Biomarkers of Inflammation and Risk of Hospitalization for Heart Failure in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background Hospitalization for heart failure (HF) is very common in patients with atrial fibrillation (AF). We hypothesized that biomarkers of inflammation can identify patients with AF at increased risk of this important complication. Methods and Results Patients with established AF were prospectively enrolled. Levels of hs‐CRP (high‐sensitivity C‐reactive protein) and interleukin‐6 were measured from plasma samples obtained at baseline. We calculated an inflammation score ranging from 0 to 4 (1 point for each biomarker between the 50th and 75th percentile, 2 points for each biomarker above the 75th percentile). Individual associations of biomarkers and the inflammation score with HF hospitalization were obtained from multivariable Cox proportional hazards models. A total of 3784 patients with AF (median age 72 years, 24% prior HF) were followed for a median of 4.0 years. The median (interquartile range) plasma levels of hs‐CRP and interleukin‐6 were 1.64 (0.81–3.69) mg/L and 3.42 (2.14–5.60) pg/mL, respectively. The overall incidence of HF hospitalization was 3.04 per 100 person‐years and increased from 1.34 to 7.31 per 100 person‐years across inflammation score categories. After multivariable adjustment, both biomarkers were significantly associated with the risk of HF hospitalization (per increase in 1 SD, adjusted hazard ratio [HR], 1.22; 95% CI, 1.11–1.34 for log‐transformed hs‐CRP; adjusted HR, 1.48; 95% CI, 1.35–1.62 for log‐transformed interleukin‐6). Similar results were obtained for the inflammation score (highest versus lowest score, adjusted HR, 2.43; 95% CI, 1.80–3.30; P value for trend <0.001). Conclusions Biomarkers of inflammation strongly predicted HF hospitalization in a large, contemporary sample of patients with AF. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02105844.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".