Patient-specific Computational Models Research Articles

Impact of effective refractory period personalization on prediction of atrial fibrillation vulnerability

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska- Curie grant agreement Introduction Although the effective refractory period (ERP) is one of the main electrophysiological properties governing atrial tachycardia (AT) maintenance, ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision making. State-of-the-art models usually do not consider physiological ERP gradients but assume a homogeneous ERP distribution. This assumption might have an influence on the ability to induce reentries in the model. Aim To evaluate the impact of incorporating clinical ERP measurements when creating in silico personalized models to predict vulnerability to atrial fibrillation (AF). Methods Clinical ERP measurements were obtained from three patients from multiple locations in the atria. The protocol for ERP identification consisted of trains of 7 S1 stimuli with a basic cycle length of 500ms followed by an S2 stimulus with a coupling interval between 300 and 200ms in decrements of 10ms until loss of capture. The atrial geometries from the electroanatomical mapping system were used to generate personalized atrial models. To reproduce patient-specific ERP, the established Courtemanche cellular model was gradually reparameterized from control conditions to a setup representing AF-induced remodeling. Three different approaches were studied: 1) a control scenario with no ERP personalization 2) a discrete split where each region had a single ERP value and 3) a continuous ERP distribution by interpolation of measured ERP data (Fig. 1). Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different locations separated by 3cm. The number and location of inducing points and type of arrhythmia were determined for the three approaches. The mean conduction velocity was set to 0.7 m/s and the electrical propagation in the atria was modeled by the monodomain equation and solved with openCARP. Results Incorporating patient-specific ERP as a continuous distribution did not induce any reentrant activity. A summary of induced ATs is shown in Table 1. For patient A, AF was induced from 3 different locations with the control setup, whereas 9 ATs were induced with the regional method, of which 4 were AF and 5 macro reentries. For patient B, AF was induced from 1 point with the control setup; whereas with the regional approach, AF was induced at 4 points. For patient C, only one macro reentry was induced with the regional method. Conclusion Incorporation of patient-specific ERP values has an impact on the assessment of AF vulnerability. Furthermore, the type of personalization affects the likelihood of AF inducibility. The incorporation of more detailed ERP distributions may lead to a more accurate prediction of AF trigger points and could in the future inform patient-specific therapy planning. Larger cohorts need to follow to demonstrate the role of incorporating clinical patient-specific ERP values into personalized models for predicting AF vulnerability.

PO-01-212 A NOVEL DEEP LEARNING MODEL FOR PATIENT-SPECIFIC COMPUTATIONAL MODELING OF CARDIAC ELECTROPHYSIOLOGY

Patient-specific computational modeling of cardiac electrophysiology (EP) has been shown to have a potential utility in a wide range of arrhythmia prognostics and treatment applications. Yet, it requires extensive computational resources, posing a critical challenge for expanding its application in the clinic. Hence, there is a pressing need to develop accurate and trustable personalized EP heart models with affordable computational costs. We develop a novel approach to modeling patient-specific cardiac electrophysiology, which utilizes, at a much-reduced cost, deep learning in lieu of solving the equations for electrical wave propagation. The new approach is developed based on an operator-learning neural network (DeepONet) to predict the propagation of electric signals in different patient-specific heart chambers (Fig. a). First, we parametrize the geometry of atria and ventricles from a cohort of patients (300+) with principal component analysis (PCA), which implicitly encodes geometric information such as wall thickness, fiber orientation, or fibrosis of each heart (Fig. b). Next, we perform pacing using the computational approach on each geometry and collect the simulated data as the training dataset of the deep-learning model. Then, we train our model to predict electric signal propagation given the encoded geometric information with the pacing location. After training, the accuracy of the deep learning approach is demonstrated by comparing its predictions with simulation results on unseen geometries: the averaged maximum relative difference is lower than 10%, and the mean relative error is lower than 5%. Regarding time cost, our model makes predictions within a fraction of a second on a personal laptop, whereas a standard computational model takes up to 12+ hours on clusters with multiple CPUs (Fig. c). We propose a novel deep-learning approach for modeling patient-specific cardiac electrophysiology with high accuracy and reduced computational cost. This development paves the way for the utilization of personalized cardiac EP modeling in clinical applications, as part of the clinical workflow, in ablation target predictions, and in arrhythmia morphology assessment.

