Biomarkers for predicting atrial fibrillation: An explorative sub-analysis of the randomised SCREEN-AF trial
Bibliographic record
Abstract
Background Atrial fibrillation (AF) is a common treatable risk factor for stroke. Screening for paroxysmal AF in general practice is difficult, but biomarkers might help improve screening strategies.Objectives We investigated six blood biomarkers for predicting paroxysmal AF in general practice.Methods This was a pre-specified sub-study of the SCREEN-AF RCT done in Germany. Between 12/2017-03/2019, we enrolled ambulatory individuals aged 75 years or older with a history of hypertension but without known AF. Participants in the intervention group received active AF screening with a wearable patch, continuous ECG monitoring for 2x2 weeks and usual care in the control group. The primary endpoint was ECG-confirmed AF within six months after randomisation. High-sensitive Troponin I (hsTnI), brain natriuretic peptide (BNP), N-terminal pro-B-type natriuretic peptide (NT-pro BNP), N-terminal pro atrial natriuretic peptide (NT-ANP), mid-regional pro atrial natriuretic peptide (MR-pro ANP) and C-reactive protein (CRP) plasma levels were investigated at randomisation for predicting AF within six months after randomisation.Results Blood samples were available for 291 of 301 (96.7%) participants, including 8 with AF (3%). Five biomarkers showed higher median results in AF-patients: BNP 78 vs. 41 ng/L (p = 0.012), NT-pro BNP 273 vs. 186 ng/L (p = 0.029), NT-proANP 4.4 vs. 3.5 nmol/L (p = 0.027), MR-pro ANP 164 vs. 125 pmol/L (p = 0.016) and hsTnI 7.4 vs. 3.9 ng/L (p = 0.012). CRP levels were not different between groups (2.8 vs 1.9 mg/L, p = 0.1706).Conclusion Natriuretic peptide levels and hsTnI are higher in patients with AF than without and may help select patients for AF screening, but larger trials are needed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".