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Record W4410621129 · doi:10.1093/europace/euaf085.296

Polygenic risk scores for risk prediction of atrial fibrillation in cardiac surgery patients: Insights from the prospective, multinational VISION cardiac surgery cohort

2025· article· en· W4410621129 on OpenAlexafffundabout
William F. McIntyre, Michael Chong, P.J. Devereaux, Katheryn Brady, Saima Bashir, Tsieh Sun, André Lamy, Jason D. Roberts, Guillaume Paré, Matthew T.V. Chan, C. Y. Wang, Ajit Singh, Emilie P. Belley‐Côté, R P Whitlock, Jessica Spence

Bibliographic record

VenueEP Europace · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPopulation Health Research Institute
FundersHeart and Stroke Foundation of Canada
KeywordsAtrial fibrillationMedicineCardiac surgeryProspective cohort studyInternal medicineCardiologyCohort

Abstract

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Abstract Background New-onset postoperative atrial fibrillation (POAF) complicates 1 in 3 cardiac surgeries and is associated with morbidity, mortality and clinical AF in long-term follow-up. Clinical risk scores have modest performance for predicting POAF. Polygenic risk scores are derived from the summation of up to millions of genetic variants and have shown good predictive ability for incident AF in the community. The ability of polygenic risk scores to predict POAF and subsequent recurrence of clinical AF in cardiac surgery patients is unclear. Methods We performed a prospective cohort study of patients from 4 regions (Canada, Hong Kong, Malaysia, United Kingdom) without a pre-operative history of atrial fibrillation (AF) who underwent cardiac surgery and were followed for 1 year. From pre-operative blood samples, we extracted DNA and calculated each participant’s polygenic risk score for AF using a penalized regression method (lassosum) to combine the effects of 5,000,621 genetic variants, weighted by their association with AF status from a previous genome-wide association study by Miyazawa (Nature Genetics, 2023). We estimated the association of this polygenic risk score for AF with the incidence of new-onset POAF using analyses adjusted for genetic ancestry. We assessed the ability of the polygenic risk score to predict POAF when added to common clinical risk scores. As a secondary objective, among patients who developed POAF, we estimated the association of the polygenic risk score with AF recurrence in follow-up beyond 30 post-operative days. Results Among 3031 patients (63.5% isolated coronary artery bypass grafting), 1282 patients (42.3%) developed new-onset POAF. The polygenic risk score for AF was strongly associated with the risk for POAF (odds ratio 1.3 per standard deviation increase in polygenic risk score [95% CI 1.2-1.4]). The 10% of participants with highest polygenic risk had a risk of POAF of 50.5% as compared to 41.4% for the bottom 90% (odds ratio 1.4 [95% CI 1.1-1.8]). When the polygenic risk score was added to the clinical risk scores, it improved the model fit for all scores, significantly improved the C-statistic for the CHA2DS2-VASc, POAF and HATCH Scores and improved measures of risk classification for all scores (Table). Follow-up data on AF status beyond 30 days were available for 902 patients; 71 patients (7.9%) had AF recurrence detected beyond 30 days post-operatively. The polygenic risk score was not significantly associated with a higher risk for AF recurrence (odds ratio, 1.1 per standard deviation increase in polygenic risk score [95% CI, 0.9-1.5]). Conclusions A higher polygenic risk score for AF is associated with the development of new-onset POAF following cardiac surgery and improves risk classification compared with clinical risk scores alone. However, this study failed to demonstrate an association of the polygenic risk score with AF recurrence in patients who develop POAF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.334
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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