Antithrombotic choice in blunt cerebrovascular injuries: Experience at a tertiary trauma center, systematic review, and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Blunt cerebrovascular injuries (BCVIs) may occur following trauma and lead to ischemic stroke if untreated. Antithrombotic therapy decreases this risk; however, the optimal agent has yet to be determined in this population. The aim of this study was to compare the risk-benefit profile of antiplatelet (AP) versus anticoagulant (AC) therapy in rates of ischemic stroke and hemorrhagic complications in BCVI patients. METHODS: We performed a retrospective review of BCVI patients at our tertiary care Trauma hospital from 2010 to 2015, and a systematic review and meta-analysis of the literature. The OVID Medline, Embase, Web of Science, and Cochrane Library databases were searched from inception to September 16, 2019. References of included publications were searched manually for other relevant articles. The search was limited to articles in humans, in patients 18 years or older, and in English. Studies that reported treatment-stratified clinical outcomes following AP or AC treatment in BCVI patients were included. Exclusion criteria included case reports, case series with n < 5, review articles, conference abstracts, animal studies, and non-peer-reviewed publications. Data were extracted from each study independently by two reviewers, including study design, country of origin, sex and age of patients, Injury Severity Score, Biffl grade, type of treatment, ischemic stroke rate, and hemorrhage rate. Pooled estimates using odds ratio (OR) were combined using a random-effects model using a Mantel-Hanzel weighting. The main outcome of interest was rate of ischemic stroke due to BCVI, and the secondary outcome was hemorrhage rate based on AC or AP treatment. RESULTS: In total, there were 2044 BCVI patients, as reported in the 22 studies in combination with our institutional data. The stroke rate was not significantly different between the two treatment groups (OR, 1.27; 95% confidence interval, 0.40-3.99); however, the hemorrhage rate was decreased in AP versus AC treated groups (OR, 0.38; 95% confidence interval, 0.15-1.00). CONCLUSION: Based on this meta-analysis, both AC and AP seem similarly effective in preventing ischemic stroke, but AP is better tolerated in the trauma population. This suggests that AP therapy may be preferred, but this should be further assessed with prospective randomized trials. LEVEL OF EVIDENCE: Review article, level II.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".