Machine learning–driven integration of 24-hour ambulatory blood pressure and its variability
Year: 2026
Type: article
Study design: Observational Study
Abstract: Twenty-four hour ambulatory blood pressure (BP) monitoring (24-hour ABPM) is considered the best out-of-office BP measurement to assess hypertension. Yet, the complexity of the obtained data, hinders their interpretation. Therefore, in this study, we investigated whether a clustering algorithm could facilitate the integrative interpretation of 24-hour ambulatory BP and its variability to construct distinctive clusters associated with cardiovascular (CV) outcomes. In 1344 community-dwelling individuals (mean age, 46.9 years; 50.6% women), we acquired 24-hour ABPM recordings, clinical data at baseline and we collected adverse outcomes (median follow-up time 19.4 years). We used dynamic time warping combined with the k-medoids clustering algorithm on the heart rate, systolic and diastolic BP recordings. Clinical characteristics and CV outcome were used to evaluate the validity of the derived clusters using the original cohort, while an external general population cohort (n = 1219) was used for further validation. We separated the dataset into 4 clusters. Cluster 1 showed the most favourable CV risk profile comprising younger individuals with the lowest 24-hour ABPM patterns. On the contrary, cluster 4 revealed the worst CV profile with older participants, higher office and 24-hour ABPM, higher medication intake and the highest BP variability. After adjusting for traditional risk factors, including office systolic BP cluster 4 had significantly higher risk of adverse CV events compared to cluster 1 (HR: 1.63; 95%CI:1.15-2.32; P = 0.006). Similar findings were made in the external validation cohort. Time series clustering of ABPM recordings may facilitate the integrative interpretation of 24-hour ambulatory BP and BP variability.
Source: PLOS Digital Health
Institution: KU Leuven
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G1180579369, G2360019775, Integrative personalized computer modeling for better cardiovascular health assessment, G4009809703, G4324373187, G4718095871, Integrated computer modelling of cardiorespiratory fitness for personalised risk profiling and heart failure prevention (iCAREFIT), Machine-learning modelling for personalized cardiorespiratory fitness profiling and prediction of response to exercise therapy (iCAREFIT), G7046444663, G7199402522, G7724036520, Computational Time Series Analysis of Cardiovascular Health and Cardiopulmonary fitness profiling
OpenAlex ID: W7168730010