Characteristics of Left Ventricular Dysfunction in Repaired Tetralogy of Fallot: A Multi-Institutional Deep Learning Analysis of Regional Strain and Dyssynchrony.
Brendan T Crabb, Rahul S Chandrupatla, Evan M Masutani, Sophie Y Wong, Sachin Govil, Silvia Montserrat, Susana Prat-González, Julián Vega-Adauy, Melany Atkins, Daniel Lorenzatti, Chiara Zocchi, Elena Panaioli, Nathalie Boddaert, Laith Alshawabkeh, Lewis Hahn, Sanjeet Hegde, Andrew D McCulloch, Francesca Raimondi, Albert Hsiao
Author Information
Brendan T Crabb: University of California San Diego, La Jolla, CA, USA. Electronic address: b2crabb@health.ucsd.edu.
Rahul S Chandrupatla: University of California San Diego, La Jolla, CA, USA. Electronic address: rchandru@health.ucsd.edu.
Evan M Masutani: University of California San Diego, La Jolla, CA, USA. Electronic address: emasutan@health.ucsd.edu.
Sophie Y Wong: University of California San Diego, La Jolla, CA, USA. Electronic address: soyou@health.ucsd.edu.
Sachin Govil: University of California San Diego, La Jolla, CA, USA. Electronic address: sagovil@eng.ucsd.edu.
Julián Vega-Adauy: Chilean Institute of Cardiac Imaging, Santiago, Chile. Electronic address: julianvega@gmail.com.
Melany Atkins: Inova Fairfax Hospital, Fairfax, VA, USA. Electronic address: melanyatkins@gmail.com.
Daniel Lorenzatti: Hospital Clínic Barcelona, Barcelona, Spain. Electronic address: danilorenzatti@gmail.com.
Chiara Zocchi: San Donato Hospital, Arezzo, Italy. Electronic address: zocchichiara@gmail.com.
Elena Panaioli: Necker Enfants-Malades Hospital, Université de Paris, Paris, France. Electronic address: epanaioli@gmail.com.
Nathalie Boddaert: Necker Enfants-Malades Hospital, Université de Paris, Paris, France. Electronic address: Nathalie.boddaert@aphp.fr.
Laith Alshawabkeh: University of California San Diego, La Jolla, CA, USA. Electronic address: lalsh@health.ucsd.edu.
Lewis Hahn: University of California San Diego, La Jolla, CA, USA. Electronic address: lehahn@health.ucsd.edu.
Sanjeet Hegde: University of California San Diego, La Jolla, CA, USA; Rady Children's Hospital San Diego, CA, USA. Electronic address: shegde@rchsd.org.
Andrew D McCulloch: University of California San Diego, La Jolla, CA, USA. Electronic address: amcculloch@ucsd.edu.
Francesca Raimondi: Necker Enfants-Malades Hospital, Université de Paris, Paris, France. Electronic address: Francesca.raimondi@gmail.com.
Albert Hsiao: University of California San Diego, La Jolla, CA, USA. Electronic address: a3hsiao@health.ucsd.edu.
BACKGROUND: Patients with repaired tetralogy of Fallot (rTOF) are commonly followed with MRI and frequently develop right ventricular (RV) dysfunction, which can be severe enough to impact left ventricular (LV) function in some patients. In this study, we sought to characterize patterns of LV dysfunction in this patient population using Deep Learning Synthetic Strain (DLSS), a fully automated deep learning algorithm capable of measuring regional LV strain and dyssynchrony. METHODS: We retrospectively collected cine SSFP MRI images from a multi-institutional cohort of 198 patients with rTOF and 21 healthy controls. Using DLSS, we measured LV strain and strain rate across 16 AHA segments from short-axis cine SSFP images and compared these values to controls. We then performed a clustering analysis to identify unique patterns of LV contraction, using segmental peak strain and several measures of dyssynchrony. We further characterized these patterns by assessing their relationship to traditional MRI metrics of volume and function. Lastly, we assessed their impact on subsequent progression to pulmonary valve replacement (PVR) through a multivariate analysis. RESULTS: Overall, patients with rTOF had decreased septal radial strain, increased lateral wall radial strain, and increased dyssynchrony relative to healthy controls. Clustering of rTOF patients identified four unique patterns of LV contraction. Most notably, patients in cluster 1 (n=39) demonstrated an LV contraction pattern with paradoxical septal wall motion and severely reduced septal strain. These patients had significantly elevated RV end-diastolic volume relative to clusters 3 and 4 (153±34 vs. 127±34 and 126±31mL/m, ANOVA p<0.01). In the multivariate analysis, this contraction pattern was the only LV metric associated with future progression to pulmonary valve replacement (HR = 2.69, p<0.005). A smaller subset of patients (cluster 2, n=29) showed reduced septal strain and LV ejection fraction despite synchronous ventricular contraction. CONCLUSIONS: Patients with rTOF demonstrate four unique patterns of LV dysfunction. Most commonly, but not exclusively, LV dysfunction is characterized by septal wall motion abnormalities and severely reduced septal strain. Patients with this pattern of LV dysfunction had concomitant RV dysfunction and rapid progression to PVR.
