Abstract
Congestive heart failure (CHF) is one of the leading causes of death worldwide, despite the optimal treatment. Cardiac resynchronization therapy (CRT) is one of the established methods for treating severe CHF with conduction disorders, in particular, complete left bundle branch block (LBBB). However, to the date, up to 30% of patients do not respond to CRT. This study is focused on the developing model-based approaches allowing one to predict consequences of ventricular pacing after installing a CRT device based on computational cardiac models.
In this work, we used experimental data from the STACOM 2019 “CRT-EPiggy” Challenge containing a training dataset of EAM data recorded in ventricles of 4 pig hearts. To simulate local activation time (LAT) in the model we used the Eikonal equation based model, which parameters were identified based on the experimental data. Solving an optimisation problem over the conductivity parameters of this model, we were able to achieve a good quality of LAT simulations before and after bi-ventricular pacing with a mean error of about 3 ms.
We found essential changes in the local conduction velocity (CV) in the ventricles at bi-ventricular pacing after CRT both in experimental data and simulations. To predict these changes and post-operational LAT from the pre-operational data, we used a population based approach to simulate effects of conductivity modulation due to pacing. This approach allowed us to predict an activation pattern at ventricular pacing based on the optimised model of LAT before pacing with an average error of 7 ms. Despite the promising overall results of our pilot study, the presence of rather big local errors in the model predictions requires further algorithm improvement.
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Acknowledgements
The work was supported by RSF grant No. 19-14-00134. Supercomputer URAN of IMM UrB RAS was used for model calculations.
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Khamzin, S., Dokuchaev, A., Solovyova, O. (2020). Prediction of CRT Response on Personalized Computer Models. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_37
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DOI: https://doi.org/10.1007/978-3-030-39074-7_37
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