Blood Pressure and Atrial Fibrillation: A Combined AF‐CHF and AFFIRM Analysis
Bibliographic record
Abstract
BACKGROUND: Hypertension is an established risk factor for new-onset atrial fibrillation (AF). However, the relationship between blood pressure and recurrent AF is less well understood. METHODS AND RESULTS: A pooled analysis of patient-level data from AFFIRM and AF-CHF trials was conducted on all 2,715 patients with paroxysmal or persistent AF, 68 ± 8 years, 66% male, randomized to rhythm control and followed for 40.6 ± 16.5 months. We assessed the impact of a baseline systolic blood pressure (SBP; <120 mmHg [N = 1,008], 120-140 mmHg [N = 930], >140 mmHg [N = 777]) on recurrent AF and proportion of time spent in AF. In patients with LVEF >40% (N = 1,719), SBP was not associated with recurrent AF in multivariate regression analyses (P = 0.752). In contrast, in patients with LVEF ≤40% (N = 996), the AF recurrence rate was higher in those with an SBP >140 mmHg compared to 120-140 mmHg (hazard ratio 1.47; 95% CI [1.12-1.93], P = 0.005). The rate of recurrent AF was similar in patients with SBP <120 mmHg compared to 120-140 mmHg (hazard ratio 1.15; 95% CI [0.92-1.43], P = 0.225). Consistently, the proportion of time spent in AF was not influenced by SBP in patients with LVEF >40% (P = 0.645). However, in patients with LVEF ≤40%, the adjusted mean proportion of time spent in AF was 17.2% if SBP was <120 mmHg, 15.4% for SBP 120-140 mmHg, and 24.0% for SBP >140 mmHg (P = 0.025). CONCLUSION: Systolic blood pressure is an important determinant of recurrent AF and overall AF burden in patients with left ventricular dysfunction (LVEF≤40%) but not in those with preserved ventricular function.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.025 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".