The objective of this work is to perform a virtual planning of surgical repairs in patients with congenital heart diseases-to test the predictive capability of a closed-loop multi-scale model. As a first step, we reproduced the pre-operative state of a specific patient with a univentricular circulation and a bidirectional cavopulmonary anastomosis (BCPA), starting from the patient's clinical data. Namely, by adopting a closed-loop multi-scale approach, the boundary conditions at the inlet and outlet sections of the three-dimensional model were automatically calculated by a lumped parameter network. Successively, we simulated three alternative surgical designs of the total cavopulmonary connection (TCPC). In particular, a T-junction of the venae cavae to the pulmonary arteries (T-TCPC), a design with an offset between the venae cavae (O-TCPC) and a Y-graft design (Y-TCPC) were compared. A multi-scale closed-loop model consisting of a lumped parameter network representing the whole circulation and a patient-specific three-dimensional finite volume model of the BCPA with detailed pulmonary anatomy was built. The three TCPC alternatives were investigated in terms of energetics and haemodynamics. Effects of exercise were also investigated. Results showed that the pre-operative caval flows should not be used as boundary conditions in post-operative simulations owing to changes in the flow waveforms post-operatively. The multi-scale approach is a possible solution to overcome this incongruence. Power losses of the Y-TCPC were lower than all other TCPC models both at rest and under exercise conditions and it distributed the inferior vena cava flow evenly to both lungs. Further work is needed to correlate results from these simulations with clinical outcomes.*Author for correspondence (giancarlo.pennati@polimi.it).One contribution of 11 to a Theme Issue 'Towards the virtual physiological human: mathematical and computational case studies'.This journal is

Single ventricle hearts are congenital cardiovascular defects in which the heart has only one functional pumping chamber. The treatment for these conditions typically requires a three-staged operative process where Stage 1 is typically achieved by a shunt between the systemic and pulmonary arteries, and Stage 2 by connecting the superior venous return to the pulmonary circulation. Surgically, the Stage 2 circulation can be achieved through a procedure called the Hemi-Fontan, which reconstructs the right atrium and pulmonary artery to allow for an enlarged confluence with the superior vena cava. Based on pre-operative data obtained from two patients prior to Stage 2 surgery, we developed two patient-specific multi-scale computational models, each including the 3D geometrical model of the surgical junction constructed from magnetic resonance imaging, and a closed-loop systemic lumped-parameter network derived from clinical measurements. "Virtual" Hemi-Fontan surgery was performed on the 3D model with guidance from clinical surgeons, and a corresponding multi-scale simulation predicts the patient's post-operative hemodynamic and physiologic conditions. For each patient, a post-operative active scenario with an increase in the heart rate (HR) and a decrease in the pulmonary and systemic vascular resistance (PVR and SVR) was also performed. Results between the baseline and this "active" state were compared to evaluate the hemodynamic and physiologic implications of changing conditions. Simulation results revealed a characteristic swirling vortex in the Hemi-Fontan in both patients, with flow hugging the wall along the SVC to Hemi-Fontan confluence. One patient model had higher levels of swirling, recirculation, and flow stagnation. However, in both models, the power loss within the surgical junction was less than 13% of the total power loss in the pulmonary circulation, and less than 2% of the total ventricular power. This implies little impact of the surgical junction geometry on the SVC pressure, cardiac output, and other systemic parameters. In contrast, varying HR, PVR, and SVR led to significant changes in theses clinically relevant global parameters. Adopting a work-flow of customized virtual planning of the Hemi-Fontan procedure with patient-specific data, this study demonstrates the ability of multi-scale modeling to reproduce patient specific flow conditions under differing physiological states. Results demonstrate that the same operation performed in two different patients can lead to different hemodynamic characteristics, and that modeling can be used to uncover physiologic changes associated with different clinical conditions.

In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi-scale modeling.

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology.

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Newborns with single ventricle physiology are usually palliated with a multi-staged procedure. When cardiovascular complications e.g., collateral vessel formation occur during the inter-stage periods, further treatments are required. An 8-month-old patient, who underwent second stage (i.e., bi-directional Glenn, BDG) surgery at 4 months, was diagnosed with a major veno-venous collateral vessel (VVC) which was endovascularly occluded to improve blood oxygen saturations. Few clinical data were collected at 8 months, whereas at 4 months a more detailed data set was available. The aim of this study is threefold: (i) to show how to build a patient-specific model describing the hemodynamics in the presence of VVC, using patient-specific clinical data collected at different times; (ii) to use this model to perform virtual VVC occlusion for quantitative hemodynamics prediction; and (iii) to compare predicted hemodynamics with post-operative measurements. The three-dimensional BDG geometry, resulting from the virtual surgery on the first stage model, was coupled with a lumped parameter model (LPM) of the 8-month patient's circulation. The latter was developed by scaling the 4-month LPM to account for changes in vascular impedances due to growth and adaptation. After virtual VVC closure, the model confirmed the 2 mmHg BDG pressure increase, as clinically observed, suggesting the importance of modeling vascular adaptation following the BDG procedure.

BACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These

Introduction It is well recognized that early identification of drug-induced proarrhythmic safety risks is crucial to drug development for ethical, animal sparing and costs reduction considerations. The availability, however, of easily accessible, user-friendly tools for real time assessments of the proarrhythmic potential of chemical compounds has been lacking. The novel Tx index, implemented in the presented web-based tool, was applied to a dataset of 84 compounds. Materials and methods The tool is based on 206,766 cellular simulations of compound-induced effects on Action Potential Duration (APD) in isolated endocardial, midmyocardial, and epicardial cells and on 7,072 tissue simulations on QT prolongation in a virtual tissue. Simulations were performed by blocking the slow and the fast components of the delayed rectifier current (IKs and IKr, respectively) and the L-type calcium current (ICaL) at different levels. Based on these simulations, four Tx indices were defined as the ratio of drug concentration leading to a 10% prolongation of the APDendo, APDmid, APDepi or QT over the maximum Effective Free Therapeutic Plasma Concentration (EFTPC), respectively. A dataset of 44 non-torsadogenic and 40 torsadogenic drug compounds was used to validate the performance of the tool. The workflow of the web-based tool was built on the cloud environment, in compliance with the highest standards of security and privacy. hERG test (positive response: hERG pIC50 >6) was applied to the 84 compounds to compare performances. Results Receiver operating characteristic (ROC) curves were constructed on the four estimated Tx indices for each compound in the dataset to enable the identification of torsadogenic potential cut-off values2. These were identified as 8, 8, and 6.4 for Tx-APDendo, Tx-APDmid, Tx-APDepi and as 9.2 for Tx-QT, respectively. The classification of the 84 compounds resulted in an accuracy ranging between 87% and 88% for the four Tx indices Tx-APDendo, Tx-APDmid, Tx-APDepi and Tx-QT. Discussion and conclusion hERG block exhibits poor performance. When applying the hERG test to the 84 compounds, it exhibited a TPR of 55%, a TNR of 89%, and an A of 73%, in close agreement with previous studies. In comparison, the in silico Tx tests described in this study yield TPRs of 85%, TNRs of 86–89% and As of 86–87%. This method does not include drug effects on Na+ channels, which is related to the misclassification of 3 compounds (quetiapine, ranolazine, and lamotrigine – significant Na+ channels blockers at EFTPC). Future work will include this channel. The presented web-based tool is a highly innovative method for an accurate torsadogenic risk assessment. Each assessment required only a few seconds of computational time. Illustration workflow of the web tool Funding Acknowledgement Type of funding source: None