11 Machine learning insights into blood pressure control in migrant and non-migrants with hypertension in the Netherlands

OP 37: Health Status 3, B210 (FCSH), September 5, 2025, 11:30 - 12:30 Aims Migrant populations in Europe often face challenges in achieving optimal blood pressure control. While traditional statistical methods have provided valuable insights, machine learning offers the potential to uncover complex interactions among factors influencing blood pressure control. We therefore applied machine learning techniques to the multi-ethnic HELIUS cohort in the Netherlands to identify subgroups of hypertensive participants achieving optimal blood pressure control based on migration background, phenotypic traits, and individual antihypertensive medications. Methods Data from 1,580 hypertensive participants were analyzed over seven years (2015–2021). Participants were classified as Dutch (non-migrants) or migrants, including those of South Asian Surinamese, African Surinamese, Turkish, Moroccan, and Ghanaian origins. The dataset included 55 individual antihypertensive medications, along with demographics, lifestyle factors, comorbidities, and treatment adherence. Blood pressure control was defined according to WHO criteria. Subgroup discovery techniques—Patient Rule Induction Method (PRIM), Subgroup Set Discovery ++ (SSD ++), and APRIORI-SD—were used to identify subgroups achieving optimal blood pressure control. Results Twenty-one subgroups were identified (11 with PRIM, 5 with APRIORI-SD, and 5 with SSD ++). Most subgroups were defined solely by medication variables, with optimal control rates around 70%. However, a notable subgroup was identified using SSD ++: 96% of participants with higher baseline blood pressure who used lercanidipine achieved optimal control, irrespective of migration background, lifestyle, demographics, or other phenotypic traits. Conclusion Our findings demonstrate that medication type plays a more significant role in achieving blood pressure control than migration background, lifestyle, or comorbidities. Identifying ways to predict individual antihypertensive medication responses before initiating treatment could significantly improve hypertension management, regardless of migration background.