Biomarkers for the prediction of cognitive decline in atrial fibrillation patients
Bibliographic record
Abstract
Abstract Background Patients with atrial fibrillation (AF) are at increased risk of cognitive decline. Biomarkers might help to identify the highest risk patients. Purpose To investigate associations of a broad panel of biomarkers with cognitive decline in a large population of AF patients. Methods We enrolled 1440 AF patients with available baseline biomarkers and cognitive testing by the Montreal Cognitive assessment (MoCA) score at inclusion and at ≥2 yearly follow-ups over three years. Cognition over time was assessed by modelling of a slope over three years. Cognitive decline was defined as a decreasing slope based on 1 standard deviation of the baseline MoCa score. We investigated the associations of biomarkers and age with cognitive decline in univariate logistic regression models by AUCs. We used Lasso modelling to build combined prediction models with biomarkers, clinical variables and both. Results Mean age was 72 years, 75% were male, 47% had paroxysmal AF and 90% were anticoagulated. During a follow-up of 3 years, cognitive decline occurred in 93 patients (6.5%). Patients with cognitive decline were older (77 vs 72 years, p=0.001), more often had paroxysmal AF (50 vs 47%, p=0.007) or a history of stroke (24 vs 11%, p=0.001), but there were no differences in anticoagulation (94 vs 90%, p=0.289). The three biomarkers with the highest univariate AUC for cognitive decline were Growth Differentiation Factor (GDF)-15 (0.67 [0.62-0.72]), Cystatin C (0.67 [0.61-0.72]) and high-sensitivity Troponin T (0.65 [0.60-0.70]), while age had the highest overall AUC (0.70 [0.64-0.75]) (Figure). The combined prediction model with the highest AUC of 0.69 (0.67-0.72) included GDF-15, age and baseline cognition. Conclusion Over 3 years, 6.5% of AF patients had cognitive decline despite a high rate of anticoagulation. Besides age, GDF-15, Cystatin C and high-sensitivity Troponin T had the highest predictive value for cognitive decline.Univariate AUCs for biomarker/ageFigure legend
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".