Glomerular filtration rate: A prognostic marker in atrial fibrillation—A subanalysis of the AntiThrombotic Agents Atrial Fibrillation
Bibliographic record
Abstract
Objective An increased cardiovascular mortality and morbidity has been widely reported in patients with atrial fibrillation (AF). In this study, a subanalysis of the AntiThrombotic Agents Atrial Fibrillation (ATA‐AF) is performed with the aim to evaluate estimated glomerular filtration rate (eGFR) as an independent prognostic marker of cardiovascular mortality and morbidity in patients with AF. Methods and Results The ATA‐AF study enrolled 7148 patients with AF, in 360 Italian centers. The eGFR was calculated from data reported in patient notes or hospital database. This post‐hoc analysis included 1097 AF patients with eGFR data available and 1‐year clinical follow‐up. The endpoint was assessed as cardiovascular mortality and/or hospital admission for cardiovascular causes at follow‐up. Patients were also divided in two groups according to the eGFR (<60 and ≥60 mL/min/1.73 m2). The Kaplan‐Meyer curve for the mentioned endpoint showed a higher endpoint incidence in the group of patient with eGFR below 60 mL/min/1.73 m2 (P < 0.001). Using multivariate analysis (Cox regression), a trend toward a higher rate of occurrence of the primary endpoint was observed for eGFR below 60 mL/min/1.73 m2 without reaching the conventional level of statistical significance (hazard ratio [HR] 1.40; 95% confidence interval [CI] 0.99‐1.99; P = 0.0572). When eGFR was included in the analysis as continuous variable a significant correlation was observed with the combined endpoint at the Cox regression (HR 0.99, 95% CI 0.98‐0.99, P = 0.04). Conclusion The result of this post‐hoc analysis indicates that an impaired eGFR is independently associated with worse prognosis among patients with AF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".