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Record W4378610504 · doi:10.1093/sleep/zsad077.0551

0551 Predicting Daytime Alertness Impairment in Obstructive Sleep Apnea

2023· article· en· W4378610504 on OpenAlexaff
Ali Warda, Victoria Mariah Lennox, Tu Nguyen, Shanta Ghosh, Sanjib Basu, Magdy Younes, David W. Carley, Bharati Prasad, Bethany Gerardy

Bibliographic record

VenueSLEEP · 2023
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsResearch ManitobaUniversity of Manitoba
Fundersnot available
KeywordsAlertnessEpworth Sleepiness ScalePolysomnographyObstructive sleep apneaMedicineExcessive daytime sleepinessPsychomotor vigilance taskBody mass indexAudiologyApneaPhysical therapyPsychologyCardiologyInternal medicineSleep disorderSleep deprivationCircadian rhythmPsychiatryInsomnia

Abstract

fetched live from OpenAlex

Abstract Introduction Obstructive sleep apnea (OSA) reduces daytime alertness leading to social and occupational impairment. The Maintenance of Wakefulness Test (MWT), a gold-standard test for daytime alertness, is time-consuming and not routinely available in clinical practice. We examined EEG-based and clinical parameters derived from diagnostic polysomnography (PSG) as predictors of alertness impairment in adults with OSA. Methods Eighty-two participants with untreated OSA on diagnostic PSG (apnea hypopnea index, AHI ≥5/hour), enrolled in a two-center clinical trial were included. The day following PSG, each participant completed four 40-minute MWT and four 10-minute Psychomotor Vigilance Tests (PVT). The outcomes were MWT mean sleep latency (MSL) and PVT lapses (reaction time ≥500 milliseconds, square root transformed). Three multiple linear regression models were used: 1. “standard predictors” (total sleep time, TST, percent REM and slow wave sleep, AHI, time < 90% oxygen saturation, and arousal index), 2. “novel predictors” derived by spectral analysis and averaged across right and left central EEG (normalized EEG power, odds ratio product; ORP parameters, and spindle characteristics), and 3. combined standard and novel predictors. Age, sex, and body mass index (BMI) were included in each model, and backward elimination (step AIC) was used for models 2 and 3. Results Participants were middle-aged (mean ± standard deviation, SD 53.5±8.7 years), with moderate OSA (AHI 33.8±18.2) and daytime sleepiness (Epworth Sleepiness Scale 11.7±3.9), and 86% were men. The adjusted r2 for MSL were: model 1=0.09, model 2=0.16, and model 3=0.27, and PVT lapses were: model 1=0.09, model 2=0.31, and model 3=0.34. For MSL, the significant predictors in model 3 included TST (t-statistic -3.59, p=0.0007), REM% (2.51, p=0.01), ORP-9 (2.95, p=0.004), ORP NREM (-3.15, p=0.002), spindle frequency (-2.95, p=0.004), and normalized EEG power (-2.59, p=0.01). For PVT lapses, model 3 predictors included 2.25-2.5 ORP% (-2.91, p=0.005), normalized EEG power (3.22, p=0.002), spindle power (-4.30, p< 0.0001), spindle frequency (2.83, p=0.006), and sex (men vs. women, -2.56, p=0.01). Conclusion We have identified several novel PSG EEG-based predictors of impaired daytime alertness in OSA that will improve occupational evaluation and prognostication in OSA after future validation. Support (if any) National Institutes of Health, UM1-HL112856, UL1TR001422, and UL1TR002003.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.303
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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