Association between lung function and the risk of atrial fibrillation in a nationwide population cohort study

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

Objective

Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients.

Methods

To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA2DS2-VASc for prediction of ischemic stroke among AF patients, was used for comparison to the best performing ML model.

Findings

The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA2DS2-VASc was statistically significant based on DeLong's test (p-value = 2.20E-06). In addition, the SHAP analysis showed that several peripheral blood biomarkers (e.g. creatinine, glycated haemoglobin, monocytes) were associated with ischemic stroke, which are not considered by CHA2DS2-VASc.

Implications

The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients.

Dissecting the associations of KCNH2 genetic polymorphisms with various types of cardiac arrhythmias

BACKGROUND

Cardiac arrhythmia, a common cardiovascular disease, is closely related to genetic polymorphisms. However, the associations between polymorphisms in KCNH2 and various arrhythmias remain inadequately explored.

METHODS

Guided by the assumption that KCNH2 genetic polymorphisms significantly contribute to the development of arrhythmias, we thoroughly explored the associations between 85 KCNH2 genetic variations and 16 cardiac arrhythmias in a sample obtained from the UK Biobank (UKBB, N = 307,473). The illnesses documented in the electronic medical records of the sample were mapped to a phecode system for a more accurate representation of distinct phenotypes. Survival analysis was used to test the effect of KCNH2 variants on arrhythmia incidence, and a phenotype-wide association study (PheWAS) was performed to investigate the effect of KCNH2 polymorphisms on 102 traits, including physical measurements, biomarkers, and hematological indicators.

RESULTS

Novel associations of variants rs2269001 and rs7789585 in KCNH2 with paroxysmal tachycardia (PT) and atrial fibrillation/flutter (AF/AFL), respectively, were identified. Moreover, with an increase in the number of minor alleles of these two variants, the incidence rates of PT and AF/AFL decreased. In addition, the PheWAS results suggested that these two single nucleotide polymorphisms were associated with multiple parameters in physical measurements and neutrophil percentage.

CONCLUSION

The multiple novel associations observed in this study illustrate the importance of KCNH2 genetic variations in the pathogenesis of arrhythmia.