Abstract 131: Impact of Concomitant Use of Antiplatelet and Anticoagulation Therapies on Outcomes in Patients With Intracerebral Hemorrhage
Bibliographic record
Abstract
Background: The impact of the preceding use of concomitant use of antiplatelet and anticoagulation therapies on outcomes of intracerebral hemorrhage (ICH) has not been thoroughly investigated. Methods: We analyzed 138,834 patients presenting with ICH between October 2013 and December 2016 from 1661 hospitals participating in the Get With The Guidelines-Stroke program. Multivariable logistic regression was used to evaluate the impact of concomitant antiplatelet therapy on in-hospital mortality in anticoagulation-related ICH. Results: Of 138,834 patients (mean [SD] age 68.4 [15.3] years; 48.0% women), 118,219 (85.2%) patients were not receiving any oral anticoagulant (OAC), 15,777 (11.4%) were receiving warfarin and 4,838 (3.5%) were receiving non-vitamin K oral anticoagulants (NOACs) prior to ICH. Stroke severity as measured by NIHSS was similar across three groups. Patients receiving warfarin or NOACs were more likely to have single antiplatelet therapy (SAPT) than those receiving no OAC (33.0% for warfarin, 30.7% for NOACs, and 27.3% for no OAC), whereas patients receiving no OAC were more likely to have dual antiplatelet therapy (DAPT) (2.5% for warfarin, 2.3% for NOACs, and 4.4% for no OAC). After adjustment for confounders, SAPT was associated with a slightly higher rate of mortality (33.8% vs. 32.1%, adjusted odds ratio [AOR] = 1.20, 95% CI [1.11-1.30]) than no antiplatelet in patients receiving warfarin, whereas there were no statistically significant differences between SAPT vs. no antiplatelet in patients with no OAC (22.4% vs. 22.9%, AOR = 0.99 [0.95-1.03]) or NOACs (26.8% vs. 26.9%, AOR = 1.09 [0.94-1.26]). DAPT was associated with increased risk of death in patients taking warfarin (46.5% vs. 32.1%, AOR = 2.08 [95% CI, 1.68-2.57]) and no OAC (30.4% vs. 22.9%, AOR = 1.51 [95% CI, 1.40-1.63]). By contrast, in patients taking NOACs, the association of DAPT was not significant but the confidence intervals were wider (32.7% vs. 26.9%, AOR = 1.37 [95% CI, 0.90-2.08]) likely due to small sample size. Conclusions: In patients experienced an ICH, prior concomitant use of antiplatelet therapy (either SAPT or DAPT) significantly increased odds of mortality in patients taking warfarin, but such a difference was not apparent in NOAC-treated ICH patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".