Sleep quality and architecture in COPD: the relationship with lung function abnormalities
Bibliographic record
Abstract
OBJECTIVE: Impaired respiratory mechanics and gas exchange may contribute to sleep disturbance in patients with COPD. We aimed to assess putative associations of different domains of lung function (airflow limitation, lung volumes, and gas exchange efficiency) with polysomnography (PSG)-derived parameters of sleep quality and architecture in COPD. METHODS: We retrospectively assessed data from COPD 181 patients ≥ 40 years of age who underwent spirometry, plethysmography, and overnight PSG. Univariate and multivariate linear regression models predicted sleep efficiency (total sleep time/total recording time) and other PSG-derived parameters that reflect sleep quality. RESULTS: The severity of COPD was widely distributed in the sample (post-bronchodilator FEV1 ranging from 25% to 128% of predicted): mild COPD (40.3%), moderate COPD (43.1%), and severe-very severe COPD (16.6%). PSG unveiled a high proportion of obstructive sleep apnea (64.1%) and significant nocturnal desaturation (mean pulse oximetry nadir = 82.2% ± 6.9%). After controlling for age, sex, BMI, apnea-hypopnea index, nocturnal desaturation, comorbidities, and psychotropic drug prescription, FEV1/FVC was associated with sleep efficiency (β = 25.366; R2 = 14%; p < 0.001), whereas DLCO predicted sleep onset latency (β = -0.314; R2 = 13%; p < 0.001) and rapid eye movement sleep time/total sleep time in % (β = 0.085; R2 = 15%; p = 0.001). CONCLUSIONS: Pulmonary function variables reflecting severity of airflow and gas exchange impairment, adjusted for some potential confounders, were weakly related to PSG outcomes in COPD patients. The direct contribution of the pathophysiological hallmarks of COPD to objectively measured parameters of sleep quality seems to be less important than it was previously assumed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".