Genetic Risk and Atrial Fibrillation in Patients with Heart Failure
Bibliographic record
Abstract
Abstract Aims To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all-cause mortality in patients with heart failure. Methods and results An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0–2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome-wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1-unit increase genetic risk score was 2.12 (95% confidence interval 1.84–2.45, P = 2.15 × 10−24) in the total cohort, 2.08 (1.72–2.50, P = 1.30 × 10−14) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37–2.99, P = 4.37 × 10−4) in heart failure with preserved ejection fraction (HFpEF). AF-associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C-index by 2.2% to 0.721. Conclusions The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".