Assessment of Predicted Rate and Associated Factors of Dabigatran-induced Bleeding Events in Malaysian Patients with Non-Valvular Atrial Fibrillation
Bibliographic record
Abstract
PURPOSE: To assess the predicted rate and the factors associated with bleeding events among patients with non-valvular atrial fibrillation (NVAF) receiving dabigatran therapy. METHODS: This retrospective cohort study includes adult patients of two tertiary hospitals in Malaysia. Potential study subjects were identified using pharmacy supply database or novel oral anticoagulant (NOAC) registry. Demographics, clinical data and laboratory test results were extracted from the medical records of the patients or electronic databases. The main outcome measure is the occurrence of a bleeding event. Bleeding events were classified into major bleeding, clinically relevant non-major bleeding, or minor bleeding, according to the International Society on Thrombosis and Haemostasis criteria. We consider clinically relevant non-major bleeding events or major bleeding events as clinically relevant bleeding events. An occurrence of any bleeding event was recorded from the initiation of NOAC therapy until the death of a patient, or the date of permanent discontinuation of NOAC use, or the last day of data collection. The predicted rate of dabigatran-induced bleeding events per 100 patient-years was estimated. RESULTS: During a median follow-up period of 18 months, 73 patients experienced 90 bleeding events. Among these patients, 25 including 4 fatal cases, experienced major bleeding events. The predicted rate per 100 patient-years of follow-up of any bleeding events was 9.0 [95% CI 6.9 to 11.1]; clinically relevant bleeding events 6.0 [95% CI 4.8 to 8.3], and major bleeding events 3.0 [95% CI 1.9 to 4.2]. The independent risk factor for clinically relevant bleeding events is prior bleeding. While prior bleeding or congestive heart failure is linked with major bleeding events. CONCLUSIONS: The predicted rate for dabigatran-induced major bleeding episodes is low but these adverse events carry a high fatality risk. Preventive measures should target older patients who have prior bleeding or congestive heart failure. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".