Systematic review and meta-analysis of the co-occurrence of atrial fibrillation and liver transplantation: a lethal combination
Bibliographic record
Abstract
INTRODUCTION: This systematic review and meta-analysis is aimed to evaluate the role of new-onset atrial fibrillation (NOAF) in patients after liver transplantation (LT) and determine the effect of NOAF on the incidence of mortality and graft rejection. MATERIAL AND METHODS: Published studies until the end of April 15, 2023, were systematically searched in PubMed, Google Scholar, Scopus, Embase, Web of Science, and the Cochrane databases. Odds ratios (ORs) with 95% confidence intervals (CI) for mortality and graft rejection were extracted. RESULTS: Five studies with a total of 4788 unique post-LT patients were included in the meta-analysis. Pooled analysis showed that mortality in patients with and without NOAF varied and amounted to 24.1% vs. 12.5%, respectively (OR = 2.51; 95%CI: 1.92 to 3.27; p < 0.001). Moreover, pooled analysis showed that graft rejection in the NOAF cohort was 26.3%, and was higher vs. patients without NOAF (13.1%; OR = 2.98; 95%CI: 2.14 to 4.15; p < 0.001) CONCLUSIONS: Post-LT NOAF is associated with increased mortality and a higher risk of graft rejection. It is likely that the development of a standard procedure for early identification of NOAF, as well as to develop recommendations for specific treatment targeted at avoiding the impacts of the illness, could provide a mortality reduction and provide an increased rate of successful LT.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".