Screening for obstructive sleep apnea among hospital outpatients.

BACKGROUND:Obstructive sleep apnea syndrome (OSAS) is common in adults. People with OSAS have a higher risk of experiencing traffic accidents and occupational injuries (OIs). We aimed to clarify the diagnostic performance of a three-channel screening device (ApneaLinkTM) compared with the gold standard of full-night attended polysomnography (PSG) among hospital outpatients not referred for sleep-related symptoms. Furthermore, we aimed to determine whether manual revision of the ApneaLinkTM autoscore enhanced diagnostic performance. METHODS:We investigated 68 patients with OI and 44 without OI recruited from the University Hospital Basel emergency room, using a cross-sectional study design. Participating patients spent one night at home with ApneaLinkTM and within 2 weeks slept for one night at the sleep laboratory. We reanalyzed all ApneaLinkTM data after manual revision. RESULTS:We identified significant correlations between the ApneaLinkTM apnea-hypopnea index (AHI) autoscore and the AHI derived by PSG (r = 0.525; p <0.001) and between the ApneaLinkTM oxygen desaturation index (ODI) autoscore and that derived by PSG (r = 0.722; p <0.001). The ApneaLinkTM autoscore showed a sensitivity and specificity of 82% when comparing AHI ≥5 with the cutoff for AHI and/or ODI ≥15 from PSG. In Bland Altman plots the mean difference between ApneaLinkTM AHI autoscore and PSG was 2.75 with SD ± 8.80 (β = 0.034), and between ApneaLinkTM AHI revised score and PSG -1.50 with SD ± 9.28 (β = 0.060). CONCLUSIONS:The ApneaLinkTM autoscore demonstrated good sensitivity and specificity compared with the gold standard (full-night attended PSG). However, Bland Altman plots revealed substantial fluctuations between PSG and ApneaLinkTM AHI autoscore respectively manually revised score. This spread for the AHI from a clinical perspective is large, and therefore the results have to be interpreted with caution. Furthermore, our findings suggest that there is no clinical benefit in manually revising the ApneaLinkTM autoscore

Obstructive sleep apnea syndrome and sleep disorders in individuals with occupational injuries

Abstract Background Some sleep disorders are known risk factors for occupational injuries (OIs). This study aimed to compare the prevalence of obstructive sleep apnea syndrome (OSAS) in a population of patients with OIs admitted to the emergency room (ER) with hospital outpatients as controls. Methods Seventy-nine patients with OIs and 56 controls were recruited at the University Hospital of Basel, Switzerland between 2009 and 2011. All patients completed a questionnaire and underwent a full-night attended polysomnography (PSG). We considered an apnea–hypopnea index (AHI) > 5 as an abnormal finding suggestive of a diagnosis of OSAS. Results Patients with OIs did not differ from controls regarding sex, age, body mass index, and job risk of OI. Patients with OIs tended to have an abnormal AHI (n = 38 [48%] vs. n = 16 [29%], odds ratio [OR] = 2.32 [95% confidence interval (CI):1.05–5.13]), and a higher AHI (8.0 vs. 5.6 events/h; Cohen’s d 0.28, p = 0.028) compared with controls. Patients with OIs also had abnormal limb movement index, arousal index, and signs of sleep bruxism compared with controls. Compared with 36 controls (66%), 70 patients with OIs (89%) had either excessive daytime sleepiness (EDS), and/or an abnormal finding during PSG (OR = 4.32, 95% CI:1.65–11.52). However, patients with OIs did not differ from controls regarding EDS or oxygen desaturation index. Conclusions Patients treated in the ER for OI had more abnormal findings suggestive of OSAS or other sleep disorders compared with a control group of hospital outpatients. Screening for these conditions should be part of the postaccident medical investigation

