An application of a patient-specific cardiac simulator for the prediction of outcomes after mitral valve replacement: a pilot study View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-03-19

AUTHORS

Hirotada Masuda, Shigeru Miyagawa, Seiryo Sugiura, Takumi Washio, Jun-ichi Okada, Takayoshi Ueno, Koichi Toda, Toru Kuratani, Toshiaki Hisada, Yoshiki Sawa

ABSTRACT

Despite advancements in preoperative prediction of patient outcomes, determination of the most appropriate surgical treatments for patients with severely impaired cardiac function remains a challenge. "UT-Heart" is a multi-scale, multi-physics heart simulator, which can be used to assess the effects of treatment without imposing any burden on the patients. This retrospective study aimed to assess whether UT-Heart can function as a tool that aids decision making for performing mitral valve replacements (MVR) in patients with severe mitral regurgitation (MR) and impaired left ventricular (LV) function. We used preoperative clinical data to create a patient-specific heart model using UT-Heart for a patient who had dilated cardiomyopathy with severe MR. After confirming that this heart model reproduced the preoperative state of the patient, we performed an in silico MVR operation without changing any parameters, such as the end-diastolic volume of the left ventricle, systemic vascular resistance, and the number of myocardiocytes. Among the functional changes introduced by in silico surgery, we found two indices, forward flow and the mechanical efficiency of the work done to the systemic circulation, which may relate positively to the favorable outcome observed in the real world. Thus, multi-scale, multi-physics heart simulators can reproduce the pathophysiology of MR with impaired LV function. By performing in silico MVR and examining the resultant functional changes, we identified two indices, whose usefulness should be tested in future studies. More... »

PAGES

351-357

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10047-021-01248-6

DOI

http://dx.doi.org/10.1007/s10047-021-01248-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1136533682

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33740156


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