Incidence and Predictors of Heart Failure in Patients With Atrial Fibrillation
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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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".