Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Cardiovascular Engineering and Technology: Credibility Assessment of the Patient-Specific Modeling of the Aneurysmal Ascending Thoracic Aorta: Verification, Validation and Uncertainty Quantification
Roberta Scuoppo et al.
Abstract
Computational modeling holds promise in predicting patient-specific outcomes and guiding clinical decision-making. The patient-specific model forming the basis of a digital twin can be considered biomedical software, thereby necessitating trust in its predictive accuracy.
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APL bioengineering: Credibility assessment of patient-specific modeling in transcatheter aortic valve implantationâPart 2: Uncertainty quantification and sensitivity analysis
Roberta Scuoppo et al.
Abstract
Transcatheter aortic valve implantation (TAVI) benefits from patient-specific computational modeling, yet model credibility remains a challenge. The ASME V&V40 standard provides a framework for assessing uncertainty and sensitivity in in silico predictions, ensuring reliability in clinical decision-making. This study evaluates uncertainty quantification (UQ) and sensitivity analysis of a patient-specific TAVI model using the ASME V&V40 standard to enhance model credibility. Four patient-specific TAVI models with 23 and 26âmm SAPIEN 3 Ultra (S3) devices were developed using finite-element simulations for deployment and fluidâstructure interaction analysis for hemodynamic analysis. Uncertain parameters included anatomical features, material properties, hemodynamic conditions, and procedural variables. A surrogate model was constructed with Gaussian-process regression, and probabilistic assessment was conducted via quasi-Monte Carlo analysis. Sensitivity analysis identified key parameters influencing model outputs. The surrogate model accurately predicted device diameter (mean relative error <1%), with balloon expansion volume and stent-frame material properties being the most influential. Hemodynamic predictions exhibited greater uncertainty, with effective orifice area and pressure gradient showing deviations beyond the 5% validation threshold. This study establishes a framework for UQ in patient-specific TAVI modeling, demonstrating reliable device deployment predictions. The findings support integrating in silico models into clinical decision-making, benefiting clinicians, manufacturers, and regulatory bodies. This study is complemented by a first part dedicated to the discrete validation of the patient-specific TAVI model against clinical data.
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The Lancet Regional Health â Europe: Recommended methodologies for clinical investigations of high-risk medical devicesâConclusions from the European Union COREâMD Project
Alan G. Fraser et al.
Abstract
Before a high-risk medical device is approved for implantation into patients, there should be evidence not only of its performance and safety with a favourable benefit-risk ratio, but also of its clinical efficacy. Regulatory guidance on study methodologies is lacking, however, so the European Commission funded the COREâMD project (Coordinating Research and Evidence for Medical Devices) to advise regulators on appropriate designs for clinical trials of high-risk devices. The COREâMD consortium recommends that evaluation should be planned in four stages. Randomised controlled trials should be performed more often, against active comparators reflecting the best available treatment, or using sham interventions with ethical safeguards. Large trials can be managed efficiently using an electronic database or registry. Non-randomised clinical studies can apply objective performance criteria or other validated patient-relevant outcome measures, with adjustments to minimise bias. Full transparency of results from clinical investigations is essential. Proportionate regulation of breakthrough or orphan devices for independently-defined serious unmet needs may involve approval with less evidence, but on condition of subsequent confirmatory studies. These COREâMD consensus proposals have been submitted to European Union medical device regulators, to be considered as a basis for more transparent and predictable requirements for clinical evidence.
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eClinicalMedicine: Shared decision making using digital twins in knee osteoarthritis care: a randomized clinical trial of an AI-enabled decision aid versus education alone on decision quality, physical function, and user experience
Prakash Jayakumar et al.
Abstract
Patient decision aids (DAs) improve decision quality during shared decision-making (SDM) for patients seeking care for knee osteoarthritis (OA). However, few DAs incorporate the âdigital twinâ concept where comprehensive data are applied to computational models to generate dynamic virtual simulations and predictions to augment decision-making in real-time. We developed an artificial intelligence-enabled DA (AI-DA) that generated digital twins using patient reported outcome measurements (PROMs) and clinical data to enhance SDM by providing personalized predictions of risks and benefits for patients with knee OA considering total knee arthroplasty (TKA). We assessed the impact of the AI-DA on patient- and process-level outcomes.
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BMC Medicine: Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury
Louis P. Lukas et al.
Abstract
Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.
