Value of cardiac magnetic resonance imaging derived spectral myocardial strain pattern for non-invasive diagnosis of myocarditis

Funding Acknowledgements

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Society of Radiology European Institute for Biomedical Imaging Research

Background

Traditionally, cardiac function is quantified by measures of peak excursion, for example ejection fraction. However, myocardial strain estimation from cine- cardiac MRI allows quantification of cardiac motion over the whole heart cycle. We propose a spectral decomposition of the strain curves applying Discrete Fourier transformation (DFT).

Purpose

To evaluate a potential additive diagnostic value of spectral temporal strain curve quantification for non-invasive diagnosis of myocarditis using cardiac MRI.

Methods

In the single-center prospective study patients with suspected myocarditis underwent comprehensive cardiac MRI followed by biventricular endomyocardial biopsy (EMB) between 2012 and 2014. DFT was applied to myocardial strain curves extracted from cine-Images. As reference model, a L1- and L2-penalized logistic regression model using global native T1 time, T2 time and presence of late-gadolinium enhancement was trained to predict EMB results and compared to two models which additionally include three orders of DFT coefficients and ejection fraction, respectively. Predictive performance was evaluated in a tournament-leave-pair-out cross-validation approach with a bootstrap correction for testing of multiple hyperparameter configurations.

Results

Out of 100 patients (28 % female, median age 40 [IQR 32 to 56) years) with acute symptom-onset (<30 days) 65 had pathologically proven myocarditis in EMB. The DFT model showed best discrimination (Area under the receiver-operating-curve [AUC] 0.72 [95% CI 52 to 87]). Addition of ejection fraction (AUC 0.60 [95% CI: 0.43 to 0.74]) did not increase AUC compared to the reference (AUC 0.60 [95% CI: 0.43 to 0.74]). Posterior distribution of the bootstrap-corrected AUC difference between DFT and reference model was gaussian (mean 12%, standard deviation 12%) with a posterior probability of 86%, that DFT has a greater AUC.

Conclusions

Discrimination of myocarditis from similar clinical presentations remains challenging. The results support incremental discriminatory value of DFT-decomposed myocardial strain for non-invasive diagnosis of myocarditis. Future research should address the value of the spectral decomposition of cardiac motion trajectories in larger samples and different disease entities.