Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Long-term prognostic value of quantitative-flow-ratio-concordant revascularization in stable coronary artery disease.

OBJECTIVES: Confirming the prognostic value of global QFR and evaluating the long-term prognosis of QFR-concordant therapy in stable coronary artery disease. BACKGROUND: Wire-based functional evaluation of coronary disease is linked to patient's prognosis. Quantitative Flow Ratio (QFR) is a newer index of computational physiology, linked to clinical outcomes and prognosis at 1 year follow-up. Long-term prognosis of QFR-concordant revascularization in stable coronary artery disease is however unknown hitherto. METHODS: Consecutive patients with stable coronary disease undergoing coronary angiography were included. Centralized and blinded QFR analysis of three coronary territories was performed. Three vessel QFR (3vQFR) was defined as the sum of the basal QFR of each coronary territory. QFR-concordant revascularization was met if all significant lesions (QFR ≤ 0.80) were revascularized and all non-significant lesions (QFR > 0.80) were not; otherwise, the case was defined as QFR-discordant revascularization. Patient-oriented composite end-point (POCE) of cardiac death, myocardial infarction and unscheduled revascularization was the primary endpoint. RESULTS: A total of 803 patients from six high-volume centers were included. Canadian Cardiovascular Society (CCS) class II angina was the most frequent (48.9%) clinical presentation. Median of follow-up was 68.8 months. 3vQFR was an independent predictor of POCE (HR 1.79 CI95% 1.01-3.18), with 2.75 as optimal cut-off value, irrespective of the therapy received. QFR-discordant revascularization (QFR+/Revascularization- or QFR-/Revascularization+) was an independent predictor of POCE in multivariate analysis (HR 1.65, CI 95% 1.03-2.64). CONCLUSION: Global burden of epicardial coronary atherosclerosis, as evaluated by 3vQFR, as well as QFR-discordant therapy are independent predictors of adverse clinical outcome at long-term follow-up in stable coronary artery disease.

[Research progress on image-based calculation of coronary artery fractional flow reserve].

Coronary artery fractional flow reserve (FFR) is a critical physiological indicator for assessment of impaired blood flow caused by coronary artery stenosis. The wire-based invasive measurement of blood flow pressure gradient across stenosis is the gold standard for clinical measurement of FFR. However, it has the risk of vascular injury and requires the use of vasodilators, increasing the time and overall cost of interventional examination. Coronary imaging is playing an important role in clinical diagnosis of stenotic lesions, evaluation of severity of lesions, and planning of therapies. In recent years, the computation of FFR based on the physiological information of blood flow obtained from routinely collected coronary image data has become a research focus in this field. This technique reduces the cost of physiological assessment of coronary lesions and the use of pressure wires. It is beneficial to strengthen the physiological guidance in interventional therapy. In order to better understand this emerging technique, this paper highlights its implementation principle and diagnostic performance, analyzes practical problems and current challenges in clinical applications, and discusses possible future development.

Effects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy: an in silico study.

Diabetic cardiomyopathy is a leading cause of heart failure in diabetes. At the cellular level, diabetic cardiomyopathy leads to altered mitochondrial energy metabolism and cardiomyocyte ultrastructure. We combined electron microscopy (EM) and computational modelling to understand the impact of diabetes-induced ultrastructural changes on cardiac bioenergetics. We collected transverse micrographs of multiple control and type I diabetic rat cardiomyocytes using EM. Micrographs were converted to finite-element meshes, and bioenergetics was simulated over them using a biophysical model. The simulations also incorporated depressed mitochondrial capacity for oxidative phosphorylation (OXPHOS) and creatine kinase (CK) reactions to simulate diabetes-induced mitochondrial dysfunction. Analysis of micrographs revealed a 14% decline in mitochondrial area fraction in diabetic cardiomyocytes, and an irregular arrangement of mitochondria and myofibrils. Simulations predicted that this irregular arrangement, coupled with the depressed activity of mitochondrial CK enzymes, leads to large spatial variation in adenosine diphosphate (ADP)/adenosine triphosphate (ATP) ratio profile of diabetic cardiomyocytes. However, when spatially averaged, myofibrillar ADP/ATP ratios of a cardiomyocyte do not change with diabetes. Instead, average concentration of inorganic phosphate rises by 40% owing to lower mitochondrial area fraction and dysfunction in OXPHOS. These simulations indicate that a disorganized cellular ultrastructure negatively impacts metabolite transport in diabetic cardiomyopathy. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units.

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory-evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time-wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound-level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross-validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand-average auditory-evoked potential waveforms (r2 > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative 'safety' and 'danger' units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.

Agreement between Murray law-based quantitative flow ratio (µQFR) and three-dimensional quantitative flow ratio (3D-QFR) in non-selected angiographic stenosis: A multicenter study.

