153: Fibrinogen Concentrate Dosing Guidelines to Treat Acquired Hypofibrinogenemia in Pediatric ECMO
Bibliographic record
Abstract
Introduction: Acquired hypofibrinogenemia may occur in patients supported with ECMO. It is commonly treated using cryoprecipitate or fibrinogen concentrate. There is very limited data on the use of fibrinogen concentrate in children on ECMO. We developed a guideline for replacement using fibrinogen concentrate. Methods: We developed and deployed an institutional guidelines for the administration of fibrinogen concentrate. All administrations were evaluated to measure the efficacy in increasing fibrinogen activity levels. Results: Between April 2022 to February 2024, 22 patients received fibrinogen concentrate (RIASTAP®). The indication for ECMO was for cardiac support in 11, pulmonary support in 4, and ECPR in 7. Median age was 2.3 m (IQR: 72 days, 1-549 days), weight 3.9 kg (IQR: 3.4-10.6kg), and first dose was 50 mg/kg (range 31-70 mg/kg). Three potential types of membranes and circuit volumes (250 ml, 320 ml, 600 ml) were used. The median increase in fibrinogen activity following the first dose was 0.4 g/L (IQR: 0.1-0.7). No thrombotic nor circuit complications were reported attributable to the delivery of the product. Conclusion and future directions: Clinicians chose to deliver the dose calculation option per weight over a target level option. The increase in fibrinogen activity achieved was modest. Given the safety profile of the current dosage, we propose to increase the dose. We will evaluate the impact on the co-administration of other replacement products and on the magnitude of bleeding. As there was no uptake of the target-based dose calculation, this option will be removed to minimize confusion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".