Predictive Performance of Oximetry to Detect Sleep Apnea in Patients Taking Opioids
Bibliographic record
Abstract
BACKGROUND: Long-term use of opioids for treatment of chronic pain is associated with significant risks including worsening unrecognized or untreated sleep apnea that may increase morbidity and mortality. Overnight oximetry has been validated for predicting sleep apnea in surgical and sleep clinic patients. The objective of the study was to assess the predictive accuracy of oxygen desaturation index (ODI 4%) from home overnight oximetry when compared to apnea hypopnea index (AHI) from polysomnography for predicting sleep apnea in patients taking opioids for chronic pain. METHODS: This was a planned post hoc analysis of a prospective cohort study conducted at 5 pain clinics. Patient characteristics and daily morphine milligram equivalent (MME) dose were recorded. All consented patients underwent home overnight oximetry (PULSOX-300i, Konica Minolta Sensing, Inc, Osaka, Japan) and in-laboratory polysomnography. The predictive performance of ODI 4% from oximetry was assessed against AHI from polysomnography. RESULTS: Among 332 consented patients, 181 with polysomnography and overnight oximetry data were analyzed. The mean age and body mass index of 181 patients were 52 ± 13 years and 29 ± 6 kg/m2, respectively, with 40% men. The area under the receiver operating curve for ODI to predict moderate-to-severe sleep apnea (AHI ≥15 events/h) and severe sleep apnea (AHI ≥30 events/h) was 0.82 (95% confidence interval [CI], 0.75-0.88) and 0.87 (95% CI, 0.80-0.94). ODI ≥5 events/h had a sensitivity of 85% (95% CI, 74-92) and specificity of 57% (95% CI, 52-61) to predict moderate-to-severe sleep apnea. ODI ≥15 events/h had a sensitivity of 71% (95% CI, 55-83) and specificity of 88% (95% CI, 84-91) to predict severe sleep apnea. CONCLUSIONS: Overnight home oximetry has a high predictive performance in predicting moderate-to-severe and severe sleep apnea in patients on opioids for chronic pain. It is a useful additional tool for health care providers for the screening of sleep apnea in this high-risk group.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".