Comparative effectiveness of novel oral anticoagulants in UK patients with non-valvular atrial fibrillation and chronic kidney disease: a matched cohort study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness and safety of novel oral anticoagulants (NOACs) compared with vitamin K antagonists (VKAs) among patients with non-valvular atrial fibrillation (NVAF), particularly those with chronic kidney disease (CKD). DESIGN: Population-based matched cohort study. SETTING: Over 670 primary care practices in the UK, contributing to the Clinical Practice Research Datalink. PARTICIPANTS: Up to 6818 adult patients newly treated with NOACs between 2011 and 2016, matched 1:1 to new users of VKAs on age, sex and high-dimensional propensity score. INTERVENTIONS: Current exposure to NOACs compared with current exposure to VKAs. MAIN OUTCOME MEASURES: HRs of ischaemic stroke and systemic embolism (SE), major bleeding, gastrointestinal (GI) bleeding, intracranial bleeding, myocardial infarction and all-cause mortality. RESULTS: In as-treated analyses, the rates of ischaemic stroke/SE were similar between NOACs and VKAs (HR 0.94; 95% CI 0.62 to 1.42), as were the rates of major bleeding (HR 0.86; 95% CI 0.56 to 1.33). NOACs also significantly increased the risk of GI bleeding (HR 1.78; 95% CI 1.27 to 2.48). In patients with NVAF and CKD, NOACs and VKAs remained comparable with respect to the risk of ischaemic stroke/SE (HR 0.79; 95% CI 0.40 to 1.58) and major bleeding (HR 0.88; 95% CI 0.47 to 1.62), with no difference in the risk of GI bleeding (HR 0.99; 95% CI 0.63 to 1.55). Similar results were obtained in on-treatment analyses using a time-dependent exposure definition. CONCLUSIONS: Our results suggest that in the UK primary care, NOACs are overall effective and safe alternatives to VKAs, among patients with NVAF altogether, as well as in patients with NVAF and CKD.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".