Prognostic implications of machine learning-derived echocardiographic phenotypes in community hypertensive patients.

Background: Echocardiogram is commonly used to evaluate cardiac remodeling in hypertension (HTN). However, study on echocardiographic phenotypes and their prognostic implications in HTN is limited.
Objective: We aimed to evaluate the prognostic implications echocardiographic phenotypes in community hypertensive patients.
Method: A total of 1881 community hypertensive patients without overt cardiovascular disease and severe renal disease (mean age 62.8 years, women 57.9%) were included. Using Two-Step cluster analysis with four conventional echocardiographic variables, two clusters with distinct echocardiographic phenotypes were identified.
Result: The Cluster 1 (namely "mild-remodeling" HTN; n  = 1492) had low prevalence of enlarged left atrium (LA; 0.9%) and left ventricular hypertrophy (LVH; 16.2%) and better LV diastolic function. They were younger and more likely to be men and had lower comorbid burden. The Cluster 2 (namely "severe-remodeling" HTN; n  = 389) had higher prevalence of enlarged LA (26.0%) and LVH (83.0%) and worse LV diastolic function. They were older and more likely to be women and had higher comorbid burden. After a median follow-up of 4.2 years, compared to the Cluster 1, the Cluster 2 had higher incidence of cardiovascular (4.1% vs 1.7%; P  = .006) and all-cause (9.8% vs 4.8%; P  < .001) death, with adjusted hazard ratio of 2.80 (95% CI 1.39-5.62; P  = .004) and 2.04 (95% CI 1.32-3.14; P  < .001) respectively.
Conclusion: These findings indicate that the conventional echocardiographic variables-based algorithm could help identify asymptomatic community hypertensive patients at risk for cardiovascular and all-cause death. Further studies are needed to develop and validate phenotype-specific prevention and intervention strategies in HTN.