Review article: diagnosis, pathophysiology and management of atrial fibrillation in cirrhosis and portal hypertension
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia and its management in cirrhosis can be challenging due to the altered hepatic metabolism of medications and increased risk of bleeding. AIMS: To provide a comprehensive overview of the diagnosis, pathophysiology and management of AF in patients with cirrhosis from both a cardiology and a hepatology perspective. METHODS: An extensive literature search was performed using the terms 'atrial fibrillation' and 'cirrhosis'. Guideline documents and consensus statements were explored. RESULTS: The prevalence of AF in patients with cirrhosis ranges between 6.6% and 14.2%, while the incidence of new-onset AF in the post-operative period after liver transplant ranged between 6.8% and 10.2%. AF in patients with cirrhosis is associated with adverse outcomes in both pre-transplant and post-transplant settings, including an increased risk of stroke when compared to the general population. We review the pathogenesis of AF in general and in cirrhosis. This review also provides guidance on the management of AF, including the use of anticoagulation and rate versus rhythm control. In the absence of strict contraindications, all patients with cirrhosis and AF should be anticoagulated. The use of DOACs is preferred over vitamin K antagonists. In patients with a high bleeding risk, a DOAC with an approved antidote may be preferred. CONCLUSIONS: Atrial fibrillation is increased in patients with cirrhosis. AF management requires careful consideration of treatment options. Since patients with cirrhosis were excluded from all major randomised clinical trials, dedicated research on the pathophysiology and management of AF in cirrhosis is needed.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".