Looking towards the future: patient-specific computational modeling to optimize outcomes for transcatheter mitral valve repair.

Severe mitral valve regurgitation (MR) is a heart valve disease that progresses to end-stage congestive heart failure and death if left untreated. Surgical repair or replacement of the mitral valve (MV) remains the gold standard for treatment of severe MR, with repair techniques aiming to restore the native geometry of the MV. However, patients with extensive co-morbidities may be ineligible for surgical intervention. With the emergence of transcatheter MV repair (TMVR) treatment paradigms for MR will evolve. The longer-term outcomes of TMVR and its effectiveness compared to surgical repair remain unknown given the differing patient eligibility for either treatment at this time. Advances in computational modeling will elucidate answers to these questions, employing techniques such as finite element method and fluid structure interactions. Use of clinical imaging will permit patient-specific MV models to be created with high accuracy and replicate MV pathophysiology. It is anticipated that TMVR technology will gradually expand to treat lower-risk patient groups, thus pre-procedural computational modeling will play a crucial role guiding clinicians towards the optimal intervention. Additionally, concerted efforts to create MV models will establish atlases of pathologies and biomechanics profiles which could delineate which patient populations would best benefit from specific surgical vs. TMVR options. In this review, we describe recent literature on MV computational modeling, its relevance to MV repair techniques, and future directions for translational application of computational modeling for treatment of MR.

Quantifying 30 Years of Technological Innovation in Total Hip Arthroplasty

AbstractTechnological innovation in total hip arthroplasty (THA) is a critical component of health care delivery for improving patient outcomes. Analyzing the content of patents and trends in their activity over time can leverage insight into innovations in THA; however, these investigations remain underutilized. We aimed to quantify patent activity and relative growth pertaining to technologies in THA over a 30-year period. A query for patents relating to “hip replacement” or “hip arthroplasty” between 1989 and 2019 using an open-source patent database yielded 24,071 unique patents for analysis. These were grouped into 12 independent technology clusters using Cooperative Patent Classification codes. Annual patent activity for each cluster was normalized with a validated formula to control for exponential growth. The 5-, 10-, and 30-year compound annual growth rates (CAGR) for each cluster were quantified and compared between clusters. Annual cumulative patent activity increased from 151 patents in 1989 to 1,182 patents in 2019, representing a 682.8% increase. The largest technology clusters concerned “drugs” (n = 6,429 patents), “instruments” (n = 4,014 patents), “materials” (n = 2,037 patents), and “components” (n = 1,946 patents). One-half of the identified clusters concerned developments in technology-assisted THA (patient-specific instrumentation, navigation, robotics, design/manufacturing, instruments, and computer modeling). The most rapidly growing technology within the past 5, 10, and 30 years was robotics (CAGR = 15.82%, 20.11%, and 9.50%, respectively). Within the past 5- and 10-year trial prostheses (CAGR = 10.76%) and design/manufacturing processes (CAGR = 10.27%), computer modeling (CAGR = 12.91%) and navigation (CAGR = 11.25%) were among the most rapidly growing clusters, respectively. The majority of patents pertaining to THA over the past 30 years have concerned developments in drugs, instruments, materials, and components. Within the past 10-years, substantial growth has been observed in patent domains related to technology-assisted THA. Monitoring of emerging technologies through patent metrics may provide valuable information for regulatory bodies and assess progress in the field of THA. This is a cross-sectional study with level of evidence IV.

Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls.

Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid-Structure Interaction (FSI) model of a patient-specific aorta. The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.

Parameter inference in a computational model of haemodynamics in pulmonary hypertension.

Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) greater than 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right heart catheterization (RHC). Patient-specific cardiovascular systems-level computational models provide a potential non-invasive tool for determining additional indicators of disease severity. Using computational modelling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers, the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic and diastolic values) RHC data and a combination of static and continuous, time-series waveform data. We determine a subset of identifiable parameters for model calibration using sensitivity analyses and multi-start inference and perform posterior uncertainty quantification. Results show that additional waveform data enables accurate calibration of the right atrial reservoir and pump function across the PH cohort. Model outcomes, including stroke work and pulmonary resistance-compliance relations, reflect typical right heart dynamics in PH phenotypes. Lastly, we show that estimated parameters agree with previous, non-modelling studies, supporting this type of analysis in translational PH research.

Computational Re-Entry Vulnerability Index Mapping to Guide Ablation in Patients With Postmyocardial InfarctionVentricular Tachycardia.

Ventricular tachycardias (VTs) in patients with myocardial infarction (MI) are often treated with catheter ablation. However, the VT induction during this procedure does not always identify all of the relevant activationpathways or may not be possible or tolerated. The re-entry vulnerability index (RVI) quantifies regional activation-repolarization differences and can detect multiple regions susceptible to re-entry without the need to inducethe arrhythmia. This study aimed to further develop and validate the RVI mapping in patient-specific computational models of post-MI VTs. Cardiac magnetic resonance imaging data from 4 patients with post-MI VTs were used to induce VTs inacomputational electrophysiological model by pacing. The RVI map of a premature beat in each patient modelwasused to guide virtual ablations. We compared our results with those of clinical ablation in the same patients. Single-site virtual RVI-guided ablation prevented VT induction in 3 of 9 cases. Multisite virtual ablations guided by RVI mapping successfully prevented re-entry in all cases (9 of 9). Overall, virtual ablation required 15-fold fewer ablation sites (235.5 ± 97.4 vs 17.0 ± 6.8) and 2-fold less ablation volume (5.34 ± 1.79mL vs 2.11 ± 0.65mL) than the clinical ablation. RVI mapping allows localization of multiple regions susceptible to re-entry and may help guide VT ablation. RVI mapping does not require the induction of arrhythmia and may result in less ablated myocardial volumeswith fewer ablation sites.

Pulse wave velocity: A clinical measure to aid material parameter estimation in computational arterial biomechanics

Determining proper material parameters from clinical data remains a large, though unavoidable, challenge in patient-specific computational cardiovascular modeling. In an attempt to couple the clinical and modelling practice, this study investigated whether pulse wave velocity (PWV), a clinical arterial stiffness measure, can guide in determining appropriate parameter values for the Gasser-Ogden-Holzapfel (GOH) constitutive model. The reduction and uncertainty analysis was demonstrated on a cylindrical descending thoracic aorta model.Starting from discretized ranges of GOH parameters and using a full factorial design, the parameter sets yielding a physiological PWV (3.5–12.5 m/s) at diastolic pressure (80 mmHg; PWV80) were selected and their PWV at dicrotic notch pressure (110 mmHg; PWV110) was determined. These PWV measures were applied to determine the reduction of the 7D GOH parameter space, the 2D subspaces and the remaining uncertainty in case only PWV80 or both measurements are available.The resulting 12,032 parameter sets lead to a 7D parameter space reduction of ≥ 82.5 % using PWV80, which increased to 96.0 % when including PWV110, in particular at 3.5–8.5 m/s. A similar trend was observed for the remaining uncertainty and the 2D subspaces comprised of medial collagen fiber parameters, while scarce reductions were found for the adventitial and elastin parameters.In conclusion, PWV80 and PWV110 are complementary measures with the potential to reduce the GOH parameter space in arterial models, in particular for media- and collagen-related parameters. Moreover, this approach has the advantage that it allows the estimation of the remaining uncertainty after parameter space reduction.

Personalized surgical planning for coronary bypass graft configurations using patient-specific computational modeling to avoid flow competition in arterial grafts.

