Exploiting in silico modelling to enhance translation of liver cell therapies from bench to bedside.

Cell therapies are emerging as promising treatments for a range of liver diseases but translational bottlenecks still remain including: securing and assessing the safe and effective delivery of cells to the disease site; ensuring successful cell engraftment and function; and preventing immunogenic responses. Here we highlight three therapies, each utilising a different cell type, at different stages in their clinical translation journey: transplantation of multipotent mesenchymal stromal/signalling cells, hepatocytes and macrophages. To overcome bottlenecks impeding clinical progression, we advocate for wider use of mechanistic in silico modelling approaches. We discuss how in silico approaches, alongside complementary experimental approaches, can enhance our understanding of the mechanisms underlying successful cell delivery and engraftment. Furthermore, such combined theoretical-experimental approaches can be exploited to develop novel therapies, address safety and efficacy challenges, bridge the gap between in vitro and in vivo model systems, and compensate for the inherent differences between animal model systems and humans. We also highlight how in silico model development can result in fewer and more targeted in vivo experiments, thereby reducing preclinical costs and experimental animal numbers and potentially accelerating translation to the clinic. The development of biologically-accurate in silico models that capture the mechanisms underpinning the behaviour of these complex systems must be reinforced by quantitative methods to assess cell survival post-transplant, and we argue that non-invasive in vivo imaging strategies should be routinely integrated into transplant studies.

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields.

BACKGROUND: Despite knowledge of qualitative changes that occur on ultrasound in tendinopathy, there is currently no objective and reliable means to quantify the severity or prognosis of tendinopathy on ultrasound. OBJECTIVE: The primary objective of this study is to produce a quantitative and automated means of inferring potential structural changes in tendinopathy by developing and implementing an algorithm which performs a texture based segmentation of tendon ultrasound (US) images. METHOD: A model-based segmentation approach is used which combines Gaussian mixture models, Markov random field theory and grey-level co-occurrence (GLCM) features. The algorithm is trained and tested on 49 longitudinal B-mode ultrasound images of the Achilles tendons which are labelled as tendinopathic (24) or healthy (25). Hyperparameters are tuned, using a training set of 25 images, to optimise a decision tree based classification of the images from texture class proportions. We segment and classify the remaining test images using the decision tree. RESULTS: Our approach successfully detects a difference in the texture profiles of tendinopathic and healthy tendons, with 22/24 of the test images accurately classified based on a simple texture proportion cut-off threshold. Results for the tendinopathic images are also collated to gain insight into the topology of structural changes that occur with tendinopathy. It is evident that distinct textures, which are predominantly present in tendinopathic tendons, appear most commonly near the transverse boundary of the tendon, though there was a large variability among diseased tendons. CONCLUSION: The GLCM based segmentation of tendons under ultrasound resulted in distinct segmentations between healthy and tendinopathic tendons and provides a potential tool to objectively quantify damage in tendinopathy.

Debiased ambient vibrations optical coherence elastography to profile cell, organoid and tissue mechanical properties.

The role of the mechanical environment in defining tissue function, development and growth has been shown to be fundamental. Assessment of the changes in stiffness of tissue matrices at multiple scales has relied mostly on invasive and often specialist equipment such as AFM or mechanical testing devices poorly suited to the cell culture workflow.In this paper, we have developed a unbiased passive optical coherence elastography method, exploiting ambient vibrations in the sample that enables real-time noninvasive quantitative profiling of cells and tissues. We demonstrate a robust method that decouples optical scattering and mechanical properties by actively compensating for scattering associated noise bias and reducing variance. The efficiency for the method to retrieve ground truth is validated in silico and in vitro, and exemplified for key applications such as time course mechanical profiling of bone and cartilage spheroids, tissue engineering cancer models, tissue repair models and single cell. Our method is readily implementable with any commercial optical coherence tomography system without any hardware modifications, and thus offers a breakthrough in on-line tissue mechanical assessment of spatial mechanical properties for organoids, soft tissues and tissue engineering.

Curvature in Biological Systems: Its Quantification, Emergence, and Implications across the Scales.

