Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.

ABSTRACT:

Aims

Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning.

Methods and results

Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validatio

SUBMITTER: Alabed S 

PROVIDER: S-EPMC9708011 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature