Improving Sleep Quality on the First Postoperative Night in the Intensive Care Unit: A Quality Improvement Initiative
Bibliographic record
Abstract
Background Sleep disruption in critically ill patients is common and associated with delirium, immune dysfunction, and reduced quality of life. The first postoperative night in the intensive care unit (ICU) is particularly challenging due to pain, anxiety, and frequent care. No intervention has yet reliably improved sleep for this population. Methodology We conducted a quality improvement project with six Plan-Do-Study-Act cycles over seven months. Postoperative adult patients admitted before 21:00 were eligible, with exclusions for instability or factors precluding reliable self-report. The primary outcome was patient-reported sleep quality (1-10 scale) the morning after ICU admission. Secondary outcomes included nurse-perceived sleep quality, sleep quantity, and patient satisfaction. Balancing measures included nurse workload and adverse events. Control charts were used to assess variation. Interventions included staff engagement, earplugs/eye masks, standardized door closure, pain management review, noise monitoring, simplified surveillance protocols, and melatonin. Results A total of 69 patients were recruited (23 pre-interventions, 46 during interventions). Patient-reported sleep quality increased from 5.0 to 5.4/10 after culture shift promotion. Nurse-perceived sleep quality had a favorable special cause variation after streamlined surveillance protocols. No increase in workload or adverse events occurred. The initial survey identified intermittent pneumatic compression devices as sleep disruptors, and the interim analysis showed that an epidural was associated with poorer sleep. Conclusions A multimodal, low-cost sleep bundle modestly improved sleep without increasing staff burden. Periodic data analysis supported real-time adaptation and may benefit similar initiatives. Future work should explore sustainability and impact on recovery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".