A systematic review of cardiac in-silico clinical trials.

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways.

Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.

Segmental outflow dynamics in the trabecular meshwork of living mice.

Aqueous humour does not drain uniformly through the trabecular meshwork (TM), but rather follows non-uniform or "segmental" routes. In this study, we examined whether segmental outflow patterns in the TM change over time in living mice and whether such changes are affected by age. Segmental outflow patterns were labelled by constant-pressure infusion of fluorescent tracer microparticles into the anterior chamber of anesthetised C57BL/6J mice at 3 or 8 months of age. Two different tracer colours were infused at separate time points with an interval of Δt = 0, 2, 7 or 14 days. In a separate experiment, one tracer was infused in vivo while the second tracer was infused ex vivo after 2 days. The spatial relationship between the two tracer patterns was analysed using the Pearson's correlation coefficient, r. In 3-month-old mice, there was a time-dependent decay in r, which was near unity at Δt = 0 and near zero at Δt = 14 days. In 8-month-old mice, r remained elevated for 14 days. Segmental outflow patterns measured in young mice ex vivo were not significantly different from those measured in vivo after accounting for the expected changes over 2 days. Therefore, segmental outflow patterns are not static in the TM but redistribute over time, achieving near complete loss of correlation by 2 weeks in young healthy mice. There is an age-related decline in the rate at which segmental outflow patterns redistribute in the TM. Further research is needed to understand the dynamic factors controlling segmental outflow.