Managing new-onset atrial fibrillation in critically ill patients: a systematic narrative review
Bibliographic record
Abstract
OBJECTIVES: The aim of this review is to summarise the latest evidence on efficacy and safety of treatments for new-onset atrial fibrillation (NOAF) in critical illness. PARTICIPANTS: Critically ill adult patients who developed NOAF during admission. PRIMARY AND SECONDARY OUTCOMES: Primary outcomes were efficacy in achieving rate or rhythm control, as defined in each study. Secondary outcomes included mortality, stroke, bleeding and adverse events. METHODS: We searched MEDLINE, EMBASE and Web of Knowledge on 11 March 2019 to identify randomised controlled trials (RCTs) and observational studies reporting treatment efficacy for NOAF in critically ill patients. Data were extracted, and quality assessment was performed using the Cochrane Risk of Bias Tool, and an adapted Newcastle-Ottawa Scale. RESULTS: Of 1406 studies identified, 16 remained after full-text screening including two RCTs. Study quality was generally low due to a lack of randomisation, absence of blinding and small cohorts. Amiodarone was the most commonly studied agent (10 studies), followed by beta-blockers (8), calcium channel blockers (6) and magnesium (3). Rates of successful rhythm control using amiodarone varied from 30.0% to 95.2%, beta-blockers from 31.8% to 92.3%, calcium channel blockers from 30.0% to 87.1% and magnesium from 55.2% to 77.8%. Adverse effects of treatment were rarely reported (five studies). CONCLUSION: The reported efficacy of beta-blockers, calcium channel blockers, magnesium and amiodarone for achieving rhythm control was highly varied. As there is currently significant variation in how NOAF is managed in critically ill patients, we recommend future research focuses on comparing the efficacy and safety of amiodarone, beta-blockers and magnesium. Further research is needed to inform the decision surrounding anticoagulant use in this patient group.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".