The role of aetiology in determining anticoagulation effectiveness for the treatment of left ventricular thrombus
Bibliographic record
Abstract
AIMS: Left ventricular (LV) thrombus is a severe complication of acute myocardial infarction (AMI) and chronic heart failure. While current guidelines support the use of direct oral anticoagulants (DOACs) as alternatives to vitamin K antagonists (VKA), their benefit across different aetiologies remains uncertain. This study aimed to compare the efficacy and safety of DOAC vs. VKA across different aetiologies of LV dysfunction. METHODS AND RESULTS: We conducted a multi-centre observational study including 901 patients with confirmed LV thrombus treated with either a VKA or DOAC. The primary outcome was thrombus resolution, secondary outcomes included stroke and systemic embolization (SSE), major bleeding and mortality with analyses performed by aetiology. The principal aetiologies were AMI (38.3%), ischaemic cardiomyopathy (ICM) (38.0%) and non-ischaemic cardiomyopathy (NICM) (23.7%). Overall, thrombus resolution was significantly higher in DOAC treated patients, but this was driven by the AMI sub-group (P = 0.018). Direct oral anticoagulant use independently predicted thrombus resolution (OR 2.0, 95% Cl 1.29-3.24, P = 0.010). Major bleeding events (BARC ≥3) were more common with VKA use (P = 0.008). Non-ischaemic cardiomyopathy had the highest SSE rate (15.3%, P = 0.002), which were significantly raised in those treated with DOAC (P < 0.001). CONCLUSION: The underlying aetiology of LV dysfunction significantly influences both treatment response and outcomes in patients with LV thrombus. Direct oral anticoagulant were associated with superior efficacy and safety in AMI-related LV thrombus, but were linked to increased rates of SSE in NICM. These findings highlight the importance of aetiology on LV thrombus management and the potential need for tailored approaches.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".