Impact of Body Mass Index and Discomfort on Upper Airway Stimulation: ADHERE Registry 2020 Update
Bibliographic record
Abstract
Objectives/Hypothesis To provide the ADHERE registry Upper Airway Stimulation (UAS) outcomes update, including analyses grouped by body mass index (BMI) and therapy discomfort. Study Design Prospective observational study. Methods ADHERE captures UAS outcomes including apnea‐hypopnea index (AHI), Epworth sleepiness scale (ESS), therapy usage, patient satisfaction, clinician assessment, and safety over a 1‐year period. BMI ≤32 kg/m2 (BMI32) and 32 < BMI ≤35 kg/m2 (BMI35) group outcomes were examined. Results One thousand eight hundred forty‐nine patients enrolled in ADHERE, 1,019 reached final visit, 843 completed the visit. Significant changes in AHI (−20.9, P < .0001) and ESS (− 4.4, P < .0001) were demonstrated. Mean therapy usage was 5.6 ± 2.2 hr/day. Significant therapy use difference was present in patients with reported discomfort versus no discomfort (4.9 ± 2.5 vs. 5.7 ± 2.1 hr/day, P = .01). Patients with discomfort had higher final visit mean AHI versus without discomfort (18.9 ± 18.5 vs. 13.5 ± 13.7 events/hr, P = .01). Changes in AHI and ESS were not significantly different. Serious adverse events reported in 2.3% of patients. Device revision rate was 1.9%. Surgical success was less likely in BMI35 versus BMI32 patients (59.8% vs. 72.2%, P = .02). There was a significant therapy use difference: 5.8 ± 2.0 hr/day in BMI32 versus 5.2 ± 2.2 hr/day in BMI35 (P = .028). Conclusions Data from ADHERE demonstrate high efficacy rates for UAS. Although surgical response rate differs between BMI32 and BMI35 patient groups, the AHI and ESS reduction is similar. Discomfort affects therapy adherence and efficacy. Thus, proper therapy settings adjustment to ensure comfort is imperative to improve outcomes. Level of Evidence 4 Laryngoscope, 131:2616–2624, 2021
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".