Surface curvature both emerges from, and influences the behavior of, living objects at length scales ranging from cell membranes to single cells to tissues and organs. The relevance of surface curvature in biology is supported by numerous experimental and theoretical investigations in recent years. In this review, first, a brief introduction to the key ideas of surface curvature in the context of biological systems is given and the challenges that arise when measuring surface curvature are discussed. Giving an overview of the emergence of curvature in biological systems, its significance at different length scales becomes apparent. On the other hand, summarizing current findings also shows that both single cells and entire cell sheets, tissues or organisms respond to curvature by modulating their shape and their migration behavior. Finally, the interplay between the distribution of morphogens or micro-organisms and the emergence of curvature across length scales is addressed with examples demonstrating these key mechanistic principles of morphogenesis. Overall, this review highlights that curved interfaces are not merely a passive by-product of the chemical, biological, and mechanical processes but that curvature acts also as a signal that co-determines these processes.

A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis.

Immune checkpoint inhibitors (ICIs), as a novel immunotherapy, are designed to modulate the immune system to attack malignancies. Despite their promising benefits, immune-related adverse events (IRAEs) may occur, and incidences are bound to increase with surging demand of this class of drugs in treating cancer. Myocarditis, although rare compared to other IRAEs, has a significantly higher fatal frequency. Due to the overwhelming complexity of the immune system, this condition is not well understood, despite the significant research efforts devoted to it. To better understand the development and progression of autoimmune myocarditis and the roles of ICIs therein, we suggest a new approach: mathematical modelling. Mathematical modelling of myocarditis has enormous potential to determine which parts of the immune system are critical to the development and progression of the disease, and therefore warrant further investigation. We provide the immunological background needed to develop a mathematical model of this disease and review relevant existing models of immunology that serve as the mathematical inspiration needed to develop this field.

Assess and address: Screening and management of depression in patients with chronic heart failure.

BACKGROUND: About one in three patients with heart failure (HF) have depression. Comorbid HF and depression are associated with poor outcomes and increased health care burden. Clinical guidelines recommend routine depression screening in patients with HF. LOCAL PROBLEM: Depression screening was not being systematically implemented in an outpatient cardiology clinic. METHODS: To create a sustainable process for a cardiology clinic to screen adults with chronic HF for depression, identify patients who have an elevated depression screening score and initiate an evidence-based treatment algorithm for patients with depressive symptoms. INTERVENTION: A nurse practitioner (NP)-led process improvement project administered the Patient Health Questionnaire (PHQ-9) tool to patients with HF. The score was reviewed by the NP and, if elevated, addressed with assessment and plan. Compliance was measured by the percentage of patients screened. Clinical impact was measured by percentage of patients with an elevated score with a documented treatment plan. RESULTS: Postimplementation results for four Plan-Do-Study-Act cycles were 38%, 68%, 72%, and 66%, respectively, with a total 63% of patients screened during the entire project. Twenty unique patients (13.2%) had elevated PHQ-9 scores; all had a documented treatment plan. CONCLUSIONS: We demonstrated how a screening protocol and an accompanying treatment algorithm can be successfully implemented in an outpatient cardiology clinic. Elements of success included a standardized screening protocol, a clinical support algorithm for treatment/referral, an optimized electronic medical record, and a follow-up system for patients with significant depressive symptoms. Stakeholder engagement throughout the project informed iterative changes and provided direction for sustainability.

Evaluation of a scalable online videogame-based biofeedback program to improve emotion regulation: A descriptive study assessing parent perspectives.

Individuals and families increasingly turn to e-mental health apps for education, diagnosis, and treatment of mental health disorders and to promote mental wellness. These apps provide significant increases in convenience from existing services, since they can augment or replace services with on-demand access within the home. This raises important questions about self-selection of interventions. Who uses these applications? How do individuals perceive their own progress within applications? This study is a retrospective data analysis-based evaluation of a commercially available e-mental health program that includes biofeedback video games that help children build emotion regulation skills by demonstrating and prompting children to practice bodily focused emotion regulation techniques. The e-mental health program also provided parent psychoeducation-focused coaching at the time of the evaluation. Data collection instruments used to inform the retrospective study included parent intake surveys, gameplay engagement data, and notes from parent coaching calls. The evaluation revealed families presenting for common symptoms associated with emotion regulation deficits, as opposed to a wellness cohort looking for additional support. Families near-universally activated and engaged with the intervention, willing to carry out an extended "dose" of the e-mental health program in their home. Parents self-reported their perceptions of their children's emotion regulation progress, primarily in terms of children's increased use of emotion regulation skills, improved emotion awareness and communication, calmer demeanor, greater confidence, and improved relationships. More work is needed to understand the corresponding clinical progress from this in-home training, as well as its implications for how emotion regulation skills grow.

