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Record W4410502045 · doi:10.1093/sleep/zsaf090.1410

1410 Effect of CPAP on Cognitive Fog in Post COVID Patients with Obstructive Sleep Apnea

2025· article· en· W4410502045 on OpenAlexaboutno aff
Venkatesh Krishnamurthy, Kristine A. Wilckens, Pinar Garbioglu, Seema Vaidya, Steven Swanger, A Whitehead, Sanjay R. Patel

Bibliographic record

VenueSLEEP · 2025
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
Fundersnot available
KeywordsObstructive sleep apneaCoronavirus disease 2019 (COVID-19)MedicineCognitionContinuous positive airway pressureSleep (system call)2019-20 coronavirus outbreakSleep apneaSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AudiologyPediatricsInternal medicinePsychiatryDiseaseVirologyComputer scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Introduction Cognitive fog is a frequent complaint among patients suffering from post-COVID Condition, reported in up to 60% of patients. Obstructive sleep apnea (OSA) commonly co-occurs in post-COVID Condition and may exacerbate symptoms. We sought to evaluate if treatment of OSA with continuous positive airway pressure (CPAP)decreases cognitive fog in post-COVID patients with OSA. Methods Patients with post COVID Condition, as per World Health Organization criteria, with self-reported symptoms of cognitive fog and diagnosed with OSA were recruited. They underwent neuropsychological testing to evaluate processing speed with Trail-Making Task A and Digit Symbol Substitution; executive function with the Stroop, Trail Making Task B, and digit span; semantic and categorical fluency; spatial memory assessed with the Benson complex figure task; and global cognition assessed with the Montreal Cognitive Assessment (MoCA) at baseline and 4±2 weeks after starting CPAP. Results Among 14 patients who completed the study, the mean age was 48.5 ±14.5 yrs, 57% were women, 14% were Black, median AHI was 12 (12;27), mean BMI was 34.3±10.1, and the mean nightly CPAP use was 5.8±2.6 hours. Cognitive function improved with CPAP on the following tests: Digit Symbol Substitution (post CPAP 60.9±2.7 vs baseline 53.6±2.9, p=0.01), Stroop color test (75.2±3.6 vs 66.9±2.1, p< 0.01), Stroop color-word test (46.9±3.3 vs 41.6±3.0, p< 0.01), Benson complex figure-delayed (13.9±0.6 vs 12.3±0.5, p=0.047). In addition, patients reported improvements on PROMIS cognitive function (26.7±2.1 vs 17±1.6, p< 0.01), Epworth Sleepiness Scale (ESS) (5.9±1.2 vs 10.5±1.1, p< 0.01), Insomnia Severity Index (ISI) (12.4±1.5 vs 18.9±1.2, p< 0.01), and the stress (12.6±2.3 vs 18.3±3.1, p< 0.01) and depression (10.3±1.9 vs 14.7±1.9, p=0.01) subscales of the Depression and Anxiety Stress Scale-21. Changes in PROMIS cognitive score (r=0.63, p=0.02) and Trial Making test A (r=-0.58, p=0.048) were correlated to the duration of nightly CPAP use. Conclusion Our findings suggest screening and treating comorbid OSA among patients with post-COVID Condition can improve cognitive fog. Support (if any) This study is supported by the Breathe Pennsylvania Foundation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.281
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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