Impact of NOAC and VKA on outcome of patients with newly diagnosed atrial fibrillation and diabetes: a report from the GARFIELD-AF registry
Bibliographic record
Abstract
Abstract Introduction Diabetes mellitus (DM) is one of the most common comorbidities observed in patients with atrial fibrillation (AF). It is associated with increased risks of stroke/SE and death. Purpose To assess the impact of NOAC and VKA on outcomes of newly diagnosed AF in patients with DM and 2-year follow-up. Methods The study population comprised 52,010 patients with newly diagnosed AF, 11,542 DM and 40,468 non-DM, who were enrolled in GARFIELD-AF, the largest multinational prospective AF registry. Adjusted hazard ratios (HRs) were obtained through Cox proportional-hazard models to quantify the effects of diabetes on death, stroke/SE and major bleeding. Comparative effectiveness analyses were restricted to 18,373 patients eligible for oral anticoagulation (OAC) enrolled between 2013- 2016 after NOACs became available in most countries. Safety and effectiveness of NOAC and VKA in DM and non-DM patients were assessed with propensity scores using an overlap weighting scheme. Weights were applied to Cox proportional-hazards models to estimate the effects of NOAC vs VKA use for each endpoint. Results Compared to non-DM patients, DM patients (44.5% with oral antidiabetic drug, 13.4% with insulin) had higher BMI (28.7 vs 26.4), more frequent history of heart failure (24.8% vs 21.9%), acute coronary syndromes (15.9% vs 9.2%), vascular disease (32.6% vs 22.6%), stroke/TIA/SE (12.8% vs 10.9%), hypertension (85.7% vs 73.7%), hypercholesterolemia (55.3% vs 37.7%), moderate to severe CKD (14.4% vs 9.6%). They had higher rates of OAC use (70.5% vs 65.8%), were more often treated with VKA (44.0% vs 38.0%), or AP in combination with OAC or as monotherapy (40.6% vs 33.8%), were at higher risk of stroke/SE according to CHA2DS2-VASc score [4.0 (3.0; 5.0) vs (3.0 (2.0; 4.0)], had at 2-year follow-up significantly higher rates of all-cause death, stroke/SE, and major bleeding (Figure 1). Overall, OAC vs no OAC led in both non-DM and DM populations to similar risk reduction for death [HR 0.75 (0.69–0.83) vs 0.74 (0.64–0.86)], stroke/SE [0.69 (0.58–0.83) vs 0.70 (0.53–0.93)], and similar increase in major bleeding risk [HR 1.40 (1.14–1.71) vs 1.37 (0.99–1.89)]. NOAC use trended toward lower all-cause death (significant for non-DM but not for DM) and major bleeding rates than VKA in both non-DM and DM patients. A non-significant increase in stroke/SE rates was observed in DM patients (Figure 2). Conclusion At baseline, DM patients had higher rates of OAC prescription. They had higher rates of all cause, cardiovascular and non-cardiovascular death, stroke/SE, and major bleeding than non-DM patients. Lower rates of all-cause death and of major bleeding were observed with NOACs compared with VKAs in both DM and non-DM patients and of stroke/SE in non-DM patients. These results tend to show that in DM AF patients, NOACs should be favoured over VKA, though higher rates of CKD in DM patients may limit the uptake of such drugs. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): The GARFIELD-AF registry was funded by an unrestricted research grant from Bayer AG. This work is supported by KANTOR CHARITABLE FOUNDATION for the Kantor-Kakkar Global Centre for Thrombosis Science.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".