All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes

• Published in: Frontiers in physiology Vol. 13; p. 908552
• Main Authors: Orkild, Benjamin A., Zenger, Brian, Iyer, Krithika, Rupp, Lindsay C., Ibrahim, Majd M, Khashani, Atefeh G., Perez, Maura D., Foote, Markus D., Bergquist, Jake A., Morris, Alan K., Kim, Jiwon J., Steinberg, Benjamin A., Selzman, Craig, Ratcliffe, Mark B., MacLeod, Rob S., Elhabian, Shireen, Morgan, Ashley E.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 02.06.2022
• Subjects: cardiac MRI; particle-based shape modeling; Physiology; principal component analysis; pulmonary hypertension; statistical shape modeling; tricuspid regurgitation; tricuspid regurgitation; cardiac MRI; pulmonary hypertension; particle-based shape modeling; statistical shape modeling; congestive heart failure; valvular heart disease; principal component analysis
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2022.908552

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls ( p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the “well compensated” RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.