Protocolized Intensive Care Unit Management of Analgesia, Sedation, and Delirium Improves Analgesia and Subsyndromal Delirium Rates
Bibliographic record
Abstract
BACKGROUND: Sedatives and analgesics, in doses that alter consciousness in the intensive care unit (ICU), contribute to delirium and mortality. Pain, agitation, and delirium can be monitored in ICU patients. These symptoms were noted before (PRE) and after (POST) a protocol to alleviate undesirable symptoms. Analgesia and sedation levels, the incidence of coma, delirium, length of stay (LOS), discharge location, and mortality were then compared. We hypothesized that the likely reduction in iatrogenic coma would result in less delirium, because these 2 morbid conditions seem to be linked. METHODS: All patients were consecutively admitted to an ICU PRE-protocol (August 2003 to February 2004, 610 patients) and POST-protocol (April 2005 to November 2005, 604 patients). Between February 2004 and April 2005, we piloted and taught individualized nonpharmacologic strategies and titration of analgesics, sedatives, and antipsychotics based on sedation, analgesia, and delirium scores. We measured the following outcomes: coma, delirium, LOS, mortality, and discharge location. RESULTS: The POST group benefited from better analgesia, received less opiates (90.72 + or - 207.45 vs 22.93 + or - 40.36 morphine equivalents/d, P = <0.0001), and, despite comparable sedation, had shorter duration of mechanical ventilation. Medication-induced coma rates (18.1%vs 7.2%, P < 0.0001), ICU and hospital LOS, and dependency at discharge were lower in the POST-protocol group. Subsyndromal delirium was significantly reduced; delirium was similar. The 30-day mortality risk in the PRE cohort was 29.4% vs 22.9% in the POST cohort (log-rank test, P = 0.009). CONCLUSION: Educational initiatives incorporating systematic management protocols with nonpharmacologic measures and individualized titration of sedation, analgesia, and delirium therapies are associated with better outcomes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".