Le CÅ“ur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot

Mira, Anna; Lamata, Pablo; Pushparajah, Kuberan; Abraham, Georgina; Mauger, Charlène Alice; McCulloch, Andrew D.; Omens, Jeffrey H.; Bissell, Malenka M.; Blair, Zach; Huffaker, Tyler; Tandon, Animesh; Engelhardt, Sandy; Koehler, Sven; Pickardt, Thomas; Beerbaum, Philipp; Sarikouch, Samir; Latus, Heiner; Greil, Gerald; Young, Alistair A.; Hussain, Tarique

doi:10.1186/s12968-022-00877-x

Cited by 6 publications

(3 citation statements)

References 34 publications

(50 reference statements)

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“…Recently, attention has been drawn to RV geometric shape, which has been associated with exercise capacity, decrease in RV function and adverse events ( 23 , 24 ). Wall stress is strongly depended on geometry.…”

Section: Discussionmentioning

confidence: 99%

Serial RV wall stress measurements: association with right ventricular function in repaired Tetralogy of Fallot patients

Minderhoud,

Hirsch,

Marin

et al. 2023

Front. Cardiovasc. Med.

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BackgroundOptimal timing of pulmonary valve replacement (PVR) in Tetralogy of Fallot (TOF) patients remains challenging. Ventricular wall stress is considered to be an early marker of right ventricular (RV) dysfunction.ObjectivesTo investigate the association of RV wall stresses and their change over time with functional parameters in TOF patients.MethodsTen TOF patients after surgical repair with moderate/severe pulmonary regurgitation were included. At two timepoints (median follow-up time 7.2 years), patient-specific computational biventricular models for wall stress assessment were created using CMR short-axis cine images and echocardiography-based RV pressures. RV ejection fraction (RVEF), NT-proBNP and cardiopulmonary exercise tests were used as outcome measures reflecting RV function. Associations between regional RV diastolic wall stress and RV function were investigated using linear mixed models.ResultsIncreased wall stress correlated with lower RV mass (rrm = −0.70, p = 0.017) and lower RV mass-to-volume (rrm = −0.80, p = 0.003) using repeated measures. Wall stress decreased significantly over time, especially in patients with a stable RVEF (p < 0.001). Higher wall stress was independently associated with lower RVEF, adjusted for left ventricular ejection fraction, RV end-diastolic volume and time since initial surgery (decrease of 1.27% RVEF per kPa increase in wall stress, p = 0.029) using repeated measurements. No association was found between wall stress, NT-proBNP, and exercise capacity.ConclusionsUsing a computational method to calculate wall stress locally in geometrically complex ventricles, we demonstrated that lower wall stress might be important to maintain ventricular function. RV wall stress assessment can be used in serial follow-up, and is potentially an early marker of impending RV dysfunction.

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Section: Discussionmentioning

confidence: 99%

Serial RV wall stress measurements: association with right ventricular function in repaired Tetralogy of Fallot patients

Minderhoud,

Hirsch,

Marin

et al. 2023

Front. Cardiovasc. Med.

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“…Advances in computational medicine have enabled more quantitative approaches to characterizing ventricular shape and remodeling in individuals with heart disease. One such approach is the use of cardiac shape modeling to condense complex, multi-dimensional data from standard of care cardiovascular magnetic resonance (CMR) images into statistical atlases of cardiac structure and function [ 1 – 13 ]. These atlases are composed of interpretable shape and wall motion features that can be important quantitative biomarkers of patient status and outcome and, in turn, aid in prognosis and treatment of disease.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot

Govil

Crabb

Deng

et al. 2023

Journal of Cardiovascular Magnetic Resonance

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Background Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. Methods Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. Results The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. Conclusions Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.

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“…Moreover, cardiovascular computational modeling has now reached maturity, providing access to new biomarkers, 8 , 9 while statistical models—atlases of the heart—have demonstrated the importance of shape in the diagnosis of cardiovascular disease. 10 , 11 Specifically, comprehensive automated atrial remodeling assessment has previously demonstrated a predictive value for reoccurrence of atrial fibrillation (AF) following pulmonary vein (PV) ablation. 12 These postprocessing analyses can be used on widely available routinely acquired balanced steady-state free-precession cine sequences at rest.…”

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confidence: 99%

Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction

Backhaus,

Nasopoulou,

Lange

et al. 2024

Circ: Cardiovascular Imaging

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BACKGROUND: It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. METHODS: Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e′, >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. RESULTS: Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01–1.04]; P =0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00–1.04]; P =0.043). CONCLUSIONS: LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03260621.

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Le CÅ“ur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot

Cited by 6 publications

References 34 publications

Serial RV wall stress measurements: association with right ventricular function in repaired Tetralogy of Fallot patients

Serial RV wall stress measurements: association with right ventricular function in repaired Tetralogy of Fallot patients

A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot

Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction

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