Abstract 18357: Outcomes and Quality of Anticoagulant Control in Patients Newly Diagnosed with Non-valvular Atrial Fibrillation: Insights from the Worldwide GARFIELD Registry
Bibliographic record
Abstract
Background: Prevalence of atrial fibrillation (AF) is increasing due to an aging population. Most AF patients require long-term anticoagulant treatment to prevent stroke. Consequences of insufficient or excessive anticoagulation with vitamin K antagonists (VKAs) can be serious. Time within therapeutic INR range (TTR) evaluates the efficacy and quality of INR control. It remains unclear whether better anticoagulation control (TTR >60%) is associated with lower thrombotic and bleeding events in real-world clinical practice. Hypothesis: The incidence of thrombotic and bleeding events in anticoagulated AF patients treated with VKAs is increased when INR is not well controlled (TTR <60%). Methods: Patients were recruited at 543 sites selected at random in accordance with nationally defined AF care settings. A total of 10,609 patients newly diagnosed with non-valvular AF and with ≥1 additional investigator-determined stroke risk factor were recruited consecutively between Dec 2009 and Oct 2011. TTR was calculated by percentage of INR recordings in target range. Results: In 6047 patients treated with VKAs, 3952 had INR recordings available at the time of this analysis. TTR was >60% in 1660 (42.0%) of these patients. A total of 55,257 INR measurements were recorded. Event rates and monitoring frequency in relation to quality of INR control are shown in the Table. Mean length of follow-up was 15.2 (SD 6.9) months. Patients with INR not well controlled were more likely to have a stroke/TIA, a major bleed, an ICH and death than patients with INR well controlled. Conclusions: These observational data support the findings of randomized clinical trials, by indicating that INR control is instrumental in determining adverse event rates in patients treated with VKAs, but are not consistent with the post-hoc comparisons of novel OACs with warfarin. Table: Event rates and INR recordings in relation to TTR.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.001 |
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".