Variation in the Rates of Do Not Resuscitate Orders After Major Trauma and the Impact of Intensive Care Unit Environment
Bibliographic record
Abstract
BACKGROUND: There is an increased emphasis on benchmarking of trauma mortality outcomes as a measure of quality. Differences in approaches to end-of-life care or perceptions of salvageability might account for some of the variability in outcomes across centers. We postulated that these differences in perceptions or practice might lead to significant variation in the use of do not resuscitate (DNR) orders and sought to identify institutional characteristics associated with their use. METHODS: Patients surviving >24 hours and admitted to an intensive care unit (ICU) in one of 68 centers across the United States were identified from a large prospective cohort study of severely injured patients. Independent predictors of a DNR order at both the patient and institutional level were identified using multivariate hierarchical modeling stratified by age <55 or >/=55. RESULTS: Of 6,765 patients, 7% had a DNR order, of whom 88% died. The proportion of patients in each center with a DNR order ranged from 0% to 57%. Independent patient-level predictors associated with a DNR order were increasing age, preinjury comorbidity burden, severe injury, and organ failure. Institutional predictors of DNR orders differed by age. Care in an open ICU was associated with a DNR order (odds ratio, 1.7; 95% confidence interval, 1.0-3.0) in the elderly, whereas care in a combined medical-surgical ICU (vs. surgical or trauma ICU) was associated with greater likelihood (odds ratio, 2.0; 95% confidence interval, 1.1-4.1) of a DNR order in the young. CONCLUSIONS: DNR orders are relatively common in seriously injured trauma patients, and there is significant variability in their use across centers. Given the institutional characteristics independently associated with DNR status, it is likely that both differences in the ethos of end-of-life care and perceptions of salvageability affect decision making.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".