Incidence and Predictors of Heart Failure in Patients With Atrial Fibrillation
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Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a frequent cause of hospitalization and death in patients with atrial fibrillation (AF). Identifying AF patients at risk of HF hospitalization could help select individuals for intensive follow-up and treatment. METHODS: We pooled data from 3 randomized trials (ACTIVE-A, RE-LY, AVERROES) of AF patients, for derivation and internal validation of a risk score for first HF hospitalization. Secondary endpoints were cardiovascular death and a composite of HF hospitalizations and cardiovascular death. RESULTS: In 23,503 patients, the mean age was 71.3 years, and 62% were male. Over a mean follow-up of 2.0 years, 875 patients (3.7%) experienced their first HF hospitalization, and 1037 patients (4.4%) died from cardiovascular causes. Incidence rates per 100 patient-years were 1.85 for HF hospitalizations, 2.15 for cardiovascular death, and 3.71 for the composite. Independent predictors for HF hospitalizations included the following: increased age, weight, heart rate and serum creatinine level, lower height and systolic blood pressure, diabetes, vascular disease, valvular disease, heart rhythm, left ventricular hypertrophy, and intraventricular conduction delay. The C-statistic (95% confidence intervals by bootstrap simulations) was 0.717 (0.705-0.732). At 2 years of follow-up, the incidence rate of the primary outcome increased across risk-score quintiles: 0.49, 0.87, 1.29, 2.44, and 4.51 per 100 patient-years, respectively. Patients in the highest quintile had an absolute risk of 6.8% for the primary endpoint at 2 years. CONCLUSIONS: In a large AF population, new-onset HF was common. A combination of characteristics can identify high-risk patients for whom strategies to prevent HF should be considered.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it