Demographic Refinement and Atrial Fibrillation Type in Stroke Risk Stratification Beyond CHA₂DS₂ VASc

Abstract

INTRODUCTION: The CHA₂DS₂ VASc score is widely used for stroke risk stratification in atrial fibrillation (AF). Efforts to improve prediction using demographic refinements have uncertain incremental value, particularly in multi-ethnic populations. We evaluated whether demographic modification or AF type provides additional discriminatory value. METHODS: We conducted a retrospective nested case-control study of patients with AF diagnosed prior to the fifth Auckland Regional Community Stroke Study (September 2020-August 2021). Cases with ischaemic stroke or transient ischaemic attack were identified from this population-based registry with central adjudication. Controls were randomly selected from the National Minimum Dataset, stratified by ethnicity. Predictors included CHA₂DS₂ VASc components, demographic refinements (alternative age transformations, ethnicity, exclusion of sex), and AF type. The primary baseline model was a multivariable logistic regression model using standard CHA₂DS₂ VASc predictors as covariates. The traditional point-based CHA₂DS₂ VASc score was evaluated separately for comparison. Discrimination was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: The study included 1,908 patients (300 cases). A logistic regression model using standard CHA₂DS₂ VASc predictors demonstrated moderate discrimination (AUC 0.68 [95% CI 0.65-0.71]). The traditional point-based CHA₂DS₂ VASc score showed lower discrimination (AUC 0.63 [95% CI 0.60-0.66]; ΔAUC +0.05; p=0.0003). Demographic refinements did not improve discrimination (ΔAUC ≤0.01). Addition of AF type modestly increased discrimination (AUC 0.71; ΔAUC +0.03; p=0.015), with greater improvement in non-anticoagulated patients (ΔAUC +0.05). CONCLUSION: Demographic refinements, including ethnicity, provide limited incremental value beyond CHA₂DS₂ VASc, whereas AF type offers a modest discriminatory signal. This finding is hypothesis-generating and requires external validation before clinical implementation is considered.