Abstract WP231: The Incidence of Intracranial Hemorrhage in Patients Anticoagulated With Factor Xa Inhibitors (FXai): A Multi-Country Observational Study (AXIOM ICH)
Bibliographic record
Abstract
Background: Intracranial haemorrhage (ICH) is associated with significant long-term morbidity, high healthcare costs and mortality. There is a paucity of accurate national incidence rate data on ICH, fatal ICH and ICH sub-types in patients on FXai anticoagulants (e.g. rivaroxaban and apixaban). Aim: To describe patient characteristics and incidence of ICH following the initial use of FXai in an ongoing multi-country study. Methods: A retrospective observational cohort analysis of electronic health records from new users of FXai in the UK and Canada. Individuals with FXai prescriptions for a therapeutic indication such as venous thromboembolism, atrial fibrillation (AF), and/or non-mechanical cardiac-valve replacement, were included. Demographic and clinical data were summarised using descriptive statistics, and incidence rates for ICH events were reported per 100 person-years with 95% confidence intervals. Subgroups have been analysed. Results: In the UK and Canada, there were a total of 383,433 new users of FXai; average age 69 years, 54% male, average BMI 29 kg/m 2 and 88% of patients used an oral FXai. The most common indication was AF (73%). 1650 and 438 ICH events were reported in the UK and Canada, with 1-year incidence rates of 0.56 and 0.72 per 100 person-years, 27% and 26% were fatal (Table), respectively. Event rates remain elevated in the years following FXai initiation. Conclusions: Findings indicate incidence rates of ICH in patients treated with FXai were consistent over time, approximately 0.64 per 100 person-years or 1:160 patients per year throughout the course of treatment. Case fatality rates are high, around 27%, remaining elevated in the years following FXai initiation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".