Natural Language Processing to Identify Racial and Ethnic Disparities in Aortic Stenosis

IMPORTANCE This study uses artificial intelligence (AI) technologies to augment quality measurement and improvement in the setting of aortic stenosis (AS). We characterise racial and ethnic disparities in the diagnosis, management, and outcome of AS within a universal healthcare system. OBJECTIVE To use natural language processing (NLP) AI methods applied to the electronic health records (EHR) to identify racial and ethnic disparities in AS while correcting for the effects of socioeconomic deprivation. DESIGN Retrospective cohort study. SETTING King’s College Hospital NHS Foundation Trust, a multi-site tertiary care hospital in London, UK PARTICIPANTS Adult patients with a diagnosis of AS between 2010-2020. MAIN OUTCOMES AND MEASURES Key outcomes were all-cause mortality, frequency of AS intervention (TAVI or surgical aortic valve replacement [AVR]) and the time from diagnosis of severe AS to intervention. All analyses were adjusted for age, sex and socioeconomic deprivation. RESULTS 5859 patients with AS were identified, with self-reported race and ethnicity labels as 4.5% Asian, 7.5% Black, and 88.0% White. For those with severe AS, TAVI was performed in 19.6% of Asian patients, 17.6% of Black patients and 24.9% of White patients; AVR was performed in 39.2% of Asian patients, 27.9% of Black patients and 32.8% of White patients. The mean time from severe AS diagnosis to TAVI was 0.69 years for Asian patients, 1.03 years for Black patients and 0.62 years for White patients (P=n.s.). The mean time to AVR was longer for Black patients (1.35 years) compared to Asian (0.49 years) and White patients (0.41 years, P<0.001). Survival in the overall cohort did not associate with ethnicity. However, in patients with severe AS, Black ethnicity was independently associated with increased mortality (hazard ratio=1.42, 95% CI=1.05-1.92, P=0.02). CONCLUSIONS AND RELEVANCE In patients with severe AS, Black patients experience lower rates of TAVI, longer time from diagnosis to AVR and higher rates of mortality, despite correction for socioeconomic deprivation. These data exhibit how AI technologies may be leveraged to shed light on health inequities, here showing that racial and ethnic disparities in AS persist in a universal healthcare system, and should stimulate strategies to address inequity. Key points Question Do racial and ethnic disparities in the diagnosis, management, and outcome of aortic stenosis (AS) exist within a universal healthcare system? Finding In this retrospective cohort study using natural language processing enabled analysis of electronic healthcare record data of 5859 patients with aortic stenosis, we identified that in severe AS, Black patients experience lower rates of transcatheter aortic valve implantation (TAVI), longer times from diagnosis to surgical aortic valve replacement (AVR) and higher rates of mortality. Meaning Natural language processing may be used to identify health inequities. Here, we find racial and ethnic disparities in AS exist even in a universal healthcare setting.