Association between anticoagulants and mortality and functional outcomes in older patients with major trauma
Bibliographic record
Abstract
BACKGROUND: The number of trauma patients taking anticoagulants and antiplatelet agents is increasing as society ages. However, there have been limited and inconsistent reports of the association between anticoagulants and mortality and functional outcomes. This study aimed to quantify the association between anticoagulant/antiplatelet medication at the time of injury and both short-term and longer-term outcomes in older major trauma patients. METHODS: This was a population-based registry study using data from the Victorian State Trauma Registry from July 2017 to June 2018. We included patients with major trauma aged 65 years and older. The outcomes of interest were in-hospital mortality, hospital length of stay, intensive care unit length of stay and the Extended Glasgow Outcome Scale (GOS-E) at 6 months after injury. We examined the association between the outcomes and anticoagulants/antiplatelet agents at the time of injury and used multivariable logistic regression models to account for known confounders. RESULTS: There were 1323 older adults eligible for inclusion in the study, of which 249 (18.8%) were taking anticoagulants (n=8 were taking both anticoagulants and antiplatelet agents), 380 (28.7%) were taking antiplatelet agents and 694 (52.5%) were not using either. Any anticoagulant use was associated with higher odds of in-hospital mortality (adjusted OR (AOR), 2.38; 95% CI 1.58 to 3.59) compared with not using anticoagulants. No differences were observed in the GOS-E at 6 months after injury between any anticoagulants use, antiplatelet use and no anticoagulant use (anticoagulant AOR, 0.71; 95% CI 0.48 to 1.05, antiplatelet AOR, 1.02; 95% CI 0.73 to 1.42). CONCLUSION: Anticoagulant use at the time of injury was associated with higher odds of in-hospital mortality but did not adversely impact functional outcomes at 6 months after injury. These findings demonstrate the importance of seeking an accurate history of anticoagulant use and its indication, as well as the immediate initiation of reversal therapies.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".