Systematic Review and Meta-Analysis on the Impact of Atrial Fibrillation on Outcomes in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic inflammatory condition affecting the gastrointestinal tract, often leading to symptoms like abdominal pain and diarrhea. Given the increasing evidence linking systemic inflammation to atrial fibrillation development, investigating IBD as a potential risk factor for atrial fibrillation becomes imperative. This meta-analysis aims to evaluate the impact of atrial fibrillation on inpatient outcomes, resource utilization, and length of hospital stays among IBD patients. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guidelines, a systematic literature search was conducted across multiple databases, including Embase, PubMed, Scopus, and Web of Science, from the inception of databases to June 5, 2024. Eligible studies included prospective or retrospective studies with definitive diagnoses of ulcerative colitis, Crohn's disease, or IBD, demonstrating the influence of atrial fibrillation. Data were extracted, and quality assessment was performed using the Newcastle-Ottawa Scale. The meta-analysis comprised 842,149 IBD patients, with 71,221 having atrial fibrillation. Pooled analysis revealed a significant association between atrial fibrillation and heightened all-cause mortality risk (risk ratio (RR): 1.42, 95% confidence interval (CI): 1.16 to 1.74, p<0.01). However, no significant differences were observed in the incidence of acute myocardial infarction, acute kidney injury, or acute respiratory failure between patients with and without atrial fibrillation. IBD patients with comorbid atrial fibrillation face higher mortality rates, potentially due to systemic inflammation, thromboembolism risks, polypharmacy, and the complexities of managing both conditions concurrently. Early identification and integrated management of atrial fibrillation in IBD patients are crucial to improving outcomes. Larger, multi-center studies are needed to explore the underlying mechanisms and develop tailored treatment strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| 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".