Abstract WP368: The Association of Prior Use of Antiplatelet Agents and Increased Mortality and Morbidity in Intracerebral Hemorrhage Patients
Bibliographic record
Abstract
Introduction: Use of anti-platelet therapy is common among patients presenting with intracerebral hemorrhage (ICH). There are limited data regarding pre-stroke antiplatelet therapy (APT) and outcomes in patients presenting with spontaneous ICH. We hypothesized that prior use of antiplatelet agents increases mortality and discharge morbidity in ICH patients. Methods: We analyzed data of 82,576 ICH patients not on anticoagulation from 2185 GWTG-Stroke hospitals between Oct 2012 and March 2016. Patients were categorized as no APT, single antiplatelet therapy (SAPT) and dual antiplatelet therapy (DAPT). Logistic regression using generalized estimating equations to account for within-site correlations were used to assess the relationship between outcomes and prior-APT use. Results: No pre-ICH APT was used in 65.8%, SAPT in 29.5%, and DAPT in 4.8%. The median age of the cohort was 69 years and prevalence of females in the cohort was 48.6%, with preponderance of white race (58.9%). Overall onset of symptoms to arrival time was 131 minutes with a median NIHSS of 9. A total of 23.7% had history of previous stroke/transient ischemic attack, 15.3% had prior myocardial infarction/coronary artery disease and 73.4% had known hypertension. There was no significant difference in in-hospital mortality among patients not on any APT vs patients on SAPT. However, in-hospital mortality was higher among ICH patients on DAPT compared with no therapy (adjusted OR 1.41, 95 % CI 1.31-1.51, P<0.0001). Conclusion: Our study suggests that patients on DAPT, but not on SAPT, have higher mortality rates after ICH compared with patients on no APT.
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 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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".