A machine learning-based score for precise echocardiographic assessment of cardiac remodelling in hypertensive young adults.

ABSTRACT:

Aims

Accurate staging of hypertension-related cardiac changes, before the development of significant left ventricular hypertrophy, could help guide early prevention advice. We evaluated whether a novel semi-supervised machine learning approach could generate a clinically meaningful summary score of cardiac remodelling in hypertension.

Methods and results

A contrastive trajectories inference approach was applied to data collected from three UK studies of young adults. Low-dimensional variance was identified in 66 echocardiography variables from participants with hypertension (systolic ≥160 mmHg) relative to a normotensive group (systolic < 120 mmHg) using a contrasted principal component analysis. A minimum spanning tree was constructed to derive a normalized score for each i

SUBMITTER: Alsharqi M 

PROVIDER: S-EPMC10562347 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature