Disturbed sleep after lung transplantation is associated with worse patient-reported outcomes and chronic lung allograft dysfunction
Bibliographic record
Abstract
Many lung transplant recipients fail to derive the expected improvements in functioning, HRQL, or long-term survival. Sleep may represent an important, albeit rarely examined, factor influencing lung transplant outcomes. Within a larger cohort study, 141 lung transplant recipients completed the Medical Outcomes Study (MOS) Sleep Scale along with a broader survey of patient-reported outcome (PRO) measures and frailty assessment. MOS Sleep yields the Sleep Problems Index (SPI); we also derived an insomnia-specific subscale. Potential perioperative predictors of disturbed sleep and time to chronic lung allograft dysfunction (CLAD) and death were derived from medical records. We investigated associations between perioperative predictors on SPI and Insomnia and associations between SPI and Insomnia on PROs and frailty by linear regressions, adjusting for age, sex, and lung function. We evaluated the associations between SPI and Insomnia on time to CLAD and death using Cox models, adjusting for age, sex, and transplant indication. Post-transplant hospital length of stay >30 days was associated with worse sleep by SPI and insomnia (SPI: p=0.01; Insomnia p=0.02). Worse sleep by SPI and insomnia was associated with worse depression, cognitive function, HRQL, physical disability, health utilities, and Fried Frailty Phenotype frailty (all p<0.01). Those in the worst quartile of SPI and insomnia exhibited increased risk of CLAD (HR 2.18; 95%CI: 1.22-3.89 ; p=0.01 for SPI and HR 1.96; 95%CI 1.09-3.53; p=0.03 for insomnia). Worsening in SPI but not insomnia was also associated with mortality (HR: 1.29; 95%CI: 1.05-1.58; p=0.01). Poor sleep after lung transplant may be a novel predictor of patient reported outcomes, frailty, CLAD, and death with potentially important screening and treatment implications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".