A systematic review on the influence of coagulopathy and immune activation on New Onset Atrial Fibrillation in patients with sepsis
Bibliographic record
Abstract
INTRODUCTION: New Onset Atrial Fibrillation (NOAF) is the most common arrhythmia in intensive care. Complications of NOAF include thromboembolic events such as myocardial infarction and stroke, which contribute to a greater risk of mortality. Inflammatory and coagulation biomarkers in sepsis are thought to be associated with NOAF development. The aim of this systematic review and narrative synthesis is to identify inflammatory and coagulation biomarkers as predisposing risk factors for NOAF in sepsis. METHODS: Three databases (Medline, Cochrane Library, and Scopus) were searched using a predefined search strategy. Inclusion / exclusion criteria were applied, and quality assessments were performed using the Newcastle Ottawa Scale (NOS). RESULTS: We identified 1776 articles; and 12 articles were included in this review. 8 articles were retrospective observational studies and 4 were prospective observational studies. There was considerable heterogeneity between studies regarding outcomes, methodological design, quality, definitions and reported biomarkers of interest. There is evidence that C-reactive protein (CRP) is associated with NOAF, with hazard ratios 3.33 (3.32-3.35) p = 0.001 and odds ratios of 1.011 (1.008-1.014) p<0.001. International Normalised Ratio (INR) and fibrinogen may be associated with NOAF with odds ratios reported as 1.837 (1.270-2.656) p = 0.001 and 1.535(1.232-1.914) p<0.001 respectively. CONCLUSION: Further research is required to confirm the association between inflammatory and coagulation biomarkers and the development of NOAF in sepsis. A broader evidence base will guide treatment strategies, improving the standard of care for patients who develop NOAF in sepsis. Furthermore, given the heterogeneity between studies consideration should be given to inclusion of immune biomarkers in future core outcome sets for trials investigating NOAF.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".