429 What are Patient Characteristics, Nursing Interventions and Patient Outcomes for Patients with Difficulty Adapting to CPAP?
Bibliographic record
Abstract
Abstract Introduction A sleep nurse clinician was added to our care team to provide situationally responsive educational, behavioral and troubleshooting interventions and case management to patients identified as high risk for untreated SDB. The objective of this study was to describe the patient characteristics, nursing interventions, and impact on CPAP usage among patients referred to the nurse-clinician. Methods Descriptive exploratory retrospective chart review was used to identify patient characteristics for all new patients referred; in-depth extraction was conducted for patients who had home CPAP for ≥ 1 month and experienced difficulty using CPAP. We examined: issues using CPAP, frequency and types of nursing contacts, nursing interventions and CPAP usage. Patient characteristics of those with difficulty adapting to CPAP were compared to those referred for assistance to obtain CPAP. Change in objective CPAP usage before vs after sleep nurse case management was determined when possible. Results 403 patients were referred, with mean (SD) age: 54.8 y (15.7), 56.1% men, RDI: 45.4 (31.4), 42.4% employed, 16.6% retired, 25% with ≥ 3 co-morbidities plus SDB. Difficulty adapting group (n = 204) had more women (p = 0.033), more employed (p = 0.03), and more insomnia (p = 0.001). CPAP issues included: mask (18%), ENT (14%), constraining beliefs (14%), access to treatment (12%), pressure intolerance (11%), comorbidities (11%), suboptimal usage (7%), insomnia (7%). Nursing contact: 74% subjects had ≤ 4 contacts (range: 1–16), 53% in-person, 25% telephone calls, 22% other. Nursing interventions were: educational (33%), troubleshooting (30%), behavioural (20%), liaison/coordination of care (13%), promoting self-management skills (3%). Preliminary mean CPAP usage (n = 18) improved by 40.0 (112.3) min and by 7.4 (31) % of nights used ≥ 4 hrs (p=NS; data extraction ongoing). Conclusion Demographic data identified women and insomnia to be significant characteristics in the difficulty adapting group. This model of care identified 2 types of interventions not previously recognized in typical interventions to promote CPAP adaptation: Liaison/coordination of care and promoting self-management skills. Some patients were able to increase their CPAP usage. Support (if any) MUHC Nursing Research Small Grants Award was supported by the Newton Foundation and the Montreal General Hospital Foundation.
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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 | 0.001 | 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".