Patient-specific Computational Models Research Articles

Deciphering postoperative hemodynamics in pediatric intensive care: insights from patient-specific computational fluid dynamics models for congenital heart disease

Abstract Purpose Precise hemodynamic control is imperative for optimizing outcomes in children undergoing cardiac surgery, particularly in the pediatric intensive care unit (PICU). Computational fluid dynamic (CFD) models tailored to individual pediatric patients following definitive repairs for congenital heart disease hold the potential to discern and analyze the intricacies of their hemodynamic systems. This study aims to explore the applicability of patient-specific CFD models in determining optimal treatment strategies to enhance patient hemodynamics. Methods Ten postoperative pediatric patients (6 females and 4 males, median age 4.5±3.6 months, median height 62.5±5.3 cm, and median weight 6.2±1.3 kg) diagnosed with ventricular septal defect (VSD, n=5), atrial septal defect (ASD, n=2), and atrioventricular septal defect (AVSD, n=3) were enrolled. Continuous radial arterial waveforms and sporadic arterial blood draws, including lactate levels, were recorded in the PICU postoperatively. Fluid dynamics equations were employed to model major and peripheral blood vessels, utilizing a 0–1-dimensional hemodynamic model validated in previous studies and refined for this investigation. Sensitivity analysis based on correlation coefficients identified significant parameters, adjusted for replicating pressure waveforms. Comparison and re-approximation was made with actual radial artery pressure waveforms from 78 arterial waveforms and blood draws. Updated parameters were then correlated with postoperative time and lactate levels in the ICU. Results Replicated waveforms demonstrated high agreement with original arterial waveforms, with mean, maximal, and minimal blood pressure percentages of 96.7±1.8%, 96.7±2.4%, and 95.8±2.4%, respectively. Notably, systemic vascular resistance and compliance exhibited a substantial relationship with time elapsed after admission to the PICU, with correlation coefficients (R) of 0.26 and 0.43, respectively. Lactate levels demonstrated a decrease with reduced peripheral vascular resistance and an increase in vascular compliance, with R of 0.23 and 0.32, respectively, aligning with expected clinical improvement and stability. Conclusion This study successfully attained precise replication of radial artery blood pressures and intricate pressure waveforms using patient-specific CFD models in pediatric patients following congenital heart disease repairs. The insights gained shed light on the pivotal hemodynamic parameters that impact both stability and instability. This research sets the stage for theoretically informed treatments by amalgamating real-time biological information with patient-specific CFD models.Re-approximation of arterial waveformCorrelation of hemodynamic parameters

Biventricular modelling for investigating ventricular arrhythmias in silico

Abstract Background Ventricular arrhythmias have a significant incidence rate worldwide and may potentially lead to sudden cardiac death in severe cases. Despite therapeutic advancements, the intricate mechanisms dictating these arrhythmias remain elusive. Biventricular modelling and deep learning provide a way to investigate the complex electrophysiological substrates underlying cardiac arrhythmogenesis. Purpose This study aims to develop and validate a patient-specific biventricular computational modelling pipeline for simulating ventricular arrhythmias in silico. We provide a comprehensive platform for understanding the initiation, propagation, and treatment response of various ventricular arrhythmias. Methods To facilitate large-scale computational experiments and personalised model predictions within clinical timeframes, it is imperative to establish a rapid and reproducible modelling framework. First, we constructed biventricular volumetric models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) data from the Cardiac Atlas Project (2018 Atria Segmentation Challenge), and then we calculate biventricular coordinates to facilitate data registration between measures, and fibre and fibrosis inclusion. We also apply the pipeline to cardiac magnetic resonance (CMR) data and electrocardiogram recordings from UK Biobank. Results We have successfully developed a pipeline for constructing biventricular volumetric models and efficiently processed biventricular coordinates (Figure). By systematically exploring interactions between ventricular regions, the model may identify critical factors contributing to arrhythmia susceptibility and guide personalised therapeutic strategies. Furthermore, the model may provide insights into the efficacy of antiarrhythmic interventions, ablation techniques, and device therapies, facilitating the development of precision medicine approaches. Conclusions We demonstrate a pipeline for constructing patient-specific biventricular computational models at scale from imaging data and apply this to LGE-MRI data and UK Biobank CMR data. These biventricular models enable investigation of the complex mechanisms of ventricular arrhythmias, providing novel insights for clinical decision-making and treatment strategies.

Successful cardiac resynchronization therapy reduces negative septal work in patient-specific models of dyssynchronous heart failure.

