Endovascular Stroke Treatment and Risk of Intracranial Hemorrhage in Anticoagulated Patients
Bibliographic record
Abstract
Background and Purpose- We aimed to determine the safety and mortality after mechanical thrombectomy in patients taking vitamin K antagonists (VKAs) or direct oral anticoagulants (DOACs). Methods- In a multicenter observational cohort study, we used multiple logistic regression analysis to evaluate associations of symptomatic intracranial hemorrhage (sICH) with VKA or DOAC prescription before thrombectomy as compared with no anticoagulation. The primary outcomes were the rate of sICH and all-cause mortality at 90 days, incorporating sensitivity analysis regarding confirmed therapeutic anticoagulation. Additionally, we performed a systematic review and meta-analysis of literature on this topic. Results- Altogether, 1932 patients were included (VKA, n=222; DOAC, n=98; no anticoagulation, n=1612); median age, 74 years (interquartile range, 62-82); 49.6% women. VKA prescription was associated with increased odds for sICH and mortality (adjusted odds ratio [aOR], 2.55 [95% CI, 1.35-4.84] and 1.64 [95% CI, 1.09-2.47]) as compared with the control group, whereas no association with DOAC intake was observed (aOR, 0.98 [95% CI, 0.29-3.35] and 1.35 [95% CI, 0.72-2.53]). Sensitivity analyses considering only patients within the confirmed therapeutic anticoagulation range did not alter the findings. A study-level meta-analysis incorporating data from 7462 patients (855 VKAs, 318 DOACs, and 6289 controls) from 15 observational cohorts corroborated these observations, yielding an increased rate of sICH in VKA patients (aOR, 1.62 [95% CI, 1.22-2.17]) but not in DOAC patients (aOR, 1.03 [95% CI, 0.60-1.80]). Conclusions- Patients taking VKA have an increased risk of sICH and mortality after mechanical thrombectomy. The lower risk of sICH associated with DOAC may also be noticeable in the acute setting. Improved selection might be advisable in VKA-treated patients. Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT03496064. Systematic Review and Meta-Analysis: CRD42019127464.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".