ArXiv Preprint
Heart failure is a serious and life-threatening condition that can lead to
elevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure
(PAWP) is an important surrogate marker indicating high pressure in the left
ventricle. PAWP is determined by Right Heart Catheterization (RHC) but it is an
invasive procedure. A non-invasive method is useful in quickly identifying
high-risk patients from a large population. In this work, we develop a tensor
learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic
Resonance Imaging (MRI). This pipeline extracts spatial and temporal features
from high-dimensional scans. For quality control, we incorporate an epistemic
uncertainty-based binning strategy to identify poor-quality training samples.
To improve the performance, we learn complementary information by integrating
features from multimodal data: cardiac MRI with short-axis and four-chamber
views, and Electronic Health Records. The experimental analysis on a large
cohort of $1346$ subjects who underwent the RHC procedure for PAWP estimation
indicates that the proposed pipeline has a diagnostic value and can produce
promising performance with significant improvement over the baseline in
clinical practice (i.e., $\Delta$AUC $=0.10$, $\Delta$Accuracy $=0.06$, and
$\Delta$MCC $=0.39$). The decision curve analysis further confirms the clinical
utility of our method.
Prasun C. Tripathi, Mohammod N. I. Suvon, Lawrence Schobs, Shuo Zhou, Samer Alabed, Andrew J. Swift, Haiping Lu
2023-03-14
