Abstract 49: Genomic Profiling and Risk of Intracerebral Hemorrhage in Patients with Atrial Fibrillation on Apixaban
Bibliographic record
Abstract
Introduction: Intracranial hemorrhage (ICH) is the most severe adverse effect of anticoagulation in atrial fibrillation (AF) patients. Hypertension, diabetes, hyperlipidemia, and chronic kidney disease are well-known cardiovascular risk factors for ICH. However, the relationship between the polygenic profiles (PP) of these risk factors and ICH risk in AF patients on anticoagulation remains unclear. We hypothesize that adverse PP increases the risk of ICH in AF patients on anticoagulation. Methods: We conducted a prospective genetic association study within All of Us . Participants over 50 with a history of AF treated with apixaban (the most widely used anticoagulant in this population) and no history of ischemic stroke or ICH were included. We calculated the polygenic profile (PP) by combining five standardized polygenic risk scores for systolic blood pressure, type 2 diabetes, low- and high-density lipoproteins, and glomerular filtration rate, along with APOE epsilon 4 and 2 genotypes. These were based on data from recent genome-wide association studies, with APOE genotypes determined by variants rs429358 and rs7412 . We categorized PP into three risk groups using a standard approach: favorable (<20%), neutral (20%-80%), and adverse (>80%). The outcome was incident ICH (new intraparenchymal, subdural, or subarachnoid hemorrhage) after apixaban initiation. Results: A total of 2,088 participants were included in the study (mean age 71 years, 953 [45%] female and 1,743[83%] of European ancestry). After a median follow-up of 2.9 years, 26 participants sustained an ICH (cumulative incidence:1.5%[95%CI:1.00–2.20], Figure 1). Multivariable Cox proportional hazards models showed that when compared to patients with a favorable PP, those with an adverse PP had a more than three-fold increase in the risk of ICH (HR:3.38,95%CI:1.09–10.50, p -trend=0.005). Polygenic information improved the discrimination of risk prediction scores for ICH (c-statistics of 0.68 and 0.75 for models without and with genomic information, respectively ( p =0.01, Figure 2). Conclusions: Our results show that among AF patients on apixaban, an adverse PP for key cardiovascular risk factors significantly increases the risk of ICH compared to those with a favorable PP. Additionally, incorporating PP data enhances the predictive power of clinical prediction scores for ICH. These findings support further research into whether polygenic profiling can improve clinical decision-making in AF patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".