Modifiable Risk Factors for Delirium in Critically Ill Trauma Patients: A Multicenter Prospective Study
Bibliographic record
Abstract
OBJECTIVE:: Intensive care unit (ICU)-acquired delirium has been associated with increased morbidity and mortality. Prevention strategies including modification of delirium risk factors are emphasized by practice guidelines. No study has specifically evaluated modifiable delirium risk factors in trauma ICU patients. Our goal was to evaluate modifiable risk factors for delirium among trauma patients admitted to the ICU. DESIGN:: Prospective observational study. SETTING:: Two level 1 trauma ICU centers. PATIENTS:: Patients 18 years of age or older admitted for trauma including mild to moderate traumatic brain injury were eligible for the study. INTERVENTIONS AND MEASUREMENTS:: Delirium was assessed daily using the confusion assessment method for the ICU (CAM-ICU). The effect of modifiable risk factors was assessed using multivariate Cox regression analysis adjusting for severity of illness and significant nonmodifiable risk factors. MAIN RESULTS:: A total of 58 of 150 recruited patients (38.7%; 95% confidence interval [CI] 30.9-46.5) screened positive for delirium during ICU stay. When adjusting for significant nonmodifiable risk factors, physical restraints (hazard ratio [HR]: 2.13; 95% CI: 1.07-4.24) and active infection or sepsis (HR: 2.12; 95% CI: 1.18-3.81) significantly increased the risk of delirium, whereas opioids (HR: 0.35; 95% CI: 0.13-0.98), episodes of hypoxia (HR: 0.55; 95% CI: 0.31-0.95), access to a television/radio in the room (HR: 0.26; 95% CI: 0.11-0.62), and number of hours mobilized per day (HR: 0.77; 95% CI: 0.68-0.88) were associated with significantly less risk of delirium. CONCLUSION:: We have identified modifiable risk factors for delirium. Future studies should aim at implementing strategies to modify these risk factors and evaluate their impact on the risk of delirium.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".