Thromboembolic events and vascular dementia in patients with atrial fibrillation and low apparent stroke risk.

The prevention of thromboembolism in atrial fibrillation (AF) is typically restricted to patients with specific risk factors and ignores outcomes such as vascular dementia. This population-based cohort study used electronic healthcare records from 5,199,994 primary care patients (UK; 2005-2020). A total of 290,525 (5.6%) had a diagnosis of AF and were aged 40-75 years, of which 36,340 had no history of stroke, a low perceived risk of stroke based on clinical risk factors and no oral anticoagulant prescription. Matching was performed for age, sex and region to 117,298 controls without AF. During 5 years median follow-up (831,005 person-years), incident stroke occurred in 3.8% with AF versus 1.5% control (adjusted hazard ratio (HR) 2.06, 95% confidence interval (CI) 1.91-2.21; P < 0.001), arterial thromboembolism 0.3% versus 0.1% (HR 2.39, 95% CI 1.83-3.11; P < 0.001), and all-cause mortality 8.9% versus 5.0% (HR 1.44, 95% CI 1.38-1.50; P < 0.001). AF was associated with all-cause dementia (HR 1.17, 95% CI 1.04-1.32; P = 0.010), driven by vascular dementia (HR 1.68, 95% CI 1.33-2.12; P < 0.001) rather than Alzheimer's disease (HR 0.85, 95% CI 0.70-1.03; P = 0.09). Death and thromboembolic outcomes, including vascular dementia, are substantially increased in patients with AF despite a lack of conventional stroke risk factors.

Identifying and handling data bias within primary healthcare data using synthetic data generators.

Advanced synthetic data generators can simulate data samples that closely resemble sensitive personal datasets while significantly reducing the risk of individual identification. The use of these advanced generators holds enormous potential in the medical field, as it allows for the simulation and sharing of sensitive patient data. This enables the development and rigorous validation of novel AI technologies for accurate diagnosis and efficient disease management. Despite the availability of massive ground truth datasets (such as UK-NHS databases that contain millions of patient records), the risk of biases being carried over to data generators still exists. These biases may arise from the under-representation of specific patient cohorts due to cultural sensitivities within certain communities or standardised data collection procedures. Machine learning models can exhibit bias in various forms, including the under-representation of certain groups in the data. This can lead to missing data and inaccurate correlations and distributions, which may also be reflected in synthetic data. Our paper aims to improve synthetic data generators by introducing probabilistic approaches to first detect difficult-to-predict data samples in ground truth data and then boost them when applying the generator. In addition, we explore strategies to generate synthetic data that can reduce bias and, at the same time, improve the performance of predictive models.