Blood biomarkers and development of Atrial Fibrillation within 1 year, in frail older patients. Outcomes of the Dutch-GERAF study
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) is a common arrhythmia with increased prevalence in frail elderly populations. The aim of this study was to determine whether the risk factors for AF differ with increasing frailty, adjusted for age and sex. Identifying patients at risk for AF development can enhance the effectiveness of screening programs, as demonstrated in the STROKESTOP-II study. Frail elderly patients, frequently referred to geriatric outpatient clinics, often present with multi-morbidity, including cardiovascular diseases. Classical risk factors for AF may lose their discriminative value in frail patients due to their high prevalence. The GERAF cohort provides an opportunity to assess the relationship between classical and other potential risk factors and the development of AF within 1 year. Methods This analysis included GERAF data at the 1-year follow-up. Stratified by frailty status (robust, moderately frail, severely frail), regression analysis was performed to assess the association between risk factors and new-onset AF, adjusted for age and sex. AF incidence was measured over the course of 1 year. Results Among 751 patients with sinus rhythm at study entry, 31 new cases of AF were identified (4.1%). At baseline, AF prevalence was 16% in robust patients, 27% in moderately frail patients, and 37% in severely frail patients. The 1-year incidence of AF was 4.1% in robust patients, 5.5% in moderately frail patients, and 2.3% in severely frail patients. After adjustment, age was associated with new-onset AF across all frailty subgroups, but classical cardiovascular risk factors (as defined by ESC guidelines) were not predictive of new AF. Notably, the severely frail group exhibited a low incidence of new AF, in contrast to their high baseline AF prevalence (37%), suggesting a possible ceiling effect. The highest incidence of AF occurred in the moderately frail subgroup, raising the possibility that the development of AF may contribute to further frailty, potentially leading to a transition to severe frailty. Conclusions In elderly patients with multi-morbidity, age was the only predictive factor for new AF development within 1 year, while classical cardiovascular risk factors did not show predictive value. Frail patients, particularly those identified by clinical assessments, may represent the optimal target group for AF screening, while severely frail patients could potentially be excluded from such programs due to their high baseline prevalence of AF and low incidence of new cases. This study suggests a ceiling effect in the severely frail, where most susceptible patients have already developed AF, highlighting the need for tailored screening approaches.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".