Supplement, 2017 n=300) is a case control study 1:1 matched by age (±5 years), sex, race and body mass index (BMI±5kg/m 2 ) including those >18 years with paroxysmal AF (PAF) and controls without AF. Participants underwent administration of STOP-Bang, NoSAS (neck circumference, obesity, snoring and sex), Berlin and Epworth Sleepiness Scale (ESS) questionnaires and16-channel research-grade polysomnography. We examined questionnaire diagnostic performance characteristics for moderate to severe OSA (apnea hypopnea index≥15) separately in PAF and without, including area under the curve (AUC, 95% confidence intervals). Analyses were performed in SAS software (version 9.4; Cary, NC). Results: The analytic sample was comprised of 300 participants (n=150 cases and n=150 controls): age 61.9 ± 11.9 years, 63.3% male, and BMI 31.4 ± 6.7 kg/m 2 . Sensitivity for the 4 questionnaires was lower, albeit comparable, in PAF (range: 52-79%) versus controls (range: 61-75%). NoSAS showed highest sensitivity in PAF (79%). Specificity range was overall lower in PAF (43-60%) versus controls (56-80%) The positive predictive value range was lower in PAF (23-27%) versus controls (54-72%). Conversely, the negative predictive value range was higher in PAF (84-89%) versus controls (63-75%). The AUC was lower in PAF versus controls except comparable for STOP-BANG (0.66, 0.56-0.77 versus 0.65, 0.57-0.74) and higher for NoSAS (0.79, 0.72-0.86 versus 0.64, 0.53-0.75) respectively. Conclusion: In this systematic assessment of standard OSA screening instruments, the NoSAS questionnaire performed most optimally in terms of sensitivity and reasonable discriminative ability of moderate to severe OSA detection in those with PAF. Further investigation is needed to identify effective OSA screening strategies with focused efforts on development/refinement of novel OSA screening tools in the AF population. Support (If Any):
Conclusion: In this sample of stroke patients, there was no significant correlation between BMI and severity of SDB. Also, 55% of patients with BMI less than 25 were diagnosed with SDB. A large proportion of female stroke patients also had SDB. Body weight and male gender do not appear to predict presence or absence of SDB or severity of SDB in those with stroke. Pre-existing anatomical and neuromuscular mechanics consequent to stroke may play a role in respiratory obstruction in this group. Preliminary data indicate that OSA may contribute to functional impairment following an acute stroke. Persistent OSA may also be a risk factor for the occurrence of recurrent stroke. However, the prevalence of OSA in the non-acute setting following stroke has not been well explored. We hypothesized that the risk for stroke would remain high in the non-acute post-stroke setting. Methods: Clinical data was gathered on 92 consecutive post-stroke outpatients at a specialty stroke clinic in an urban academic center between January and September 2016. The follow up dates were scheduled approximately 1 month after the initial admission for stroke. Patients completed the STOP-BANG OSA risk assessment tool at the time of visit. We also collected data on demographics, comorbidities, length of hospital stay, and type and severity of stoke. Results: Average age of the patients was 64.18 years. 51% of patients were male. The mean NIH stroke scale was 8.4. 70 patients (76.1%) were screened positive in the Stop-bang questionnaire. There were more males who were STOP-BANG positive (61% vs 18%) and they had a higher BMI (32.22 vs 26.27, p-0.003). The average age was similar (65.58 vs 59.72 years, p-value 0.07). Comorbidities including incidence of hypertension (78.6% vs 54.5%, p-0.05), diabetes (32.8% vs 22.7%, p-0.43), atrial fibrillation (18.5% vs 13.6%, p-0.75), h/o smoking (48.6% vs 50%, p-1.00), heart failure (12.8% vs 4.5%, p-0.44) and h/o previous stroke (28.6% vs 9.1% p-value 0.08) were identical in both the groups. The severity of stroke (average NIH stroke scale 9.39 vs 5.4, p-0.42), length of stay (average 4.375 vs 2.94 days p-0.1194) and place of discharge (9 vs 6, patients were discharged to rehab p-1.00) were comparable. The incidence of embolic and hemorrhagic stroke was similar (27% vs 22% and 8% vs 4% respectively). Conclusion:The risk of OSA, remains high at one month following discharge from hospitalization for acute stroke. In this pilot study, only BMI and gender were independently associated with risk of having OSA. Support (If Any):
Introduction Initial download and use of sleep tracking is very high, but prolonged use is very low. Poor prolonged use may be attributable to several factors such as engagement, functionality, aesthetics, information, and recommendation. We appraised these five factors in 16 consumer- and research/medical- grade digital sleep devices. Methods Three reviewers independently assessed 16 consumer- and medical-grade sleep digital devices using the Mobile Application Rating Scale (MARS) App quality ratings, which measures engagement (engagement, entertainment, interest, customization, interactivity, target group), functionality (functionality, performance, ease of use, navigation, gestural design), aesthetics (layout, graphics, visual appeal), information (Accuracy. Goals, Quality of information, Quantity of information, Visual information, Credibility, and Evidence base) and recommended on a Likert scale, with 1- Inadequate to 5 Excellent. Each subcategory is rated on a 1-5 Likert scale which is summed for each category: engagement (30), functionality (25), aesthetics (15), information (35) and recommended (yes or no). Results Devices that had the highest engagement score were Fitbit (27), Apple Watch (27), Garmin (27), and Dreem 2 headband (25.5). Apple Watch (30) had highest score; while Fitbit (13), Apple Watch (13), Garmin (13), Samsung Gear (13) had highest aesthetic score. While for information, ActiGraph (35), SOMNOwatch plus (35), CleveMed SleepView Monitor (35), CleveMed Sapphire PSG (35), SOMNOscreen plus (35), Nox T3 Sleep Monitor (35) and Nox A1 PSG System (35) had the highest ratings. The Dreem 2 headband has the potential induce prolong use among users with and without sleep disorders, based on high scores on engagement (25.5), Functionality (20.5), and Information (26.5). Conclusion Consumer- and research-grade digital devices that measure sleep have varying levels of engagement, functionality, aesthetics, information and recommendations to facilitate prolong use. Consumer grade devices had higher engagement, functionality and aesthetics scores, while research grade devices had higher information and recommendation scores. If consumer- and research-grade devices are to have prolonged use, standardization is needed across the five MARS components. Support K01HL135452, R01MD007716, R01HL142066, and K07AG052685
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