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors.

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

Fluid mechanical modeling of the upper urinary tract.

The upper urinary tract (UUT) consists of kidneys and ureters, and is an integral part of the human urogenital system. Yet malfunctioning and complications of the UUT can happen at all stages of life, attributed to reasons such as congenital anomalies, urinary tract infections, urolithiasis and urothelial cancers, all of which require urological interventions and significantly compromise patients' quality of life. Therefore, many models have been developed to address the relevant scientific and clinical challenges of the UUT. Of all approaches, fluid mechanical modeling serves a pivotal role and various methods have been employed to develop physiologically meaningful models. In this article, we provide an overview on the historical evolution of fluid mechanical models of UUT that utilize theoretical, computational, and experimental approaches. Descriptions of the physiological functionality of each component are also given and the mechanical characterizations associated with the UUT are provided. As such, it is our aim to offer a brief summary of the current knowledge of the subject, and provide a comprehensive introduction for engineers, scientists, and clinicians who are interested in the field of fluid mechanical modeling of UUT. This article is categorized under: Cancer > Biomedical Engineering Infectious Diseases > Biomedical Engineering Reproductive System Diseases > Biomedical Engineering.

Blood-spinal cord barrier leakage is independent of motor neuron pathology in ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving progressive degeneration of upper and lower motor neurons. The pattern of lower motor neuron loss along the spinal cord follows the pattern of deposition of phosphorylated TDP-43 aggregates. The blood-spinal cord barrier (BSCB) restricts entry into the spinal cord parenchyma of blood components that can promote motor neuron degeneration, but in ALS there is evidence for barrier breakdown. Here we sought to quantify BSCB breakdown along the spinal cord axis, to determine whether BSCB breakdown displays the same patterning as motor neuron loss and TDP-43 proteinopathy. Cerebrospinal fluid hemoglobin was measured in living ALS patients (n = 87 control, n = 236 ALS) as a potential biomarker of BSCB and blood-brain barrier leakage. Cervical, thoracic, and lumbar post-mortem spinal cord tissue (n = 5 control, n = 13 ALS) were then immunolabelled and semi-automated imaging and analysis performed to quantify hemoglobin leakage, lower motor neuron loss, and phosphorylated TDP-43 inclusion load. Hemoglobin leakage was observed along the whole ALS spinal cord axis and was most severe in the dorsal gray and white matter in the thoracic spinal cord. In contrast, motor neuron loss and TDP-43 proteinopathy were seen at all three levels of the ALS spinal cord, with most abundant TDP-43 deposition in the anterior gray matter of the cervical and lumbar cord. Our data show that leakage of the BSCB occurs during life, but at end-stage disease the regions with most severe BSCB damage are not those where TDP-43 accumulation is most abundant. This suggests BSCB leakage and TDP-43 pathology are independent pathologies in ALS.

A Systematically Reduced Mathematical Model for Organoid Expansion.

Organoids are three-dimensional multicellular tissue constructs. When cultured in vitro, they recapitulate the structure, heterogeneity, and function of their in vivo counterparts. As awareness of the multiple uses of organoids has grown, e.g. in drug discovery and personalised medicine, demand has increased for low-cost and efficient methods of producing them in a reproducible manner and at scale. Here we focus on a bioreactor technology for organoid production, which exploits fluid flow to enhance mass transport to and from the organoids. To ensure large numbers of organoids can be grown within the bioreactor in a reproducible manner, nutrient delivery to, and waste product removal from, the organoids must be carefully controlled. We develop a continuum mathematical model to investigate how mass transport within the bioreactor depends on the inlet flow rate and cell seeding density, focusing on the transport of two key metabolites: glucose and lactate. We exploit the thin geometry of the bioreactor to systematically simplify our model. This significantly reduces the computational cost of generating model solutions, and provides insight into the dominant mass transport mechanisms. We test the validity of the reduced models by comparison with simulations of the full model. We then exploit our reduced mathematical model to determine, for a given inlet flow rate and cell seeding density, the evolution of the spatial metabolite distributions throughout the bioreactor. To assess the bioreactor transport characteristics, we introduce metrics quantifying glucose conversion (the ratio between the total amounts of consumed and supplied glucose), the maximum lactate concentration, the proportion of the bioreactor with intolerable lactate concentrations, and the time when intolerable lactate concentrations are first experienced within the bioreactor. We determine the dependence of these metrics on organoid-line characteristics such as proliferation rate and rate of glucose consumption per cell. Finally, for a given organoid line, we determine how the distribution of metabolites and the associated metrics depend on the inlet flow rate. Insights from this study can be used to inform bioreactor operating conditions, ultimately improving the quality and number of bioreactor-expanded organoids.

