Identifying Obstructive Sleep Apnea in Administrative Data
Bibliographic record
Abstract
AbstractAbstract In approximately 5,000 patients who underwent preoperative polysomnography, 56% met criteria for a diagnosis of obstructive sleep apnea (OSA). In these patients with known or excluded OSA, none of the health administrative diagnostic codes, diagnostic procedures, or therapeutic interventions by themselves or in combination identified OSA with adequately high sensitivity and specificity. Existing studies using administrative codes to identify OSA should be interpreted with caution. Background: Health administrative (HA) databases are increasingly used to identify surgical patients with obstructive sleep apnea (OSA) for research purposes, primarily using diagnostic codes. Such means to identify patients with OSA are not validated. The authors determined the accuracy of case-ascertainment algorithms for identifying patients with OSA with the use of HA data. Methods: Clinical data derived from an academic health sciences network within a universal health insurance plan were used as the reference standard. The authors linked patients to HA data and retrieved all claims in the 2 yr before surgery to determine the presence of any diagnostic codes, diagnostic procedures, or therapeutic interventions consistent with OSA. Results: The authors identified 4,965 patients (2003 to 2012) who underwent preoperative polysomnogram. Of these, 4,353 patients were linked to HA data; 2,427 of these (56%) had OSA based on diagnosis by a sleep physician or the apnea hypopnea index. A claim for a polysomnogram and receipt of a positive airway pressure device had a sensitivity, specificity, and positive likelihood ratio (+LR) for OSA of 19, 98, and 10.9%, respectively. An International Classification of Diseases, Tenth Revision , code for sleep apnea in hospitalization abstracts was 9% sensitive and 98% specific (+LR, 4.5). A physician billing claim for OSA ( International Classification of Diseases, Ninth Revision , 780.5) was 58% sensitive and 38% specific (+LR, 0.9). A polysomnogram and a positive airway pressure device or any code for OSA was 70% sensitive and 36% specific (+LR, 1.1). Conclusions: No code or combination of codes provided a +LR high enough to adequately identify patients with OSA. Existing studies using administrative codes to identify OSA should be interpreted with caution.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".