Flow competition between coronary artery bypass grafts (CABG) and native coronary arteries is a significant problem affecting arterial graft patency. The objectives of this study were to compare the predictive hemodynamic flow resulting from various total arterial grafting configurations and to evaluate whether the use of computational fluid dynamics (CFD) models capable of predicting flow can assist surgeons to make better decisions for individual patients by avoiding poorly functioning grafts. Sixteen cardiac surgeons declared their preferred CABG configuration using bilateral internal mammary and radial arteries for each of 5 patients who had differing degrees of severe triple vessel coronary disease. Surgeons selected both a preferred 'aortic' strategy, with at least one graft arising from the ascending aorta, and a preferred "anaortic" strategy which could be performed as a "no-aortic touch" operation. CT coronary angiograms of the 5 patients were coupled to CFD models using a novel flow solver "COMCAB." Twelve different CABG configurations were compared for each patient of which 4 were "aortic" and 8 were "anaortic." Surgeons then selected their preferred grafting configurations after being shown predictive hemodynamic metrics including functional assessment of stenoses (instantaneous wave-free ratio; fractional flow reserve), transit time flowmetry graft parameters (mean graft flow; pulsatility index) and myocardial perfusion. A total of 87.5% (7/8) of "anaortic" configurations compared to 25% (1/4) of "aortic" configurations led to unsatisfactory grafts in at least 1 of the 5 patients (P = 0.038). The use of the computational models led to a significant decrease in the selection of unsatisfactory grafting configurations when surgeons employed "anaortic" (21.25% (17/80) vs. 1.25% (1/80), P < 0.001) but not "aortic" techniques (5% (4/80) vs. 0% (0/80), P = 0.64). Similarly, there was an increase in the selection of ideal configurations for "anaortic" (6.25% (5/80) vs. 28.75% (23/80), P < 0.001) but not "aortic" techniques (65% (52/80) vs. 61.25% (49/80), P = 0.74). Furthermore, surgeons who planned to use more than one unique "anaortic" configuration across all 5 patients increased (12.5% (2/16) vs. 87.5% (14/16), P<0.001). "COMCAB" is a promising tool to improve personalized surgical planning particularly for CABG configurations involving composite or sequential grafts which are used more frequently in anaortic operations.

Computational analysis of ventricular mechanics in hypertrophic cardiomyopathy patients

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is associated with many pathological features, such as a reduction in global longitudinal strain (GLS), myofiber disarray and hypertrophy. The effects of these features on left ventricle (LV) function are, however, not clear in two phenotypes of HCM, namely, obstructive and non-obstructive. To address this issue, we developed patient-specific computational models of the LV using clinical measurements from 2 female HCM patients and a control subject. Left ventricular mechanics was described using an active stress formulation and myofiber disarray was described using a structural tensor in the constitutive models. Unloaded LV configuration for each subject was first determined from their respective end-diastole LV geometries segmented from the cardiac magnetic resonance images, and an empirical single-beat estimation of the end-diastolic pressure volume relationship. The LV was then connected to a closed-loop circulatory model and calibrated using the clinically measured LV pressure and volume waveforms, peak GLS and blood pressure. Without consideration of myofiber disarray, peak myofiber tension was found to be lowest in the obstructive HCM subject (60 kPa), followed by the non-obstructive subject (242 kPa) and the control subject (375 kPa). With increasing myofiber disarray, we found that peak tension has to increase in the HCM models to match the clinical measurements. In the obstructive HCM patient, however, peak tension was still depressed (cf. normal subject) at the largest degree of myofiber disarray found in the clinic. The computational modeling workflow proposed here can be used in future studies with more HCM patient data.

Optimization framework for patient-specific modeling under uncertainty.

Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient-specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real-world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient-specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.

