Efficacy and safety of four-factor prothrombin complex concentrate fixed, weight-based dosing for reversal of warfarin anticoagulation
Bibliographic record
Abstract
Background Four-factor prothrombin complex concentrate (4F-PCC) is widely used for urgent reversal of anticoagulation with warfarin, but the optimal 4F-PCC dosing approach is unknown. Herein, we sought to determine the efficacy of a novel fixed, weight-based dosing nomogram.Methods We retrospectively studied consecutive adult patients receiving fixed, weight-based 4F-PCC dosing for warfarin reversal between 30 April 2009 and 31 December 2010. The primary outcome was reversal of warfarin anticoagulation, defined as INR ≤1.5 within 6 h. Secondary outcome was the occurrence of thromboembolic events.Results A total of 227 patients (56% male), with a median age of 74 years and a median weight of 76kg were evaluated. The most common indications for 4F-PCC were active bleeding (37.4%: 12.7% intracranial, 12.3% gastrointestinal, 4.0% trauma, 8.4% other), reversal for a procedure (22.0%), reversal for surgery (29.5%) or other (11.1%). 66.1% of patients achieved an INR ≤1.5 within 6 h of 4F-PCC administration. 95.0% (57/60) of patients completed a planned procedure and 95.7% (67/70) of patients completed a planned surgery. The median baseline INR was 2.9 (1.5–10) and decreased significantly to a median of 1.3 (1.0–3.7) (p < .001) post-4F-PCC administration. There was no statistically significant difference in response to a fixed, weight-based dose of 4F-PCC based on pre-PCC INR, as long as the pre-treatment INR was ≤ 4.5. Although the majority of patients in our study (99%) received doses over 1000IU, rates of thrombosis were low (1.8%).Conclusion Fixed, weight-based dosing of 4F-PCC is effective for reversing warfarin anticoagulation in patients with a pre-dosing INR ≤ 4.5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".