The use of the odds ratio product and self-reported data to detect comorbid insomnia and sleep apnea
Bibliographic record
Abstract
STUDY OBJECTIVES: To evaluate the utility of the odds ratio product (ORP) in differentiating comorbid insomnia and sleep apnea (COMISA) from obstructive sleep apnea (OSA) and chronic insomnia (CIN). METHODS: We retrospectively analyzed 9750 patients in four groups: (1) 1152 controls, (2) 2395 with CIN, (3) 2297 with OSA, and (4) 3906 with COMISA. CIN was defined as difficulty initiating/maintaining sleep with daytime fatigue/sleepiness occurring "often"/"always." OSA was defined as an apnea-hypopnea index ≥5 on polysomnography. ORP, computed every 3 s from polysomnography, was analyzed alongside sleep metrics, comorbidities, and sleep habits. Associations were assessed using univariate multinomial logistic regression, followed by stepwise regression to identify independent predictors of COMISA versus OSA or CIN. Machine learning models classified COMISA, OSA, and CIN as distinct clinical groups. RESULTS: ORP-derived features showed stronger associations with COMISA than traditional sleep metrics (except N3 latency). Independent objective predictors of COMISA included male sex (OR = 1.31, 95% CI = [1.16, 1.47]), BMI (1.27, [1.25, 1.29]), N3 latency (1.21, [1.13, 1.29]), age (1.17, [1.16, 1.19]), peak ORP during spontaneous arousals (1.12, [1.01, 1.25]), and time in ORP decile 7 (1.10, [1.07, 1.13]). Subjective predictors included depression, hypertension, allergy, headache, sleep aid/alcohol use, sleepiness, and lower sleep duration. Machine learning achieved overall accuracy of 61.2 per cent (p<.05), with sensitivity of 71 per cent for COMISA, 65 per cent for OSA, and 43 per cent for CIN. CONCLUSIONS: ORP is a promising objective marker for COMISA, distinguishing it from OSA more effectively than sleep metrics but separating COMISA from CIN poorly. Statement of Significance This study highlights the potential of the odds ratio product (ORP) as a novel objective marker to complement self-report questionnaires for identifying comorbid insomnia and sleep apnea (COMISA). Our findings demonstrate that ORP, combined with demographics and self-reported comorbidities and sleep habits, can distinguish COMISA from obstructive sleep apnea more effectively than traditional polysomnography metrics. These findings may refine the current understanding of COMISA from a research perspective and, in the future, may have clinical implications for improving the diagnosis and management of the disorder, which is often underrecognized in clinical patients who are evaluated for sleep-disordered breathing. Future studies should explore how ORP can be integrated into clinical sleep assessments and diagnostic strategies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".