Biology subjects

Fusini, L.

Publications and source records attributed to Fusini, L..

3 recordsLinked to original sources

bioRxiv2025

Personalized computational hemodynamic analysis in transcatheter aortic valve: investigation of long-term degeneration

Introduced as an alternative to open-heart surgery for elderly patients, Transcatheter Aortic Valve Implantation (TAVI) has recently been extended to younger patients due to comparable performance with the gold-standard. However, the long-term durability of the bio-prosthetic TAVI valves is limited by Structural Valve Deterioration (SVD), an inevitable degenerative process whose pathogenesis is still unclear. In this study, we aim to computationally investigate a possible relation between aortic hemodynamics and SVD development. To this aim, we collect data from twelve patients with and without SVD at long-term follow-up exams. Starting from pre-operative clinical images, we build early post-operative virtual scenarios and we perform Computational Fluid Dynamics simulations by prescribing a personalized flow rate based on Echo Doppler data. In order to identify a premature onset of SVD, we propose three computational hemodynamic indices: Wall Damage Index (WDI), Leaflet Delamination Index (LDI), and Leaflet Permeability Index (LPI). Additionally, to each index we associate a score and, using the Wilcoxon rank-sum test, we find that each score individually shows a statistically greater median value in the SVD sub-population (WDI: p = 0.008, LDI: p = 0.001, LPI: p = 0.020). Finally, we define a synthetic scoring system that clearly separates between SVD and non-SVD patients. Our results suggest that aortic hemodynamics may drive a premature onset of SVD, and the synthetic score could potentially assist clinicians in a patient-specific planning of follow-up exams to closely monitor those patients at high SVD risk.

bioengineering↗

bioRxiv2024

Computational hemodynamic indices to identify Transcatheter Aortic Valve Implantation degeneration

PurposeStructural Valve Deterioration (SVD) is the main limiting factor to the long-term durability of bioprosthetic valves, which are used for Transcatheter Aortic Valve Implantation (TAVI). The aim of this study is to perform a patient-specific computational analysis of post-TAVI blood dynamics to identify hemodynamic indices that correlate with a premature onset of SVD. MethodsThe study population comprises two subgroups: patients with and without SVD at long-term follow-up exams. Starting from pre-operative CT images, we created reliable post-TAVI scenarios by virtually inserting the bioprosthetic valve (stent and leaflets), and we performed numerical simulations imposing realistic inlet conditions based on patient-specific data. The numerical results were post-processed to build suitable synthetic scores based on normalized hemodynamic indices. ResultsWe defined three synthetic scores, based on hemodynamic indices evaluated in different contexts: on the leaflets, in the ascending aorta, and in the whole domain. Our proposed synthetic scores are able to clearly isolate the SVD group. Notably, we found that leaflets OSI individually shows statistically significant differences between the two subgroups of patients. ConclusionThe results of this computational study suggest that blood dynamics may play an important role in creating the conditions that lead to SVD. More-over, the proposed synthetic scores could provide further indications for clinicians in assessing and predicting TAVI valves long-term performance.

bioengineering↗

bioRxiv2020

Modeling the effect of COVID-19 disease on the cardiac function: a computational study

BackgroundThe effect of COVID-19 on the cardiac function and on the vascular system increases the morbidity and mortality of infected subjects with cardiovascular diseases. ObjectivesTo provide preliminary results on cardiac global outcomes (such as cardiac output, ventricular pressures) obtained by means of computational models in plausible scenarios characterized by COVID-19. MethodsWe considered a lumped parameters computational model of the cardiovascular system, which models, from the mechanical point of view, the systemic and pulmonary circulations, the four cardiac valves and the four heart chambers, through mathematical equations of the underlying physical processes. To study the effect of COVID-19, we varied the heart rate, the contractility and the pulmonary resistances in suitable ranges. ResultsOur computations on individuals with both otherwise normal and impaired cardiac functions revealed that COVID-19 worsen cardiac function, as shown by a decrease of some cardiac biomarkers values such as cardiac output and ejection fraction. In the case of existing impaired cardiac function, the presence of COVID-19 lead to values outside the normal ranges. ConclusionsComputational models revealed to be an effective tool to study the effect of COVID-19 on the cardiovascular system. Such effect could be significant for patients with impaired cardiac function. This is especially useful to perform a sensitivity analysis of the hemodynamics for different conditions. CONDENSED ABSTRACTEmerging studies address how COVID-19 infection might impact the cardiovascular system. This relates particularly to the development of myocardial injury, acute coronary syndrome, myocarditis, arrhythmia, and heart failure. Prospective treatment approach is advised for these patients. By the assessment of conventional important biomarkers obtained with new sources as a 0-dimentional computational model, we propose a new study protocol as an effective method to evaluate short-term prognosis. The clinical protocol proposed will help to rapidly identify which patients require intensive monitoring, diagnostic strategy and most adequate therapy.

pathology↗