GLP-1 Receptor Agonists for Weight Loss and Risk of Major Safety Outcomes: A Multicentre Cohort Study.

Stroke risk stratification in atrial fibrillation (AF) is central to guiding anticoagulation use to prevent ischaemic stroke (IS). The CHADS VASc score remains the most widely used clinical tool, but its predictive performance is modest, with reported discrimination typically ranging from 0.55 to 0.70 in external validation studies []. It includes clinical and demographic factors of cardiac failure, hypertension, age ≥75 years [doubled], previous IS/transient ischaemic attack (TIA) [doubled], vascular disease, and female sex. Efforts to improve risk prediction have largely focused on demographic refinement, including alternative modelling of age, incorporation of ethnicity, and removal of the female sex component [, ]. However, the incremental value of these approaches remains uncertain, particularly in contemporary multi-ethnic populations. Ethnicity is an important social determinant of health in New Zealand, with Māori and Pacific people developing AF and experiencing IS over one decade earlier than Europeans [, ]. Auckland’s population (1.9M) is ethnically diverse, comprising approximately 12% Māori, 14% Pacific peoples, 37% Asian (East and South), and 38% European or other ethnicities []. We evaluated whether demographic refinement or AF type improves stroke risk prediction beyond CHADS VASc. We conducted a retrospective nested case-control study of patients with AF diagnosed prior to the fifth Auckland Regional Stroke Study (ARCOS V, 1 September 2020–31 August 2021). In short, ARCOS is a prospective population-based registry capturing stroke and TIA events across Auckland using multiple overlapping ascertainment sources, with central diagnostic adjudication []. Cases were obtained from ARCOS V. Controls were randomly selected from the New Zealand National Minimum Dataset using ICD-10 AF (I48) codes, and did not experience IS/TIA during ARCOS V. Controls were stratified by ethnicity to ensure adequate representation of major ethnic groups; no weighting or conditional methods were applied, as analyses focused on comparing relative model discrimination within the sampled dataset rather than estimation of population incidence or calibration.