Fenotipi clinici nell'ipertensione: un approccio guidato dai dati per la stratificazione del rischio.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can uncover risk profiles and refine preventative strategies. This study applied a data-driven approach to identify clinical phenotypes of hypertension, examine their associations with cardiovascular imaging characteristics and adverse outcomes, and assess the mediating role of cardiac imaging features in these associations. METHODS: Fourteen thousand eight hundred forty UK Biobank participants with diagnosed hypertension and cardiovascular magnetic resonance imaging were analyzed. K-means clustering was applied to 77 cli