The Interplay Between Beta-Amyloid 1-42 (Aß1-42)-Induced Hippocampal Inflammatory Response, p-tau, Vascular Pathology, and Their Synergistic Contributions to Neuronal Death and Behavioral Deficits.

Alzheimer's disease (AD), the most common chronic neurodegenerative disorder, has complex neuropathology. The principal neuropathological hallmarks of the disease are the deposition of extracellular ß-amyloid (Aß) plaques and neurofibrillary tangles (NFTs) comprised of hyperphosphorylated tau (p-tau) protein. These changes occur with neuroinflammation, a compromised blood-brain barrier (BBB) integrity, and neuronal synaptic dysfunction, all of which ultimately lead to neuronal cell loss and cognitive deficits in AD. Aß1-42 was stereotaxically administered bilaterally into the CA1 region of the hippocampi of 18-month-old male C57BL/6 mice. This study aimed to characterize, utilizing immunohistochemistry and behavioral testing, the spatial and temporal effects of Aß1-42 on a broad set of parameters characteristic of AD: p-tau, neuroinflammation, vascular pathology, pyramidal cell survival, and behavior. Three days after Aß1-42 injection and before significant neuronal cell loss was detected, acute neuroinflammatory and vascular responses were observed. These responses included the up-regulation of glial fibrillary acidic protein (GFAP), cell adhesion molecule-1 (PECAM-1, also known as CD31), fibrinogen labeling, and an increased number of activated astrocytes and microglia in the CA1 region of the hippocampus. From day 7, there was significant pyramidal cell loss in the CA1 region of the hippocampus, and by 30 days, significant localized up-regulation of p-tau, GFAP, Iba-1, CD31, and alpha-smooth muscle actin (α-SMA) in the Aß1-42-injected mice compared with controls. These molecular changes in Aß1-42-injected mice were accompanied by cognitive deterioration, as demonstrated by long-term spatial memory impairment. This study is reporting a comprehensive examination of a complex set of parameters associated with intrahippocampal administration of Aß1-42 in mice, their spatiotemporal interactions and combined contribution to the disease progression. We show that a single Aß injection can reproduce aspects of the inflammatory, vascular, and p-tau induced pathology occurring in the AD human brain that lead to cognitive deficits.

Combining multiple spatial statistics enhances the description of immune cell localisation within tumours.

Digital pathology enables computational analysis algorithms to be applied at scale to histological images. An example is the identification of immune cells within solid tumours. Image analysis algorithms can extract precise cell locations from immunohistochemistry slides, but the resulting spatial coordinates, or point patterns, can be difficult to interpret. Since localisation of immune cells within tumours may reflect their functional status and correlates with patient prognosis, novel descriptors of their spatial distributions are of biological and clinical interest. A range of spatial statistics have been used to analyse such point patterns but, individually, these approaches only partially describe complex immune cell distributions. In this study, we apply three spatial statistics to locations of CD68+ macrophages within human head and neck tumours, and show that images grouped semi-quantitatively by a pathologist share similar statistics. We generate a synthetic dataset which emulates human samples and use it to demonstrate that combining multiple spatial statistics with a maximum likelihood approach better predicts human classifications than any single statistic. We can also estimate the error associated with our classifications. Importantly, this methodology is adaptable and can be extended to other histological investigations or applied to point patterns outside of histology.

Mathematical modelling reveals cellular dynamics within tumour spheroids.

