Outcomes of Elderly Patients on Direct Oral Anticoagulants (DOACs) Versus Warfarin After Traumatic Brain Injury
Bibliographic record
Abstract
ABSTRACT: Background: Although evidence supports the improved safety profile of direct oral anticoagulants (DOACs) over warfarin (WF), outcomes among elderly traumatic brain injury (TBI) patients on this regimen remain unclear. This study describes the association between anticoagulation status (DOAC vs. WF use) and the rates of occurrence of intracranial hemorrhage (ICH), hematoma progression, need for surgical intervention and mortality in elderly TBI cases. Methods: This retrospective cohort study from 2014 to 2019 included all trauma patients > 65 years on either WF or DOACs at the time of injury. The primary outcome was the rate of ICH after TBI. Multivariable regression analysis identified independent predictors of functional dependency and mortality. Results: A total of 501 elderly TBI patients (mean age = 82 years old) were included. WF users had higher CT Marshall scores (p = 0.007), more severe TBI (GCS < 8) (p = 0.003) and higher rates of subdural hematomas compared to the DOAC group (p = 0.003). Patients on DOACs had lower rates of ICH (42% vs. 57%, p = 0.001) and hospitalization (30% vs. 41%, p = 0.013) and better Glasgow outcome scale-extended scores at hospital discharge (mean 6.98 vs. 6.41, p = 0.005). Multicompartment ICH (OR 2.30, p = 0.027) and longer hospitalization (OR 0.04, p < 0.001) were associated with higher functional dependency rates, while higher CT Marshall scores (OR 1.09, p < 0.001) and poorer baseline frailty status (OR 0.62, p = 0.026) predicted increased mortality risk. Conclusion: Elderly TBI patients on DOACs have lower rates of ICH, lower need for hospitalization and better functional outcomes at discharge compared to those taking WF. These findings need further confirmation using prospective multicenter studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".