The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

/pdf/computational-models-in-cardiology-4legs32ugi.pdf

Computational models in cardiology

Background Quantitative flow ratio (QFR) is a novel modality for physiological lesion assessment based on 3‐dimensional vessel reconstructions and contrast flow velocity estimates. We evaluated the...

Diagnostic Performance of In‐Procedure Angiography‐Derived Quantitative Flow Reserve Compared to Pressure‐Derived Fractional Flow Reserve: The FAVOR II Europe‐Japan Study

Molecular communication (MC) and molecular network (MN) are communication paradigms that use biochemical signaling to achieve information exchange among naturally and artificially synthesized nanosystems. Among the envisaged application areas of MC and MN is the field of nanomedicine, where the subject of targeted drug delivery (TDD) is at the forefront. Typically, when someone gets sick, therapeutic drugs are administered to the person for healing purposes. Since no therapeutic drug can be effective until it is delivered to the target site in the body, different modalities to improve the delivery of drugs to the targeted sites are being explored in contemporary research. The most promising of these modalities is TDD. TDD modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. Research in TDD has been going on for many years in the field of medical science; however, the translation of expectations and promises to clinical reality has not been satisfactorily achieved because of several challenges. The exploration of TDD ideas under the MC and MN paradigms is considered as an option to addressing these challenges and to facilitate the translation of TDD from the bench to the patients’ bedsides. Over the past decade, there have been some research efforts made in exploring the ideas of TDD on the MC and MN platforms. While the number of research output in terms of scientific articles is few at the moment, the desire in the scientific community to participate in realizing the goal of TDD is quite high as is evidence from the rise in research output over the last few years. To increase awareness and provide the multidisciplinary research community with the necessary background information on TDD, this paper presents a visionary survey of this subject within the domain of MC and MN. We start by introducing in an elaborate manner, the motivation behind the application of MC and MN paradigms to the study and implementation of TDD. Specifically, an explanation on how MC-based TDD concepts differ from traditional TDD being explored under the field of medical science is provided. We also summarize the taxonomy of the different perspectives through which MC-based TDD research can be viewed. System models and design challenges/requirements for developing MC-based TDD are discussed. Various metrics that can be used to evaluate the performance of MC-based TDD systems are highlighted. We also provide a discussion on the envisaged path from contemporary research activities to clinical implementation of the MC-based TDD. Finally, we discuss issues such as informatics and software tools, as well as issues that border on the requirement for standards and regulatory policies in MC-based TDD research and practice.

Molecular Communication and Nanonetwork for Targeted Drug Delivery: A Survey

Background: Measuring fractional flow reserve (FFR) with a pressure wire remains underutilized because of the invasiveness of guide wire placement or the need for a hyperemic stimulus FFR derived 

Accuracy of Fractional Flow Reserve Derived From Coronary Angiography

Patient-specific computer models have been developed representing a variety of aspects of the cardiovascular system spanning the disciplines of electrophysiology, electromechanics, solid mechanics, and fluid dynamics. These physiological mechanistic models predict macroscopic phenomena such as electrical impulse propagation and contraction throughout the entire heart as well as flow and pressure dynamics occurring in the ventricular chambers, aorta, and coronary arteries during each heartbeat. Such models have been used to study a variety of clinical scenarios including aortic aneurysms, coronary stenosis, cardiac valvular disease, left ventricular assist devices, cardiac resynchronization therapy, ablation therapy, and risk stratification. After decades of research, these models are beginning to be incorporated into clinical practice directly via marketed devices and indirectly by improving our understanding of the underlying mechanisms of health and disease within a clinical context.

/pdf/patient-specific-cardiovascular-computational-modeling-1je0s3m4wu.pdf

Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges.

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.

https://heart.bmj.com/content/heartjnl/102/1/18.full.pdf

Computational fluid dynamics modelling in cardiovascular medicine

Objectives This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient’s physiological response to stenting. Background Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated with improved clinical and economic outcomes compared with angiographic guidance alone. Virtual (v)FFR can be calculated based upon a 3-dimensional (3D) reconstruction of the coronary anatomy from the angiogram, using computational fluid dynamics (CFD) modeling. This technology can be used to perform virtual stenting, with a predicted post-PCI FFR, and the prospect of optimized treatment planning. Methods Patients undergoing elective PCI had pressure-wire–based FFR measurements pre- and post-PCI. A 3D reconstruction of the diseased artery was generated from the angiogram and imported into the VIRTUheart workflow, without the need for any invasive physiological measurements. VCI was performed using a radius correction tool replicating the dimensions of the stent deployed during PCI. Virtual FFR (vFFR) was calculated pre- and post-VCI, using CFD analysis. vFFR pre- and post-VCI were compared with measured (m)FFR pre- and post-PCI, respectively. Results Fifty-four patients and 59 vessels underwent PCI. The mFFR and vFFR pre-PCI were 0.66 ± 0.14 and 0.68 ± 0.13, respectively. Pre-PCI vFFR deviated from mFFR by ±0.05 (mean Δ = −0.02; SD = 0.07). The mean mFFR and vFFR post-PCI/VCI were 0.90 ± 0.05 and 0.92 ± 0.05, respectively. Post-VCI vFFR deviated from post-PCI mFFR by ±0.02 (mean Δ = −0.01; SD = 0.03). Mean CFD processing time was 95 s per case. Conclusions The authors have developed a novel VCI tool, based upon the angiogram, that predicts the physiological response to stenting with a high degree of accuracy.

Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram

Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis: Results From the VIRTU-Fast Study

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Computational fluid dynamics modelling in cardiovascular medicine

Virtual Coronary Intervention: A Treatment Planning Tool Based Upon the Angiogram

Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis: Results From the VIRTU-Fast Study