Tumour spheroids are widely used as an in vitro assay for characterising the dynamics and response to treatment of different cancer cell lines. Their popularity is largely due to the reproducible manner in which spheroids grow: the diffusion of nutrients and oxygen from the surrounding culture medium, and their consumption by tumour cells, causes proliferation to be localised at the spheroid boundary. As the spheroid grows, cells at the spheroid centre may become hypoxic and die, forming a necrotic core. The pressure created by the localisation of tumour cell proliferation and death generates an cellular flow of tumour cells from the spheroid rim towards its core. Experiments by Dorie et al. showed that this flow causes inert microspheres to infiltrate into tumour spheroids via advection from the spheroid surface, by adding microbeads to the surface of tumour spheroids and observing the distribution over time. We use an off-lattice hybrid agent-based model to re-assess these experiments and establish the extent to which the spatio-temporal data generated by microspheres can be used to infer kinetic parameters associated with the tumour spheroids that they infiltrate. Variation in these parameters, such as the rate of tumour cell proliferation or sensitivity to hypoxia, can produce spheroids with similar bulk growth dynamics but differing internal compositions (the proportion of the tumour which is proliferating, hypoxic/quiescent and necrotic/nutrient-deficient). We use this model to show that the types of experiment conducted by Dorie et al. could be used to infer spheroid composition and parameters associated with tumour cell lines such as their sensitivity to hypoxia or average rate of proliferation, and note that these observations cannot be conducted within previous continuum models of microbead infiltration into tumour spheroids as they rely on resolving the trajectories of individual microbeads.

Predicting Bone Formation in Mesenchymal Stromal Cell-Seeded Hydrogels Using Experiment-Based Mathematical Modeling.

In vitro bone formation by mesenchymal stromal cells encapsulated in type-1 collagen hydrogels is demonstrated after a 28-day in vitro culture period. Analysis of the hydrogels is carried out by X-ray microcomputed tomography, histology, and immunohistochemistry, which collectively demonstrates that bone formation in the hydrogels was quantifiably proportional to the initial collagen concentration, and subsequently the population density of seeded cells. This was established by varying the initial collagen concentration at a constant cell seeding density (3 × 105 cells/0.3 mL hydrogel), and separately varying cell seeding density at a constant collagen concentration (1 mg/mL). Using these data, a mathematical model is presented for the total hydrogel volume and mineralization volume based on the observed linear contraction dynamics of cell-seeded collagen gels. The model parameters are fitted by comparing the predictions of the mathematical model for the hydrogel and mineralized volumes on day 28 with the experimental data. The model is then used to predict the hydrogel and mineralization volumes for a range of hydrogel collagen concentrations and cell seeding densities, providing comprehensive input/output descriptors for generating mineralized hydrogels for bone tissue engineering. It is proposed that this quantitative approach will be a useful tool for generating in vitro manufactured bone tissue, defining input parameters that yield predictable output measures of tissue maturation. Impact statement This article describes a simple yet powerful quantitative description of in vitro tissue-engineered bone by combining experimental data with mathematical modeling. The overall aim of the article is to examine what is currently known about cell-mediated collagen contraction, and demonstrate that this phenomenon can be exploited to tailor bone formation by choosing a specific set of input parameters in the form of cell seeding density and collagen hydrogel concentration. Our study utilizes a clinically relevant cell source (human mesenchymal stem cells) with a biomaterial that has received regulatory approval for use in humans (collagen type 1), and hence could be useful for clinical applications, as well as furthering our understanding of cell/extracellular matrix interactions in determining in vitro bone tissue formation.

Identifying chondrogenesis strategies for tissue engineering of articular cartilage.

A key step in the tissue engineering of articular cartilage is the chondrogenic differentiation of mesenchymal stem cells (MSCs) into chondrocytes (native cartilage cells). Chondrogenesis is regulated by transforming growth factor-ß (TGF-ß), a short-lived cytokine whose effect is prolonged by storage in the extracellular matrix. Tissue engineering applications aim to maximise the yield of differentiated MSCs. Recent experiments involve seeding a hydrogel construct with a layer of MSCs lying below a layer of chondrocytes, stimulating the seeded cells in the construct from above with exogenous TGF-ß and then culturing it in vitro. To investigate the efficacy of this strategy, we develop a mathematical model to describe the interactions between MSCs, chondrocytes and TGF-ß. Using this model, we investigate the effect of varying the initial concentration of TGF-ß, the initial densities of the MSCs and chondrocytes, and the relative depths of the two layers on the long-time composition of the tissue construct.

Lattice and continuum modelling of a bioactive porous tissue scaffold.

