Racial and Ethnic Disparities in Aortic Stenosis within a Universal Healthcare System Characterised by Natural Language Processing for Targeted Intervention
Background
Aortic stenosis (AS) is a condition marked by high morbidity and mortality in severe, symptomatic cases without intervention via transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR). Racial and ethnic disparities in access to these treatments have been documented, particularly in North America, where socioeconomic factors like health insurance confound analyses. This study evaluates disparities in AS management across racial and ethnic groups, accounting for socioeconomic deprivation, using an artificial intelligence framework.
Methods
We conducted a retrospective cohort study using a natural language processing (NLP) pipeline to analyse both structured and unstructured data from > 1 million patients at a London hospital. Key variables included age, sex, self-reported race and ethnicity, AS severity, and socioeconomic status. The primary outcomes were rates of valvular intervention and all-cause mortality.
Results
Among 6,967 AS patients, Black patients were younger, more symptomatic and more comorbid than White patients. Black patients with objective evidence of AS on echocardiography were less likely to receive a clinical diagnosis than White patients. In severe AS, TAVI and SAVR procedures were performed at lower rates among Black patients than White patients, with a longer time to SAVR. In multivariate analysis of severe AS, controlling for socioeconomic status, Black patients experienced higher mortality (HR=1.42, 95% CI=1.05-1.92, P=0.02).
Conclusions
An artificial intelligence framework characterises racial and ethnic disparities in AS management, which persist in a universal healthcare system, highlighting targets for future healthcare interventions.