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Springer Nature - Biomechanics and Modeling in Mechanobiology:Assessing calcification effects in TEVAR procedures: a computational analysis
Giuia De Campo et al.
Abstract
Thoracic endovascular aortic repair (TEVAR) procedure is sometimes discouraged from clinical guidelines in the presence of calcifications and thrombus along the sealing zones. This computational study aims to understand which is the effect of calcification on stent graft displacement after TEVAR procedure, simulated in a patient-specific anatomy with a penetrating aortic ulcer (PAU).
A patient-specific anatomy without calcification is taken as reference, and four models with idealized calcifications positioned in different regions and with different material properties are analyzed. Opening area, von Mises stresses and contact pressures are evaluated to provide a reliable comparison between the calcified (Ca) and the non-calcified models (noCa), and among the calcified models themselves.
Comparing qualitatively the Ca and noCa models, no particular changes in the stent graft apposition are observed. In addition, in the Ca models the opening area results lower with respect to the noCa models, but no significant differences are observed among the Ca models. Regarding the von Mises stresses, it seems that the calcifications act as load-bearing structures, absorbing the stresses and reducing them on the aorta. Decreasing the Young modulus of the calcifications, this effect is reduced. Higher contact pressures are observed when the highest Youngâs modulus of calcification is adopted, with all Ca models having greater pressures than the noCa model.
From this analysis, the stent graft seems to be positioned correctly inside the aorta, even in the presence of calcifications. In this setting, the calcifications seem to reduce the stresses on the aorta, thus reducing the likelihood of aneurysm rupture.
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Springer Nature - Physical and Engineering Sciences in Medicine: Clinical image analysis to build patient-specific models of acute ischemic stroke patients
Virginia Fregona et al.
Abstract
Mechanical thrombectomy (MT) is an emergency treatment for acute ischemic stroke (AIS) to remove a clot occluding a large cerebral vessel. Histological analysis on retrieved thrombi have shown that they are mainly composed of red blood cells (RBCs), platelets and fibrin, and the outcome of MT appears to be influenced by clot composition. Therefore, being able to predict clot composition from routine medical images used for AIS diagnosis could support the choice of interventional strategy. Along with that, finite element simulations of the MT procedure can help provide insights into the impact of the procedural choices, the vessels morphology and the clot characteristics on the MT outcome. To achieve this, a realistic representation of the involved structures is necessary. In this context, this work aimed to (i) develop a methodology for the analysis of routine radiological images aiming at inferring information about clot characteristics (position, length, and composition) and (ii) develop a semi-automatic pipeline to position the clot in the patient-specific reconstructed geometry to build a patient-specific model which could be the starting point for the in silico replica of the MT procedure. However, image analysis alone could not distinguish between white and mixed clots, while a distinction between red and non-red clots was possible. Consequently, histological analyses were used to assign the clot composition, and thus the mechanical properties, in the positioning simulation. The resulting patient-specific model showed a strong similarity with pre-interventional clinical images.
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CPT: Pharmacometrics & Systems Pharmacology: A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models
Javiera CortĂ©s-RĂos et al.
Abstract
In silico clinical trials (ISCT) are computational frameworks that employ mathematical models to generate virtual patients and simulate their responses to new treatments, treatment regimens, or medical devices via simulations mirroring real-world clinical trials. ISCTs are an important component of the model-informed drug development (MIDD) framework for optimizing therapies, treatment personalization, informing regulatory decisions, and accelerating overall drug development by enhancing R&D productivity. However, the emergence of complex models, such as quantitative systems pharmacology (QSP) models, presents significant challenges for their effective implementation. Guidelines for conducting ISCTs have been published to address these challenges, focusing on algorithms and credibility frameworks for generating plausible virtual patients and calibrating virtual populations. However, it is not straightforward to apply existing workflows to models where parameter distributions and correlations are estimated using nonlinear mixed effects (NLME) population fitting approaches, a common practice in the pharmaceutical industry when individual-patient-level data is available. Here, we illustrate a modeling workflow for conducting ISCTs with NLME models, detailing key considerations, methods, and challenges at each step. We demonstrate the practical implementation of this workflow through two examples to showcase its broad applicability: (1) a simple model predicting tumor growth in response to chemotherapy and (2) a more complex mechanistic QSP model of hepatitis B virus infection that captures the physiological mechanisms underlying treatment response with standard-of-care therapies.
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