A contemporary procedure to grow artificial tissue is to seed cells onto a porous biomaterial scaffold and culture it within a perfusion bioreactor to facilitate the transport of nutrients to growing cells. Typical models of cell growth for tissue engineering applications make use of spatially homogeneous or spatially continuous equations to model cell growth, flow of culture medium, nutrient transport and their interactions. The network structure of the physical porous scaffold is often incorporated through parameters in these models, either phenomenologically or through techniques like mathematical homogenization. We derive a model on a square grid lattice to demonstrate the importance of explicitly modelling the network structure of the porous scaffold and compare results from this model with those from a modified continuum model from the literature. We capture two-way coupling between cell growth and fluid flow by allowing cells to block pores, and by allowing the shear stress of the fluid to affect cell growth and death. We explore a range of parameters for both models and demonstrate quantitative and qualitative differences between predictions from each of these approaches, including spatial pattern formation and local oscillations in cell density present only in the lattice model. These differences suggest that for some parameter regimes, corresponding to specific cell types and scaffold geometries, the lattice model gives qualitatively different model predictions than typical continuum models. Our results inform model selection for bioactive porous tissue scaffolds, aiding in the development of successful tissue engineering experiments and eventually clinically successful technologies.

The Fluid Mechanics of Ureteroscope Irrigation.

PURPOSE: To develop a physical understanding of ureterorenoscopy irrigation, we derive mathematical models from basic physical principles and compare these predictions with the results of benchtop experiments. Mathematical modeling can be used to understand the role of inlet pressure, tip deflection, the presence of working tools, geometric properties of the instruments used, and material properties of the irrigation fluid on resulting flow rate. MATERIALS AND METHODS: We develop theoretical models to describe irrigation flow in an idealized setup and compare with benchtop experiments for flow through a straight scope, a scope with a deflected tip, and a scope with a working tool inserted. The benchtop experiments were performed using Boston Scientific LithoVue ureteroscope and a variety of Boston Scientific working tools. Standard ureteroscope working channels have circular cross sections, but using theoretical models we investigate whether modifications to the cross-sectional geometry can enhance flow rates. RESULTS: The theoretical flow predictions are confirmed by experimental results. Tip deflection is shown to have a negligible effect on flow rate, but the presence of working tools decreases flow significantly (for a fixed driving pressure). Flow rate is predicted to improve when tools are placed at the edge of the channel, rather than the center, and modifying the cross-sectional shape from a circle to an ellipse can further increase flow rate. CONCLUSIONS: A mathematical framework is formulated and shown to accurately predict the properties of ureteroscope irrigation flow. The theoretical approach has significant potential in quantifying irrigation flow and improving ureteroscope design.

International External Validation Study of the 2014 European Society of Cardiology Guidelines on Sudden Cardiac Death Prevention in Hypertrophic Cardiomyopathy (EVIDENCE-HCM).

BACKGROUND: Identification of people with hypertrophic cardiomyopathy (HCM) who are at risk of sudden cardiac death (SCD) and require a prophylactic implantable cardioverter defibrillator is challenging. In 2014, the European Society of Cardiology proposed a new risk stratification method based on a risk prediction model (HCM Risk-SCD) that estimates the 5-year risk of SCD. The aim was to externally validate the 2014 European Society of Cardiology recommendations in a geographically diverse cohort of patients recruited from the United States, Europe, the Middle East, and Asia. METHODS: This was an observational, retrospective, longitudinal cohort study. RESULTS: The cohort consisted of 3703 patients. Seventy three (2%) patients reached the SCD end point within 5 years of follow-up (5-year incidence, 2.4% [95% confidence interval {CI}, 1.9-3.0]). The validation study revealed a calibration slope of 1.02 (95% CI, 0.93-1.12), C-index of 0.70 (95% CI, 0.68-0.72), and D-statistic of 1.17 (95% CI, 1.05-1.29). In a complete case analysis (n= 2147; 44 SCD end points at 5 years), patients with a predicted 5-year risk of <4% (n=1524; 71%) had an observed 5-year SCD incidence of 1.4% (95% CI, 0.8-2.2); patients with a predicted risk of ≥6% (n=297; 14%) had an observed SCD incidence of 8.9% (95% CI, 5.96-13.1) at 5 years. For every 13 (297/23) implantable cardioverter defibrillator implantations in patients with an estimated 5-year SCD risk ≥6%, 1 patient can potentially be saved from SCD. CONCLUSIONS: This study confirms that the HCM Risk-SCD model provides accurate prognostic information that can be used to target implantable cardioverter defibrillator therapy in patients at the highest risk of SCD.