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

ID: 325017 Patient-specific computational modeling in a longitudinal study of spinal cord stimulation for pain

ID: 325017 Patient-specific computational modeling in a longitudinal study of spinal cord stimulation for pain

Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions - new opportunities for precision medicine.

The last decade has witnessed a substantial growth in percutaneous treatment options for heart valve disease. The development in these innovative therapies has been mirrored by advances in multi-detector computed tomography (MDCT). MDCT plays a central role in obtaining detailed pre-procedural anatomical information, helping to inform clinical decisions surrounding procedural planning, improve clinical outcomes and prevent potential complications. Improvements in MDCT image acquisition and processing techniques have led to increased application of advanced analytics in routine clinical care. Workflow implementation of patient-specific computational modeling, fluid dynamics, 3D printing, extended reality, extracellular volume mapping and artificial intelligence are shaping the landscape for delivering patient-specific care. This review will provide an insight of key innovations in the field of MDCT for planning transcatheter heart valve interventions.

Optimizing distal and proximal splenic artery embolization with patient-specific computational fluid dynamics

Splenic artery embolization (SAE) has become a favored alternative to splenectomy, offering a less invasive intervention for injured spleens while preserving spleen function. However, our understanding of the role that hemodynamics plays during embolization remains limited. In this study, we utilized patient-specific computational fluid dynamics (CFD) simulations to study distal and proximal embolization strategies commonly used in SAE. Detailed 3D computer models were constructed considering the descending aorta, various major visceral arteries, and the iliac arteries. Subsequently, the blood flow and pressure associated with different coil placement locations in proximal embolization were studied considering the collateral vessels. Coil induced variations in pressure fields were quantified and compared to baseline. The coil induced flow stagnation was also quantified with particle residence time. Distal embolization was modeled with Lagrangian particle tracking and the effect of particle size, release location, and timing on embolization outcome was studied. Our findings highlight the crucial role of collateral vessels in maintaining blood supply to the spleen following proximal embolization. It was demonstrated that coil location can affect distal pressure and that strategic coil placement guided by patient-specific CFD simulations can further reduce this pressure as desired. Additionally, the results point to the critical roles that particle size, release timing, and location play in distal embolization. Our study provides an early attempt to use patient-specific computer modeling for optimizing embolization strategies and ultimately improving patient outcomes during SAE procedures.

Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models.

The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability. Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively. Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Predicting Resistance to Immunotherapy in Melanoma, Glioblastoma, Renal, Stomach and Bladder Cancers by Machine Learning on Immune Profiles

Strategies for tackling cancer involve surgery, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICB). However, the effectiveness of ICB remains constrained, prompting the need for a proactive strategy to foresee treatment responses and resistances. This study undertook an analysis across diverse cancer patient cohorts (including melanoma, clear cell renal carcinoma, glioblastoma, bladder, and stomach cancers) subjected to various immune checkpoint blockade treatments. Surprisingly, our findings unveiled that over 38% of patients demonstrated resistance and persistent disease progression despite undergoing ICB intervention. To unravel the intricacies of resistance, we scrutinized the immune profiles of cancer patients experiencing ongoing disease progression and resistance post-ICB therapy. These profiles delineated multifaceted defects, including compromised macrophage, monocyte, and T cell responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and an elevated expression of immunosuppressive and G protein-coupled receptor molecules (TGFB1, IL2RA, IL1B, EDNRB, ADORA2A, SELP, and CD276). Building upon these insights into resistance profiles, we harnessed machine learning algorithms to construct models predicting the response and resistance to ICB and developed the accompanying software. While previous work on glioblastoma with only one type of algorithm had an accuracy of 0.82, we managed to develop 20 models that provided estimates of future events of resistance or response in five cancer types, with accuracies ranging between 0.79 and 1, based on their distinct immune characteristics. In conclusion, our approach advocates for the personalized application of immunotherapy in cancer patients based on patient-specific attributes and computational models.

Whole-brain simulation of interictal epileptic discharges for patient-specific interpretation of interictal SEEG data

In patients with refractory epilepsy, the clinical interpretation of stereoelectroencephalographic (SEEG) signals is crucial to delineate the epileptogenic network that should be targeted by surgery. We propose a pipeline of patient-specific computational modeling of interictal epileptic activity to improve the definition of regions of interest. Comparison between the computationally defined regions of interest and the resected region confirmed the efficiency of the pipeline. This result suggests that computational modeling can be used to reconstruct signals and aid clinical interpretation.

