Case-Ascertainment Models to Identify Adults with Obstructive Sleep Apnea Using Health Administrative Data: Internal and External Validation
Bibliographic record
Abstract
BACKGROUND: There is limited evidence on whether obstructive sleep apnea (OSA) can be accurately identified using health administrative data. STUDY DESIGN AND METHODS: We derived and validated a case-ascertainment model to identify OSA using linked provincial health administrative and clinical data from all consecutive adults who underwent a diagnostic sleep study (index date) at two large academic centers (Ontario, Canada) from 2007 to 2017. The presence of moderate/severe OSA (an apnea-hypopnea index≥15) was defined using clinical data. Of 39 candidate health administrative variables considered, 32 were tested. We used classification and regression tree (CART) methods to identify the most parsimonious models via cost-complexity pruning. Identified variables were also used to create parsimonious logistic regression models. All individuals with an estimated probability of 0.5 or greater using the predictive models were classified as having OSA. RESULTS: The case-ascertainment models were derived and validated internally through bootstrapping on 5099 individuals from one center (33% moderate/severe OSA) and validated externally on 13,486 adults from the other (45% moderate/severe OSA). On the external cohort, parsimonious models demonstrated c-statistics of 0.75-0.81, sensitivities of 59-60%, specificities of 87-88%, positive predictive values of 79%, negative predictive values of 73%, positive likelihood ratios (+LRs) of 4.5-5.0 and -LRs of 0.5. Logistic models performed better than CART models (mean integrated calibration indices of 0.02-0.03 and 0.06-0.12, respectively). The best model included: sex, age, and hypertension at the index date, as well as an outpatient specialty physician visit for OSA, a repeated sleep study, and a positive airway pressure treatment claim within 1 year since the index date. INTERPRETATION: Among adults who underwent a sleep study, case-ascertainment models for identifying moderate/severe OSA using health administrative data had relatively low sensitivity but high specificity and good discriminative ability. These findings could help study trends and outcomes of OSA individuals using routinely collected health care data.
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 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 | 0.130 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".