Reversal of antithrombotics in the critically ill: An international online survey
Bibliographic record
Abstract
PURPOSE: Critically ill patients face an increased risk of both thrombotic and bleeding complications, necessitating careful administration of antithrombotic agents such as platelet aggregation inhibitors (PAI), anticoagulants and fibrinolytics for prophylactic and therapeutic purposes, but also posing challenges for reversal strategies. This survey aims to assess the current clinical practice of reversal of antithrombotics in the intensive care unit (ICU). METHODS: An international online 79-item survey was performed among critical care physicians. The survey was disseminated via multiple intensive care societies. Reversal practices for PAI, vitamin K antagonists (VKA), heparins, factor Xa inhibitors, direct thrombin inhibitors (DTI) and fibrinolytics were surveyed. RESULTS: From June 2023 to January 2024, 477 participants started the survey, with 208 completed surveys from 49 countries. The majority (79 %) of respondents practiced ICU medicine in Europe. Only 17 % of the included participants indicated the presence of an ICU-specific antithrombotic reversal protocol in their hospital. Of those, specific protocols were present for 92 % for reversal of VKA, 75 % for unfractioned heparin, 58 % for low-molecular-weight heparin, 53 % for factor Xa inhibitors, 50 % for PAI, 44 % for DTI and 31 % for fibrinolytics. There was heterogeneity in reported reversal practice for different antithrombotics in specific scenarios and between continents. However, dosing strategies of applicable reversal agents were similar. CONCLUSION: This survey shows variability in the reported clinical approaches to reverse antithrombotic agents in the ICU. The majority of hospitals included do not have a specific protocol for antithrombotic agents reversal emphasizing the need for ICU specific guidelines.
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 | 0.001 | 0.007 |
| 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".