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

peer reviewedArousals during sleep are transient accelerations of the EEG signal, considered to reflectsleep perturbations associated with poorer sleep quality. They are typically detected by visualinspection, which is time consuming, subjective, and prevents good comparability across scorers,studies and research centres. We developed a fully automatic algorithm which aims at detectingartefact and arousal events in whole-night EEG recordings, based on time-frequency analysis withadapted thresholds derived from individual data. We ran an automated detection of arousals over35 sleep EEG recordings in healthy young and older individuals and compared it against humanvisual detection from two research centres with the aim to evaluate the algorithm performance.Comparison across human scorers revealed a high variability in the number of detected arousals,which was always lower than the number detected automatically. Despite indexing more events,automatic detection showed high agreement with human detection as reflected by its correlationwith human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleepstage did not influence performance, while age may impact automatic detection, depending on thehuman rater considered as gold standard. We propose our freely available algorithm as a reliable andtime-sparing alternative to visual detection of arousals

Sensitivity, specificity, positive predictive value and negative predictive value of ApneaLink^TM autoscore compared with polysomnography.

<p>Sensitivity, specificity, positive predictive value and negative predictive value of ApneaLink^TM autoscore compared with polysomnography.</p

Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Arousals during sleep are transient accelerations of the EEG signal typically detected by visual inspection of the sleep recording. Such visual identification is a time-consuming, subjective process that prevents comparability across scorers, studies and research centres. We developed an algorithm, which automatically detects arousals in whole-night EEG recordings, based on time and frequency analysis with adapted thresholds derived from individual data. We performed automatic arousals detection over 35 sleep recordings of young (µ=24.07±3, N=18) and older (µ=61.38±6, N=17) healthy individuals, and compared it against human raters (HR) detection from two research centres. We assessed performance of the automatic algorithm using generalized linear mixed models with Cohen’s kappa as dependent variable. Performance of automatic detection was compared to a gold standard, composed of either all arousals found by any of the HR (inclusive detection – ID) or only those common to both HR (conservative detection – CD). Comparison between human scorers revealed a high variability in the number of arousals detected (µ=71±32 vs 111±50). Although many more arousals were automatically detected (µ=200 ± 43), agreement of automatic detection against human detection was high, as reflected by very large Cohen’s kappa values (κ=.93 for ID, .94 for CD). Importantly, automatic detection was correlated to human detection (r=.38, p=.025 for CD). Algorithm performance was not significantly influenced by sleep stage (p=.74 for ID; p=.97 for CD), age (p=.12 for ID; p=.91 for CD) or sex (p=.10 for ID; p=.21 for CD). We further found that relative power in the theta and alpha bands were, respectively, higher and lower (p<.0001) for arousals that were only detected by the algorithm, arguably making them less obvious for the human eye. Our results show that the automated algorithm is performing at least equally as well as HR. While the automatic method detects most of HR events, it finds many more events that bear the characteristics of AASM arousals, but are missed by visual inspection of the EEG. This is seen for other micro-events detectors such as spindle detectors. In conclusion, our algorithm a reliable tool for automatic detection of arousals

Flow of study participants.

<p>OI, occupational injury; w/o, without; PSG, polysomnography.</p

Bland-Altman plot illustrating the difference in AHI as measured from ApneaLink^TM manually revised versus PSG.

<p><b>Plots present differences between the two methods compared to mean AHI of the two methods. The black solid line represents the mean difference, the black thin lines the 95% confidence intervals on the limits of agreement.</b> ALM, ApneaLink^TM manually revised; PSG, polysomnography.</p

Demographic and clinical characteristics of study participants.

<p>Demographic and clinical characteristics of study participants.</p

Sensitivity, specificity, positive predictive value, and negative predictive value of ApneaLink^TM after manual revision compared with polysomnography.

<p>Sensitivity, specificity, positive predictive value, and negative predictive value of ApneaLink^TM after manual revision compared with polysomnography.</p

Distribution of patients with and without an OI across AHI categories based on manually revised ApneaLink^TM score.

<p>OI, occupational injury; w/o, without; AHI, apnea-hypopnea index; CI, confidence interval.</p