Resilience and response of the congenital cardiac network in Italy during the COVID-19 pandemic

: The worldwide response to the current COVID-19 pandemic has been focused on how to prevent the disease and to protect the high-risk patient from a potentially lethal infection. Several consensus and guidelines articles have been published dealing with the cardiac patient with systemic hypertension, heart transplant or heart failure. Very little is known about the patients, both in the pediatric as well as in the adult age, with congenital heart disease. The peculiar physiology of the heart with a native, repaired or palliated congenital heart defect deserves a specialized care. Hereby we describe the early recommendations issued by the Italian Society of Pediatric Cardiology and Congenital Heart Disease and how the network of the congenital cardiac institutions in Italy reacted to the threat of potential wide spread of the infection among this fragile kind of patient

Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

International audienceComputer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations

Longitudinal Analysis using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies

International audiencePersonalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution