 10.6 ppb versus 20.2 ± 14.5 ppb, p = 0.221; respectively. FeNO was not an inclusionary parameter to predict any OSA, moderate/severe OSA, and severe OSA: odds ratio (OR) 1.023 (95% confidence interval [CI]: 0.986-1.062); OR 1.012 (95% CI: 0.991-1.034); and OR 0.999 (95% CI: 0.980-1.018), respectively. The AUC values for FeNO in the diagnosis of any OSA, moderate/severe OSA, and severe OSA showed no discriminatory properties: AUC: 0.567 (95% CI: 0.464-0.670), AUC: 0.541 (95% CI: 0.465-0.618), and AUC: 0.535 (95% CI: 0.459-0.610); respectively. CONCLUSIONS: In a sleep-lab setting, our findings suggest that FeNO measurements are inconsequential in the screening of OSA in adults.

Simplifying the Screening of Obstructive Sleep Apnea With a 2-Item Model, No-Apnea: A Cross-Sectional Study.

STUDY OBJECTIVES: To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria, and then compare it with two widely used tools, namely STOP-BANG and NoSAS. METHODS: This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of the new instrument. RESULTS: A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035). A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0-9 points). We used the cutoff ≥ 3 to classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/ severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15, and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5, OSA-15, and OSA-30. Subsequently, the model was validated confirming its reproducibility. In both cohorts, No-Apnea discrimination was similar to STOP-BANG or NoSAS. CONCLUSIONS: The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar performance characteristics.