Muscle Mass and Direct Oral Anticoagulant Activity in Older Adults With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Direct oral anticoagulants (DOAC) are hydrophilic drugs with plasma levels inversely proportional to lean body mass. Sarcopenic patients with low muscle mass may be at risk for supra-therapeutic DOAC levels and bleeding complications. We therefore sought to examine the influence of lean body mass on DOAC levels in older adults with atrial fibrillation (AF). METHODS: A prospective cohort study was conducted with patients 65 years of age or more receiving rivaroxaban or apixaban for AF. Appendicular lean mass (ALM) was measured using a bioimpedance device and a dual X-ray absorptiometry scanner. DOAC levels were measured using a standardized anti-Xa assay 4 hours after (peak) and 1 hour before (trough) ingestion. RESULTS: The cohort consisted of 62 patients (47% female, 77.0 ± 6.1 years). The prescribed DOACs were apixaban 2.5 mg (21%), apixaban 5 mg (53%), and rivaroxaban 20 mg (26%). Overall, 16% had supra-therapeutic DOAC levels at trough and 25% at peak. In the multivariable logistic regression model, lower ALM was independently associated with supra-therapeutic DOAC levels at trough (odds ratio per ↓ 1-kg 1.23, 95% confidence interval 1.02 to 1.49) and peak (odds ratio per ↓ 1-kg 1.18, 95% confidence interval 1.02 to 1.37). Addition of ALM to a model consisting of age, total body weight, and renal function resulted in improved discrimination for supra-therapeutic DOAC levels. CONCLUSION: Our proof-of-concept study has identified an association between ALM and DOAC levels in older adults with AF. Further research is needed to determine the impact of ALM on bleeding complications and the potential role of ALM-guided dosing for sarcopenic patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".