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

Genetic heterogeneity and co-occurring driver mutations impact clinical outcomes in blood cancers, but predicting the emergent effect of co-occurring mutations that impact multiple complex and interacting signalling networks is challenging. Here, we used mathematical models to predict the impact of co-occurring mutations on cellular signalling and cell fates in diffuse large B cell lymphoma and multiple myeloma. Simulations predicted adverse impact on clinical prognosis when combinations of mutations induced both anti-apoptotic (AA) and pro-proliferative (PP) signalling. We integrated patient-specific mutational profiles into personalised lymphoma models, and identified patients characterised by simultaneous upregulation of anti-apoptotic and pro-proliferative (AAPP) signalling in all genomic and cell-of-origin classifications (8-25% of patients). In a discovery cohort and two validation cohorts, patients with upregulation of neither, one (AA or PP), or both (AAPP) signalling states had good, intermediate and poor prognosis respectively. Combining AAPP signalling with genetic or clinical prognostic predictors reliably stratified patients into striking prognostic categories. AAPP patients in poor prognosis genetic clusters had 7.8 months median overall survival, while patients lacking both features had 90% overall survival at 120 months in a validation cohort. Personalised computational models enable identification of novel risk-stratified patient subgroups, providing a valuable tool for future risk-adapted clinical trials.

How does sutures pattern influence stomach motility after endoscopic sleeve gastroplasty? A computational study.

The relatively recent adoption of Endoscopic Sleeve Gastroplasty (ESG) amongst obese patients has gained approval within the surgical community due to its notable benefits, including significant weight loss, safety, feasibility, repeatability, and potential reversibility. However, despite its promising clinical outcomes and reduced invasiveness, there is still a lack of standardised procedures for performing ESG. Multiple suture patterns and stitching methods have been proposed over time, yet rational tools to quantify and compare their effects on gastric tissues are absent. To address this gap, this study proposed a computational approach. The research involved a case study analyzing three distinct suture patterns (C-shaped, U-shaped and Z-shaped) using a patient-specific computational stomach model generated from magnetic resonance imaging. Simulations mimicked food intake by placing wire features in the intragastric cavity to replicate sutures, followed by applying a linearly increasing internal pressure up to 15mmHg. The outcomes facilitated comparisons between suture configurations based on pressure-volume behaviours and the distribution of maximum stress on biological tissues, revealing the U-shaped as the more effective in terms of volume reduction, even if with reduced elongation strains and increased tissues stresses, whereas the Z-shaped is responsible of the greatest stomach shortness after ESG. In summary, computational biomechanics methods serve as potent tools in clinical and surgical settings, offering insights into aspects that are challenging to explore in vivo, such as tissue elongation and stress. These methods allow for mechanical comparisons between different configurations, although they might not encompass crucial clinical outcomes.

The right atrium affects in silico arrhythmia vulnerability in both atria

Abstract Introduction The role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients. It remains unclear if incorporating the RA influences the reentry inducibility of the model. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain. Aim To evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability. Methods Imaging data from 8 subjects were obtained to generate patient-specific computer models. We created 2 models for each subject: a monoatrial with only the LA and a biatrial with both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche cellular model was modified from control conditions to a setup representing AF-induced remodeling [1] with 0%, 50%, and 100% changes for H, M, and S, respectively. Conduction velocity was set to 1.2, 1.0, and 0.8m/s for each remodeling state. Fibrosis extent corresponded to Utah 2 (5-20%) and Utah 4 (>35%) stages [2] for M and S, while the H state was modeled without fibrosis. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm using openCARP [3]. A point was classified as inducing arrhythmia if reentry was maintained for at least 1s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) was assessed at the stimulation site. We compared LA vulnerability ratios in monoatrial and biatrial models. Results Incorporating the RA increased the mean LA vulnerability ratio by 115.8% (0.19±0.13 to 0.41±0.22, p=0.033) in state M and 29.0% in state S (0.31±0.14 to 0.40±0.15, p=0.219) as illustrated in Figure 1. No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.5% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in scenario M and decreased it by 7.2% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion increased LA inducibility revealing 5.5±3.0 new points per patient in the LA for the biatrial model, which did not induce reentry in the monoatrial model. Conclusions LA arrhythmia vulnerability in a biatrial model differs from that in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models.

