Update on antithrombotic therapy and body mass: a clinical consensus statement of the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy and the European Society of Cardiology Working Group on Thrombosis
Bibliographic record
Abstract
Obesity and underweight are a growing health problem worldwide and a challenge for clinicians concerning antithrombotic therapy, due to the associated risks of thrombosis and/or bleeding. This clinical consensus statement updates a previous one published in 2018, by reviewing the most recent evidence on antithrombotic drugs based on body size categories according to the World Health Organization classification. The document focuses mostly on individuals at the extremes of body weight, i.e. underweight and moderate-to-morbid obesity, who require antithrombotic drugs, according to current guidelines, for the treatment or prevention of cardiovascular diseases or venous thromboembolism. Managing antithrombotic therapy or thromboprophylaxis in these individuals is challenging, due to profound changes in body composition, metabolism and organ function, and altered drug pharmacokinetics and pharmacodynamics, as well as weak or no evidence from clinical trials. The document also includes artificial intelligence simulations derived from in silico pharmacokinetic/pharmacodynamic models, which can mimic the pharmacokinetic changes and help identify optimal regimens of antithrombotic drugs for severely underweight or severely obese individuals. Further, bariatric surgery in morbidly obese subjects is frequently performed worldwide. Bariatric surgery causes specific and additional changes in metabolism and gastrointestinal anatomy, depending on the type of the procedure, which can also impact the pharmacokinetics of antithrombotic drugs and their management. Based on existing literature, the document provides consensus statements on optimizing antithrombotic drug management for underweight and all classes of obese patients, while highlighting the current gaps in knowledge in these complex clinical settings, which require personalized medicine and precision pharmacology.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".