New-Onset Atrial Fibrillation in Sepsis: A Narrative Review
Bibliographic record
Abstract
Atrial fibrillation (AF) is a frequently identified arrhythmia during the course of sepsis. The aim of this narrative review is to assess the characteristics of patients with new-onset AF related to sepsis and the risk of stroke and death, to understand if there is a need for anticoagulation. We searched for studies on AF and sepsis on PubMed, the Cochrane database, and Web of Science, and 17 studies were included. The mean incidence of new-onset AF in patients with sepsis was 20.6% (14.7% in retrospective studies and 31.6% in prospective). Risk factors for new-onset AF included advanced age, white race, male sex, obesity, history of cardiopulmonary disease, heart or respiratory failure, and higher disease severity score. In-hospital mortality was higher in patients with than in those without new-onset AF in 10 studies. In four studies the overall intensive care unit and hospital mortality rates were comparable between patients with and without new-onset AF, while three other studies did not provide mortality data. One study reported on the in-hospital incidence of stroke, which was 2.6 versus 0.69% in patients with or without new-onset AF, respectively. Seven of the studies provided follow-up data after discharge. In three studies, new-onset AF was associated with excess mortality at 28 days, 1 year, and 5 years after discharge of 34, 21, and 3% patients, respectively. In two studies, the mortality rate was comparable in patients with and without new-onset AF. Postdischarge stroke was reported in five studies, whereof two studies had no events after 30 and 90 days, one study showed a nonsignificant increase in stroke, and two studies demonstrated a significant increase in risk of stroke after new-onset AF. The absolute risk increase was 0.6 to 1.6%. Large prospective studies are needed to better understand the need for anticoagulation after new-onset AF in sepsis.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".