FEops HEARTguide Patient-Specific Computational Simulations for WATCHMAN FLX Left Atrial Appendage Closure: A Retrospective Study

BackgroundThree-dimensional transesophageal echocardiography (3D-TEE) is the primary imaging tool for left atrial appendage closure planning. The utility of cardiac computed tomography angiography (CCTA) and patient-specific computational models is unknown. ObjectivesThe purpose of this study was to evaluate the accuracy of the FEops HEARTguide patient-specific computational modeling in predicting appropriate device size, location, and compression of the WATCHMAN FLX compared to intraprocedural 3D-TEE. MethodsPatients with both preprocedural and postprocedural CCTA and 3D-TEE imaging of the LAA who received a WATCHMAN FLX left atrial appendage closure device were studied (n = 22). The FEops HEARTguide platform used baseline CCTA imaging to generate a prediction of device size(s), device position(s), and device dimensions. Blinded (without knowledge of implanted device size/position) and unblinded (implant device size/position disclosed) simulations were evaluated. ResultsIn 16 (72.7%) patients, the blind simulation predicted the final implanted device size. In these patients, the 3D-TEE measurements were not significantly different and had excellent correlation (Pearson correlation coefficient (r) ≥ 0.90). No patients had peridevice leak after device implant. In the 6 patients for whom the model did not predict the implanted device size, a larger device size was ultimately implanted as per operator preference. The model measurements of the unblinded patients demonstrated excellent correlation with 3D-TEE. ConclusionsThis is the first study to demonstrate that the FEops HEARTguide model accurately predicts WATCHMAN FLX device implantation characteristics. Future studies are needed to evaluate if computational modeling can improve confidence in sizing, positioning, and compression of the device without compromising technical success.

Finite element analysis to predict temperature distribution in the human neck with abnormal thyroid: A proof of concept

Background and ObjectiveHyperthyroidism, hypothyroidism, goiter and cancer are some of the dysfunctions that can occur concerning the thyroid, an important body homeostasis regulatory gland located in the cervical region. These disorders are mostly caused by changes in metabolism and can impair quality of life. This study presents a non-invasive approach that can detect changes in thyroid metabolism through the finite element analysis and medical images. The objective of this work was to develop a numerical model to represent the temperature distribution in the human neck with and without the presence of thyroid nodules. The patient-specific computational model for the case with thyroid nodules was calibrated with infrared thermography. MethodsA three-dimensional geometrical model of the neck was constructed based on the segmentation of magnetic resonance (MR) images. The Finite Element Method (FEM) was used to simulate heat diffusion and convection in the cervical region. The infrared thermography image was used to calibrate the heat transfer constants to obtain the surface temperature of the human neck model containing the enlarged thyroid with nodules. Subsequently, another case for the entire neck with an abnormally large thyroid without the nodules was simulated using the calibrated physical constants. ResultsResults of the simulations with and without the presence of thyroid nodules were compared, showing the influence of the generation of heat from the nodules, allowing observation of the thermal differences on the cervical surface and at the thyroid itself. The model with nodules presented higher skin temperature distribution in the anterior triangle region when compared to the case without nodules. An average of 0.36∘C of absolute error and 1% of relative error was obtained for the calibration between the simulated model and the infrared image. ConclusionsThis research consists of an innovative approach by comparing the results obtained via FEM simulation and the corresponding infrared image of the same neck region under study. Since there are great variability and uncertainties in the determination of the thermal constants, we applied a procedure for calibrating them based on a patient-specific case, which involves a multinodular goiter accompanied by hyperthyroidism. This proof-of-concept study allows the creation of comparative scenarios between the FEM simulations and the corresponding infrared image. Thus, it is expected that, in the future, this approach could be used to include the effect of drugs in the treatment strategies of thyroid diseases and disorders.

Rule-based definition of muscle bundles in patient-specific models of the left atrium.

