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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".