#2795 In-silico assessment of arteriovenous fistula maturation with patient-specific computational hemodynamic models

Abstract Background and Aims Native arteriovenous fistula (nAVF) is the preferred access for haemodialysis (HD) treatment. Both the quality and complications of the haemodialysis technique are closely related to vascular access. nAVF maturation process includes several morphological changes affecting haemodynamics, with previous studies suggesting that it is highly related to AVF failure. Computational Fluid Dynamics (CFD) simulations by using idealized morphologies or the analysis of one patient-specific geometry have been proposed to understand the pathophysiology of maturation failure. However, it is difficult to correlate with clinical practice and therefore limits its use. The aim is to develop a patient-specific modelling time pipeline, incorporating the haemodynamics of the blood flow to real anatomical and morphological characteristics. Method Design: prospective computational simulation analysis of nAVF in 3 patients at 3 different stages: after fistula creation, (1 week), maturation (1 month) and post-maturation (6 months). Analysis: morphological and haemodynamic. Inclusion criteria: >18 years and end-stage chronic kidney disease. Variables and data: Once morphology was extracted from nAVF magnetic resonance imaging, generated meshes were refined and compiled to obtain a volumetric mesh. The boundary conditions of the model were defined and extracted using ultrasound nAFV data. Data were analyzed at the 3 stages. Models: A 3-element Windkessel model was used to simulate the pressure waveform to feed the vein of the system. In this model, clinical data were also considered to classify the patient as normotensive or hypertensive to estimate waveform parameters. The Windkessel model estimates wall shear stress (WSS), velocity and flow for all the points located in the mesh by making an analogy from an electric circuit resistance model. As validation, the flow obtained from the Windkessel model was compared with the one obtained from the ultrasound scan of the patient. Results Different temporal points were analyzed for each nAVF. Results from the simulations in Fig. 1, illustrating the evolution over time and development. The geometrical characteristics were found to significantly impact on flow dynamics. The area most affected by flow was the juxta-anastomotic section which showed higher magnitude distribution of WSS and velocity for all time-points. According to the analysis of the simulations, a perpendicular junction between the vein and the artery potentially resulted in less damage in this area (in WSS terms), being important to mitigate the AVF failure. At maturation stage (1 month) the analysis shows WSS distribution mainly located in the juxta-anastomotic section and decreased in magnitude regarding one week; therefore, this can be marked as the stabilization point for nAFV maturation. Conclusion

In silico analysis of embolism in cerebral arteries using fluid-structure interaction method

Ischemic stroke, particularly embolic stroke, stands as a significant global contributor to mortality and long-term disabilities. This paper presents a comprehensive simulation of emboli motion through the middle cerebral artery (MCA), a prevalent site for embolic stroke. Our patient-specific computational model integrates major branches of the middle cerebral artery reconstructed from magnetic resonance angiography images, pulsatile flow dynamics, and emboli of varying geometries, sizes, and material properties. The fluid-structure interactions method is employed to simulate deformable emboli motion through the middle cerebral artery, allowing observation of hemodynamic changes in artery branches upon embolus entry. We investigated the impact of embolus presence on shear stress magnitude on artery walls, analyzed the effects of embolus material properties and geometries on embolus trajectory and motion dynamics within the middle cerebral artery. Additionally, we evaluated the non-Newtonian behavior of blood, comparing it with Newtonian blood behavior. Our findings highlight that embolus geometry significantly influences trajectory, motion patterns, and hemodynamics within middle cerebral artery branches. Emboli with visco-hyperelastic material properties experienced higher stresses upon collision with artery walls compared to those with hyperelastic properties. Furthermore, considering blood as a non-Newtonian fluid had notable effects on emboli stresses and trajectories within the artery, particularly during collisions. Notably, the maximum von Mises stress experienced in our study was 21.83 kPa, suggesting a very low probability of emboli breaking during movement, impact, and after coming to a stop. However, in certain situations, the magnitude of shear stress on them exceeded 1 kPa, increasing the likelihood of cracking and disintegration. These results serve as an initial step in anticipating critical clinical conditions arising from arterial embolism in the middle cerebral artery. They provide insights into the biomechanical parameters influencing embolism, contributing to improved clinical decision-making for stroke management.

Image-based biomarkers for engineering neuroblastoma patient-specific computational models

AbstractChildhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours.

Impact of Age-Related Change in Caval Flow Ratio on Hepatic Flow Distribution in the Fontan Circulation.

