Racial and ethnic disparities in aortic stenosis within a universal healthcare system characterized by natural language processing for targeted intervention

TY - JOUR

T1 - Racial and ethnic disparities in aortic stenosis within a universal healthcare system characterized by natural language processing for targeted intervention

AU - Biswas, Dhruva

AU - Wu, Jack

AU - Brown, Sam

AU - Bharucha, Apurva

AU - Fairhurst, Natalie

AU - Kaye, George

AU - Jones, Kate

AU - Copeland, Freya Parker

AU - O'Donnell, Bethan

AU - Kyle, Daniel

AU - Searle, Tom

AU - Pareek, Nilesh

AU - Dworakowski, Rafal

AU - Papachristidis, Alexandros

AU - Melikian, Narbeh

AU - Wendler, Olaf

AU - Deshpande, Ranjit

AU - Baghai, Max

AU - Galloway, James

AU - Teo, James T.

AU - Dobson, Richard

AU - Byrne, Jonathan

AU - MacCarthy, Philip

AU - Shah, Ajay M.

AU - Eskandari, Mehdi

AU - O'Gallagher, Kevin

N1 - © The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology.

PY - 2025/5/1

Y1 - 2025/5/1

N2 - AIMS: 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 such as health insurance confound analyses. This study evaluates disparities in AS management across racial and ethnic groups, accounting for socioeconomic deprivation, using an artificial intelligence (AI) framework.METHODS AND RESULTS: We conducted a retrospective cohort study using a natural language processing 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. Among 6967 patients with AS, 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 among White patients, with a longer time to SAVR. In multivariate analysis of severe AS, controlling for socioeconomic status, Black patients experienced higher mortality (hazard ratio = 1.42, 95% confidence interval = 1.05-1.92, P = 0.02). CONCLUSION: An AI framework characterizes racial and ethnic disparities in AS management, which persist in a universal healthcare system, highlighting targets for future healthcare interventions.

AB - AIMS: 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 such as health insurance confound analyses. This study evaluates disparities in AS management across racial and ethnic groups, accounting for socioeconomic deprivation, using an artificial intelligence (AI) framework.METHODS AND RESULTS: We conducted a retrospective cohort study using a natural language processing 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. Among 6967 patients with AS, 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 among White patients, with a longer time to SAVR. In multivariate analysis of severe AS, controlling for socioeconomic status, Black patients experienced higher mortality (hazard ratio = 1.42, 95% confidence interval = 1.05-1.92, P = 0.02). CONCLUSION: An AI framework characterizes racial and ethnic disparities in AS management, which persist in a universal healthcare system, highlighting targets for future healthcare interventions.

UR - https://www.scopus.com/pages/publications/105005504118

U2 - 10.1093/ehjdh/ztaf018

DO - 10.1093/ehjdh/ztaf018

M3 - Article

C2 - 40395418

SN - 2634-3916

VL - 6

SP - 392

EP - 403

JO - European Heart Journal Digital Health

JF - European Heart Journal Digital Health

IS - 3

ER -