Pain, agitation and delirium assessment and management in a community medical-surgical ICU: results from a prospective observational study and nurse survey
Bibliographic record
Abstract
BACKGROUND: Delirium is a common manifestation in the intensive care unit (ICU) that is associated with increased mortality and morbidity. Guidelines suggested appropriate management of pain, agitation and delirium (PAD) is crucial in improving patient outcomes. However, the practice of PAD assessment and management in community hospitals is unclear and the mechanisms contributing to the potential care gap are unknown. OBJECTIVES: This quality improvement initiative aimed to review the practice of PAD assessment and management in a community medical-surgical ICU (MSICU) and to explore the community MSICU nurses' perceived comfort and satisfaction with PAD management in order to understand the mechanisms of the observed care gap and to inform subsequent quality improvement interventions. METHODS: We prospectively collected basic demographic data, clinical information and daily data on PAD process measures including PAD assessment and target Richmond Agitation-Sedation Scale (RASS) score ordered by intensivists on all patients admitted to a community MSICU for >24 hours over a 20-week period. All ICU nurses in the same community MSICU were invited to participate in an anonymous survey. RESULTS: We collected data on a total of 1101 patient-days (PD). 653 PD (59%), 861 PD (78%) and 439 PD (39%) had PAD assessment performed, respectively. Target RASS was ordered by the intensivists on 515 PD (47%). Our nurse survey revealed that 88%, 85% and 41% of nurses were comfortable with PAD assessment, respectively. CONCLUSIONS: Delirium assessment was not routinely performed. This is partly explained by the discomfort nurses felt towards conducting delirium assessment. Our results suggested that improvement in nurse comfort with delirium assessment and management is needed in the community MSICU setting.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".