Antithrombotic drug removal from whole blood using Haemoadsorption with a porous polymer bead sorbent
Bibliographic record
Abstract
AIM: To evaluate the ability of the DrugSorb™-AntiThrombotic Removal (ATR) haemoadsorption device utilizing porous polymer bead sorbent technology to remove three commonly used antithrombotic drugs from whole blood. METHODS AND RESULTS: We evaluated the removal of apixaban, rivaroxaban, and ticagrelor by the DrugSorb-ATR haemoadsorption device in a benchtop clinical scale model using bovine whole blood. Blood spiked at clinically relevant concentrations of an antithrombotic agent was continuously circulated through a 300-mL DrugSorb-ATR haemoadsorption device at a flow rate of 300 mL/min. Drug concentration was monitored over 6 h to evaluate drug removal. Results were compared with a control circuit without the haemoadsorption device. Removal rates at 30, 60, 120, and 360 minutes were: apixaban: 81.5%, 96.3%, 99.3% >99.8%; rivaroxaban: 80.7%, 95.1%, 98.9%, >99.5%; ticagrelor: 62.5%; 75%, 86.6%, >95% (all P <0.0001 vs. control). Blood pH and haematological parameters were not significantly affected by the DrugSorb-ATR haemoadsorption device when compared with the control circuit. CONCLUSION: DrugSorb-ATR efficiently removes apixaban, rivaroxaban, and ticagrelor in a clinical-scale benchtop recirculation circuit with the bulk of removal occurring in the first 60 minutes. The clinical implications of these findings are currently investigated in patients undergoing on-pump cardiothoracic surgery in two US pivotal trials (ClinicalTrials.gov Identifiers: NCT04976530 and NCT05093504).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 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".