Anti-Xa Monitoring of Enoxaparin for Acute Coronary Syndromes in Patients with Renal Disease
Bibliographic record
Abstract
BACKGROUND: There are limited data on dosing of enoxaparin in patients with renal disease due to the routine exclusion of this population in clinical trials. To account for the potentially delayed drug elimination in these patients, we developed guidelines for adjusting enoxaparin dosing based on anti-Xa monitoring. OBJECTIVE: To evaluate anti-Xa level monitoring, resulting from the standards of practice as set out by our hospital's guidelines for enoxaparin dosing in renally impaired patients. METHODS: A total of 72 separate acute coronary syndrome patient admissions were retrospectively reviewed. All patients had anti-Xa levels taken and creatinine clearance values <30 mL/min during enoxaparin therapy. RESULTS: The average trough anti-Xa level at the once- and twice-daily doses was 0.40 and 0.72 IU/mL, respectively. With twice-daily dosing, only 6% of the trough concentrations were in the target range of 0.2-0.3 IU/mL compared with 36% with once-daily dosing. Of the 22 patients who had a change of dosing frequency from twice to once daily, 5% of trough anti-Xa levels were </=0.5 IU/mL with twice-daily versus 68% with once-daily dosing. CONCLUSIONS: Although the relationship between anti-Xa activity, efficacy, and adverse effects has not been definitively established, anti-Xa levels can assist with dosing of enoxaparin in renally impaired patients. Our hospital guidelines are effective in adjusting dosing to reach target anti-Xa levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".