The Effect of Sleep Disorder Interventions in Patients With Obstructive Sleep Apnea Undergoing Solid-Organ Transplants: A Systematic Review
Bibliographic record
Abstract
Poor sleep results in a wide range of health consequences, and is prevalent among patients undergoing solid-organ transplant, who have high rates of obstructive sleep apnea (OSA). Our objective was to synthesize the evidence examining the impact of OSA therapies such as continuous positive airway pressure (CPAP) on outcomes in the solid-organ transplant setting. We conducted a systematic review registered via PROSERO (CRD42024512577) and reported in accordance with PRISMA guidelines. Databases searched included: MEDLINE, MEDLINE ePubs Ahead of Print and In-process Citations, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO. Included studies were those investigating interventions used in adult patients (≥18 years) with OSA undergoing solid-organ transplantation. A total of 1407 studies were identified, with screening identifying 38 studies for full-text review, and only 3 were eligible for data extraction. Only 1 study in heart transplant patients evaluated changes in sleep parameters, and demonstrated that the implementation of nasal CPAP led to significant improvements in Apnea-Hypopnea Index, Arousal Index, and total sleep time with oxygen saturation <90%. Patients with untreated OSA were more likely to develop graft dysfunction earlier than patients with treated or no OSA. There was no detectable impact of CPAP on 1-year mortality or 5-year overall survival after adjustment. Future studies, namely RCTs, should be conducted to assess the tolerability, compliance, and effectiveness of CPAP in this at-risk population. Additional studies should focus on optimizing sleep in solid-organ transplant populations by providing interventions and education that best match individual patient needs, with engagement from patient partners.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".