Atrial fibrillation (AF) is the most common arrhythmia encountered clinically, and as the population ages, its prevalence is increasing. Although the CHA2DS2− VASc score is the most used risk-stratification system for stroke risk in AF, it lacks personalization. Patient-specific computer models of the atria can facilitate personalized risk assessment and treatment planning. However, a challenge faced in creating such models is the complexity of the atrial muscle arrangement and its influence on the atrial fiber architecture. This work proposes a semi-automated rule-based algorithm to generate the local fiber orientation in the left atrium (LA). We use the solutions of several harmonic equations to decompose the LA anatomy into subregions. Solution gradients define a two-layer fiber field in each subregion. The robustness of our approach is demonstrated by recreating the fiber orientation on nine models of the LA obtained from AF patients who underwent WATCHMAN device implantation. This cohort of patients encompasses a variety of morphology variants of the left atrium, both in terms of the left atrial appendages (LAAs) and the number of pulmonary veins (PVs). We test the fiber construction algorithm by performing electrophysiology (EP) simulations. Furthermore, this study is the first to compare its results with other rule-based algorithms for the LA fiber architecture definition available in the literature. This analysis suggests that a multi-layer fiber architecture is important to capture complex electrical activation patterns. A notable advantage of our approach is the ability to reconstruct the main LA fiber bundles in a variety of morphologies while solving for a small number of harmonic fields, leading to a comparatively straightforward and reproducible approach.

Advancements and Opportunities in Characterizing Patient-Specific Wall Shear Stress Imposed by Coronary Artery Stenting

The success of drug-eluting stents (DES) is limited by restenosis and, to a lesser extent, late stent thrombosis. Mechanical stimuli have been implicated in these outcomes, with indices of wall shear stress (WSS) determined from computational simulations being reported most frequently. The current work summarizes state-of-the-art computational approaches applicable to patient-specific models aimed at further understanding changes in WSS indexes imposed by stent implantation. We begin with a review of best practices involved in the process and then summarize the literature related to stent-induced WSS alterations. Image-based reconstruction methods are also discussed, along with the latest generation boundary conditions that replicate cardiac physiology and downstream vasculature in the setting of coronary artery disease. The influence of existing material property data on WSS results obtained with geometries reconstructed from finite element modeling and fluid structure interaction (FSI) simulations is reviewed, along with the novel approaches being used to provide coronary artery plaque data that are currently missing from the literature. We also consider the use of machine learning tools that have the potential for impact when assessing the role of adverse stent-induced WSS in suboptimal clinical outcomes. We conclude by focusing on challenging cases that involve DES implantation, which may benefit from recent advancements in patient-specific computational modeling.

Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar.

Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility.

A predictive patient-specific computational model of coronary artery bypass grafts for potential use by cardiac surgeons to guide selection of graft configurations.

Cardiac surgeons face a significant degree of uncertainty when deciding upon coronary artery bypass graft configurations for patients with coronary artery disease. This leads to significant variation in preferred configuration between different surgeons for a particular patient. Additionally, for the majority of cases, there is no consensus regarding the optimal grafting strategy. This situation results in the tendency for individual surgeons to opt for a “one size fits all” approach and use the same grafting configuration for the majority of their patients neglecting the patient-specific nature of the diseased coronary circulation. Quantitative metrics to assess the adequacy of coronary bypass graft flows have recently been advocated for routine intraoperative use by cardiac surgeons. In this work, a novel patient-specific 1D-0D computational model called “COMCAB” is developed to provide the predictive haemodynamic parameters of functional graft performance that can aid surgeons to avoid configurations with grafts that have poor flow and thus poor patency. This model has significant potential for future expanded applications.

Left Atrial Appendage Occlusion: Current Stroke Prevention Strategies and a Shift Toward Data-Driven, Patient-Specific Approaches.

