The management of anticoagulants in patients with atrial fibrillation and history of falls or risk of falls: The Liverpool AF-Falls Project. A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Johnson and Johnson Medical Background Atrial fibrillation affects an estimated 33 million individuals worldwide and a major cause of stroke, heart failure, and death. Treatment with anticoagulants substantially reduces risk of stroke but is also associated with an increased risk of bleeding and especially intracranial haemorrhages which are the most feared complication. Because of that many patients do not receive anticoagulants; particularly patients at risk of falls or with history of falls. It is unclear what anticoagulant treatment these patients should be offered, and the Liverpool AF-Falls Project aims to investigate this area. Purpose This systematic review and meta-analysis aimed to determine the most appropriate anticoagulant treatment option for the management of atrial fibrillation patients at risk of falls or with a history of falls. Methods We conducted a systematic review and meta-analysis, including studies evaluating safety and efficacy of different anticoagulants (Vitamin K Antagonist-VKA- versus Non-Vitamin K Antagonist Oral Anti-Coagulants-NOAC). Outcomes were ischemic stroke, major bleeding, intracranial haemorrhage, haemorrhagic stroke and mortality. Bibliographic databases (CENTRAL, CINAHL, ClinicalTrials.gov, EMBASE, MEDLINE, Scopus and Web of Science) were searched. Two independent reviewers identified studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias 2 tool for randomized clinical trials and with the Newcastle-Ottawa-Scale for observational studies. Pairwise meta-analysis with random and fixed effects models were conducted. Heterogeneity was assessed with the I2 statistics. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were used to assess the effect of drugs on efficacy and safety. Results 823 articles were identified, 643 after removing duplicates. 95 were screened for full text and 3 articles were retained for final quantitative synthesis including 26,514 patients. Risk of bias was moderate in Rao et al. 2018 and Steffel et al. 2017, and low in Miao et al. 2019. In meta-analysis, the hazard for intracranial haemorrhage was lower with NOACs compared to VKA (hazard ratio (HR) 0.33, 95% confidence interval (CI) [0.13–0.82]; p<0,001; I²=52%). There were no difference between NOACs and VKA regarding risks in ischemic stroke (HR 0.88, 95%CI [0.70–1.10; p=0.25; I²=0%), major bleeding (HR 0.88, 95%CI [0.62–1.27]; p=0.51, I²=0%); haemorrhagic stroke (HR 0.36, 95%CI [0.11–1.13]; p=0.08; I²=0%) and all-cause mortality (HR 0.95, 95%CI [0.67–1.33]; p=0.75; I² = 0%). Conclusions NOACs were associated with less intracranial haemorrhages than VKAs. There were no statistically significant differences in other outcomes. However, limited number of studies were identified suggesting research gaps in the AF patients with increased falling risk or history of falls, requiring careful interpretation pending more evidence.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".