The Fontan operation is a palliative technique for patients born with single ventricle heart disease. The superior vena cava (SVC), inferior vena cava (IVC), and hepatic veins are connected to the pulmonary arteries in a total cavopulmonary connection by an extracardiac conduit or a lateral tunnel connection. A balanced hepatic flow distribution (HFD) to both lungs is essential to prevent pulmonary arteriovenous malformations and cyanosis. HFD is highly dependent on the local hemodynamics. The effect of age-related changes in caval inflows on HFD was evaluated using cardiac magnetic resonance data and patient-specific computational fluid dynamics modeling. SVC and IVC flow from 414 patients with Fontan were collected to establish a relationship between SVC:IVC flow ratio and age. Computational fluid dynamics modeling was performed in 60 (30 extracardiac and 30 lateral tunnel) patient models to quantify the HFD that corresponded to patient ages of 3, 8, and 15 years, respectively. SVC:IVC flow ratio inverted at ≈8 years of age, indicating a clear shift to lower body flow predominance. Our data showed that variation of HFD in response to age-related changes in caval inflows (SVC:IVC, 2, 1, and 0.5 corresponded to ages, 3, 8, and 15+, respectively) was not significant for extracardiac but statistically significant for lateral tunnel cohorts. For all 3 caval inflow ratios, a positive correlation existed between the IVC flow distribution to both the lungs and the HFD. However, as the SVC:IVC ratio changed from 2 to 0.5 (age, 3-15+) years, the correlation's strength decreased from 0.87 to 0.64, due to potential flow perturbation as IVC flow momentum increased. Our analysis provided quantitative insights into the impact of the changing caval inflows on Fontan's long-term HFD, highlighting the importance of SVC:IVC variations over time on Fontan's long-term hemodynamics. These findings broaden our understanding of Fontan hemodynamics and patient outcomes.

Tailoring left ventricular assist device cannula implantation using coupled multi-scale multi-objective optimization

BackgroundThe frequent occurrence of thromboembolic cerebral events continues to limit the widespread implementation of Ventricular Assist Devices (VAD) despite continued advancements in VAD design and anti-coagulation treatments. Recent studies point to the optimal positioning of the outflow graft (OG) as a potential mitigator of post implantation thromboembolism. ObjectiveThis study aims to examine the tailoring of the OG implantation orientation with the goal of minimizing the number of thrombi reaching the cerebral vessels by means of a formal shape optimization scheme incorporated into a multi-scale hemodynamics analysis. MethodsA 3-D patient-specific computational fluid dynamics model is loosely coupled in a two-way manner to a 0-D lumped parameter model of the peripheral circulation. A Lagrangian particle-tracking scheme models and tracks thrombi as non-interacting solid spheres. The loose coupling between CFD and LPM is integrated into a geometric shape optimization scheme which aims to optimize an objective function that targets a drop in cerebral embolization, and an overall reduction in particle residence times. ResultsThe results elucidate the importance of OG anastomosis orientation and placement particularly in the case that studied particle release from the OG, as a fivefold decrease in cerebral embolization was observed between the optimal and non-optimal implantations. Another case considered particle release from the ventricle and aortic root walls, in which optimal implantation was achieved with a shallow insertion angle. Particle release from all three origins was investigated in the third case, demonstrating that the optimal configurations were generally characterized by VAD flow directed along the central lumen of the aortic arch. Because optimal configurations depended on the anatomic origin of the thrombus, it is important to determine, in clinical studies, the most likely sites of thrombus formation in VAD patients.

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.

Preoperative computed tomography-imaging with patient-specific computer simulation in transcatheter aortic valve implantation: Design and rationale of the GUIDE-TAVI trial

Transcatheter aortic valve implantation (TAVI) is an established treatment option for patients with severe aortic valve stenosis, but is still associated with relatively high rates of pacemaker implantation and paravalvular regurgitation. Routine preoperative computed tomography (CT) combined with patient-specific computer modelling can predict the interaction between the TAVI device and the patient's unique anatomy, allowing physicians to assess the risk for paravalvular regurgitation and conduction disorders in advance to the procedure. The aim of this trial is to assess potential improvement in the procedural outcome of TAVI by applying CT-based patient-specific computer simulations in patients with suitable anatomy for TAVI. The GUIDE-TAVI trial is an international multicenter randomized controlled trial including patients accepted for TAVI by the Heart Team. Patients enrolled in the study will be randomized into 2 arms of each 227 patients. In patients randomized to the use of FEops HEARTGuide (FHG), patient-specific computer simulation with FHG is performed in addition to routine preoperative CT imaging and results of the FHG are available to the operator(s) prior to the scheduled intervention. In patients randomized to no use of FHG, only routine preoperative CT imaging is performed. The primary objective is to evaluate whether the use of FHG will reduce the incidence of mild to severe PVR, according to the Valve Academic Research Consortium 3. Secondary endpoints include the incidence of new conduction disorders requiring permanent pacemaker implantation, the difference between preoperative and final selected valve size, the difference between target and final implantation depth, change of preoperative decision, failure to implant valve, early safety composite endpoint and quality of life. The GUIDE-TAVI trial is the first multicenter randomized controlled trial to evaluate the value of 3-dimensional computer simulations in addition to standard preprocedural planning in TAVI procedures.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.