Clustered Phenotypes of Hypertensive Heart Disease With Strain Measurements Reveals Distinct Characteristics, Clinical Course, and Prognosis
Hwang, I.-C.; Kim, H. M.; Park, J.; Choi, H.-M.; Yoon, Y. E.; Cho, G.-Y.
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BackgroundHypertensive heart disease (HHD) encompasses diverse clinical profiles, comorbidities, and cardiac remodeling, but current classifications insufficiently capture this heterogeneity or guide risk stratification. MethodsWe studied 1,607 patients with hypertension from the STRATS-HHD registry who underwent echocardiography at baseline and after 6-18 months of antihypertensive therapy. Twenty clinical, laboratory, and echocardiographic variables--including left atrial reservoir strain (LASr) and left ventricular global longitudinal strain (LV-GLS)--underwent principal component analysis and K-means clustering (K=4). Clusters were derived in a derivation cohort (n=1,204) and validated in an independent cohort (n=403). Longitudinal remodeling was assessed with baseline-adjusted models, and outcomes (CV death, heart failure hospitalization [HHF], coronary events, stroke, composite CV death/HHF, and major adverse cardiovascular events [MACE]) with multivariable Cox regression. ResultsFour clusters emerged: (1) AF-predominant, with advanced remodeling and highest event risk; (2) elderly, with metabolic-renal comorbidities but preserved function; (3) middle-aged, with prevalent coronary disease and relatively preserved function; and (4) younger, with severe hypertension, marked strain impairment, and greatest remodeling regression with therapy. Prognosis varied across clusters: cluster 1 had the highest risk of CV death, HHF, stroke, and MACE; cluster 3 had elevated coronary risk; and cluster 4 showed the most favorable outcomes. Medication-remodeling associations differed, with renin-angiotensin system blockade linked to LV mass regression in cluster 4. ConclusionsMachine learning-based clustering incorporating LA and LV strain identified four distinct HHD phenotypes with divergent remodeling, therapeutic responses, and outcomes. Data-driven phenotyping may improve risk stratification and guide tailored management in hypertension.
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