Meta‐Analysis of Intracranial Hemorrhage in Acute Coronary Syndromes: Incidence, Predictors, and Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Little is known about the incidence, predictors, or outcomes of intracranial hemorrhage (ICH) in patients with non-ST-segment elevation acute coronary syndromes (NSTE ACS). We aimed to determine the incidence and timing of ICH, characterize the location of ICH, and identify independent baseline predictors of ICH in NSTE ACS patients. METHODS AND RESULTS: We pooled patient-level data from 4 contemporary antithrombotic therapy trials. Multivariable modeling identified independent predictors of ICH. ICHs were adjudicated by a clinical events committee. Of 37 815 patients, 135 (0.4%) had an ICH. The median (25th, 75th percentiles) follow-up was 332 (184, 434) days but differed across trials. Locations of ICH were intracerebral (50%), subdural (31%), subarachnoid (18.5%), and intraventricular (11%). Independent predictors of ICH were older age (HR per 10 years, 1.61; 95% CI, 1.35 to 1.91); prior stroke/transient ischemic attack; HR, 1.95; 95% CI, 1.14 to 3.35), higher systolic blood pressure; HR per 10 mm Hg increase, 1.09; 95% CI, 1.01 to 1.18), and larger number of antithrombotic agents (HR per each additional agent, 2.06; 95% CI, 1.49 to 2.84). Of all ICHs, 45 (33%) were fatal. CONCLUSIONS: In patients with NSTE ACS enrolled in recent clinical trials of antithrombotic therapies, ICH was uncommon. Patients with older age, prior transient ischemic attack/stroke, higher systolic blood pressure, or larger number of antithrombotic agents were at increased risk. One-third of patients with ICH died. These data may be useful to trialists and data and safety monitoring committees for trial conduct and monitoring. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov/. Unique identifiers: TRACER: NCT00527943, PLATO: NCT00391872, APPRAISE-2: NCT00831441, TRILOGY ACS: NCT00699998.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".