CMR-based right ventricular strain analysis in cardiac amyloidosis and its potential as a supportive diagnostic feature

Abstract

$\it Background:$ Right ventricular (RV) strain has provided valuable prognostic information for patients with $\underline {cardiac amyloidosis}$ (CA). However, the extent to which RV strain and strain rate can differentiate CA is not yet clinically established. CA underdiagnosis delays treatment strategies and exacerbates patient prognosis. $\it Aims:$ Evaluation of $\underline {cardiac magnetic resonance}$ (CMR) quantified RV global and regional strain of CA and $\underline {HCM}$ patients along with CA subtypes. $\it Methods:$ CMR feature tracking attained longitudinal, radial and circumferential global and regional strain in 47 control subjects (CTRL), 43 CA-, 20 hypertrophic cardiomyopathy- (HCM) patients. CA patients were subdivided in 21 transthyretin-related amyloidosis (ATTR) and 20 acquired immunoglobulin light chain (AL) patients. Strain data and baseline clinical parameters were statistically analysed with respect to $\underline {diagnostic performance}$ and discriminatory power between the different clinical entities. $\it Results:$ Effective differentiation of CA from HCM patients was achieved utilizing global longitudinal (GLS: 16.5 $\pm$ 3.9% vs. −21.3 $\pm$ 6.7%, p = 0.032), radial (GRS: 11.7 $\pm$ 5.3% vs. 16.5 $\pm$ 7.1%, p < 0.001) and circumferential (GCS: -7.6 $\pm$ 4.0% vs. −9.4 $\pm$ 4.4%, p = 0.015) right ventricular strain. Highest strain-based hypertrophic phenotype differentiation was attained using GRS (AUC = 0.86). Binomial regression found right $\underline {ventricular ejection fraction}$ (RV-EF) (p = 0.017) to be a significant predictor of CA-HCM differentiation. CA subtypes had comparable cardiac strains. $\it Conclusion:$ CMR-derived RV global strains and various regional longitudinal strains provide discriminative radiological features for CA-HCM differentiation. However, in terms of feasibility, cine-derived RV-EF quantification may suffice for efficient differential diagnostic support