The left atrial appendage (LAA) is a complex structure with unknown physiologic function protruding from the main body of the left atrium. In patients with atrial fibrillation, the left atrium does not contract effectively. Insufficient atrial and LAA contractility predisposes the LAA morphology to hemostasis and thrombus formation, leading to an increased risk of cardioembolic events. Oral anticoagulation therapies are the mainstay of stroke prevention options for patients; however, not all patients are candidates for long-term oral anticoagulation. Percutaneous occlusion devices are an attractive alternative to long-term anticoagulation therapy, although they are not without limitations, such as peri-implant leakage and device-related thrombosis. Although efforts have been made to reduce these risks, significant interpatient heterogeneity inevitably yields some degree of device-anatomy mismatch that is difficult to resolve using current devices and can ultimately lead to insufficient occlusion and poor patient outcomes. In this state-of-the-art review, we evaluated the anatomy of the LAA as well as the current pathophysiologic understanding and stroke prevention strategies used in the management of the risk of stroke associated with atrial fibrillation. We highlighted recent advances in computed tomography imaging, preprocedural planning, computational modeling, and novel additive manufacturing techniques, which represent the tools needed for a paradigm shift toward patient-centric LAA occlusion. Together, we envisage that these techniques will facilitate a pipeline from the imaging of patient anatomy to patient-specific computational and bench-top models that enable customized, data-driven approaches for LAA occlusion that are engineered specifically to meet each patient's unique needs.

Stability of unruptured intracranial aneurysms in the anterior circulation: nomogram models for risk assessment.

The probable stability of the lesion is critical in guiding treatment decisions in unruptured intracranial aneurysms (IAs). The authors aimed to develop multidimensional predictive models for the stability of unruptured IAs. Patients with unruptured IAs in the anterior circulation were prospectively enrolled and regularly followed up. Clinical data were collected, IA morphological features were assessed, and adjacent hemodynamic features were quantified with patient-specific computational fluid dynamics modeling. Based on multivariate logistic regression analyses, nomograms incorporating these factors were developed in a primary cohort (patients enrolled between January 2017 and February 2018) to predict aneurysm rupture or growth within 2 years. The predictive accuracies of the nomograms were compared with the population, hypertension, age, size, earlier rupture, and site (PHASES) and earlier subarachnoid hemorrhage, location, age, population, size, and shape (ELAPSS) scores and validated in the validation cohort (patients enrolled between March and October 2018). Among 231 patients with 272 unruptured IAs in the primary cohort, hypertension, aneurysm location, irregular shape, size ratio, normalized wall shear stress average, and relative resident time were independently related to the 2-year stability of unruptured IAs. The nomogram including clinical, morphological, and hemodynamic features (C+M+H nomogram) had the highest predictive accuracy (c-statistic 0.94), followed by the nomogram including clinical and morphological features (C+M nomogram; c-statistic 0.89), PHASES score (c-statistic 0.68), and ELAPSS score (c-statistic 0.58). Similarly, the C+M+H nomogram had the highest predictive accuracy (c-statistic 0.94) in the validation cohort (85 patients with 97 unruptured IAs). Hemodynamics have predictive values for 2-year stability of unruptured IAs treated conservatively. Multidimensional nomograms have significantly higher predictive accuracies than conventional risk prediction scores.

Designing a Novel Asymmetric Transcatheter Aortic Valve for Stenotic Bicuspid Aortic Valves Using Patient-Specific Computational Modeling.

Bicuspid aortic valve (BAV), the most common congenital heart malformation, is characterized by the presence of only two valve leaflets with asymmetrical geometry, resulting in elliptical systolic opening. BAV often leads to early onset of calcific aortic stenosis (AS). Following the rapid expansion of transcatheter aortic valve replacement (TAVR), designed specifically for treating conventional tricuspid AS, BAV patients with AS were initially treated "off-label" with TAVR, which recently gained FDA and CE regulatory approval. Despite its increasing use in BAV, pathological BAV anatomy often leads to complications stemming from mismatched anatomical features. To mitigate these complications, a novel eccentric polymeric TAVR valve incorporating asymmetrical leaflets was designed specifically for BAV anatomies. Computational modeling was used to optimize its asymmetric leaflets for lower functional stresses and improved hemodynamic performance. Deployment and flow were simulated in patient-specific BAV models (n = 6) and compared to a current commercial TAVR valve (Evolut R 29mm), to assess deployment and flow parameters. The novel eccentric BAV-dedicated valve demonstrated significant improvements in peak systolic orifice area, along with lower jet velocity and wall shear stress (WSS). This feasibility study demonstrates the clinical potential of the first known BAV-dedicated TAVR design, which will foster advancement of patient-dedicated valvular devices.