External Validation of the REACT‐HF Score for Predicting Heart Failure in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Validated risk prediction scores for incident heart failure (HF) in patients with atrial fibrillation are lacking. We aimed to externally validate the the HF prediction risk score derived from three large control randomized trials RE-LY, AVERROES, and ACTIVE-A (REACT-HF) score and assess potential improvements by incorporating biomarkers. METHODS: We included 2599 patients with atrial fibrillation without prior HF from the Swiss-AF (Swiss Atrial Fibrillation) and BEAT-AF (Basel Atrial Fibrillation) cohorts. We estimated the C statistics of the REACT-HF score with Cox proportional hazards models and improved prediction by adding hs-CRP (high-sensitivity C-reactive protein), NT-proBNP (N-terminal pro-B-type natriuretic peptide), and high-sensitivity troponin T. The primary outcome was incident HF hospitalization within 2 years. Secondary outcomes included cardiovascular death, a composite of incident HF hospitalization and cardiovascular death, and all-cause death. RESULTS: The mean age was 70.2±10.3 years, 29.1% were women, and 54.8% had paroxysmal atrial fibrillation. Across risk score quintiles, the incidence rates per 100 patient-years increased for the primary outcome (0.27, 0.54, 1.00, 2.24, 5.49), cardiovascular death (0.00, 0.11, 0.10, 0.91, 2.01), the composite of cardiovascular death and first HF hospitalization (0.27, 0.65, 1.10, 3.09, 6.86), and all-cause death (0.00, 0.65, 0.40, 1.56, 4.10). The estimated C statistic (95% CI) for the primary outcome was 0.76 (0.72-0.81). C statistics for the secondary outcomes were consistent. The biomarker-enhanced model, including hs-CRP and NT-proBNP, improved the C statistic to 0.84 (0.80-0.87). CONCLUSIONS: In this external validation, the REACT-HF risk score demonstrated good discrimination for predicting the first HF hospitalization within 2 years of follow-up. The addition of NT-proBNP and hs-CRP further improved the score. The REACT-HF score may help identify patients with atrial fibrillation at risk for HF, aiding in preventive therapy.
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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 | 0.039 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".