BACKGROUND: The agreement between single-projection Murray-based quantitative flow ratio (mQFR) and conventional three-dimensional quantitative flow ratio (3D-QFR) has not been reported hitherto. METHODS: Patients from a multinational database were randomly selected for the study of agreement, according to sample size calculation. Both conventional 3D-QFR and mQFR were analyzed for all available arteries at a central corelab by independent analysts, blinded to each other's results. RESULTS: Ninety-eight coronary arteries from 35 patients were finally analyzed. Median 3D-QFR was 0.82 (interquartile range 0.78-0.87). The intraclass correlation coefficient for the absolute agreement between 3D-QFR and mQFR was 0.996 (95% confidence interval [CI]: 0.993-0.997); Lin's coefficient 0.996 (95% CI: 0.993-0.997), without constant or proportional bias (intercept = 0 and slope = 1 in orthogonal regression). As dichotomous variable, there was absolute agreement between mQFR and 3D-QFR, resulting in no single false positive or negative. Kappa index was 1 and the diagnostic accuracy 100%. CONCLUSIONS: mQFR using a single angiographic projection showed almost perfect agreement with standard 3D-QFR. These results encourage the interchangeable use of mQFR and 3D-QFR, which can be interesting to improve QFR feasibility in retrospective studies, wherein appropriate double angiographic projections might be challenging to obtain.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome Project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science.

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction.

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.

Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates.

Objective.Brain-computer interfaces (BCIs) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes.Approach.We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects.Main results.The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy.Significance.Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.

CellML 2.0.

We present here CellML 2.0, an XML-based language for describing and exchanging mathematical models of physiological systems. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of reusable components, each with variables and equations giving relationships between those variables. Models may import other models to create systems of increasing complexity. CellML 2.0 is defined by the normative specification presented here, prescribing the CellML syntax and the rules by which it should be used. The normative specification is intended primarily for the developers of software tools which directly consume CellML syntax. Users of CellML models may prefer to browse the informative rendering of the specification (https://cellml.org/specifications/cellml_2.0/) which extends the normative specification with explanations of the rules combined with examples of their usage.

Deep phenotyping of cardiac function in heart transplant patients using cardiovascular system models.

KEY POINTS: Right heart catheterization data from clinical records of heart transplant patients are used to identify patient-specific models of the cardiovascular system. These patient-specific cardiovascular models represent a snapshot of cardiovascular function at a given post-transplant recovery time point. This approach is used to describe cardiac function in 10 heart transplant patients, five of which had multiple right heart catheterizations allowing an assessment of cardiac function over time. These patient-specific models are used to predict cardiovascular function in the form of right and left ventricular pressure-volume loops and ventricular power, an important metric in the clinical assessment of cardiac function. Outcomes for the longitudinally tracked patients show that our approach was able to identify the one patient from the group of five that exhibited post-transplant cardiovascular complications. ABSTRACT: Heart transplant patients are followed with periodic right heart catheterizations (RHCs) to identify post-transplant complications and guide treatment. Post-transplant positive outcomes are associated with a steady reduction of right ventricular and pulmonary arterial pressures, toward normal levels of right-side pressure (about 20 mmHg) measured by RHC. This study shows that more information about patient progression is obtained by combining standard RHC measures with mechanistic computational cardiovascular system models. The purpose of this study is twofold: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state, and to use these models to track post-transplant recovery and outcome. To obtain reliable parameter estimates comparable within and across datasets, we use sensitivity analysis, parameter subset selection, and optimization to determine patient-specific mechanistic parameters that can be reliably extracted from the RHC data. Patient-specific models are identified for 10 patients from their first post-transplant RHC, and longitudinal analysis is carried out for five patients. Results of the sensitivity analysis and subset selection show that we can reliably estimate seven non-measurable quantities; namely, ventricular diastolic relaxation, systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and systemic arterial elastance. Changes in parameters and predicted cardiovascular function post-transplant are used to evaluate the cardiovascular state during recovery of five patients. Of these five patients, only one showed inconsistent trends during recovery in ventricular pressure-volume relationships and power output. At the four-year post-transplant time point this patient exhibited biventricular failure along with graft dysfunction while the remaining four exhibited no cardiovascular complications.

Simulation of the Perfusion of Contrast Agent Used in Cardiac Magnetic Resonance: A Step Toward Non-invasive Cardiac Perfusion Quantification.

This work presents a new mathematical model to describe cardiac perfusion in the myocardium as acquired by cardiac magnetic resonance (CMR) perfusion exams. The combination of first pass (or contrast-enhanced CMR) and late enhancement CMR is a widely used non-invasive exam that can identify abnormal perfused regions of the heart via the use of a contrast agent (CA). The exam provides important information to the diagnosis, management, and prognosis of ischemia and infarct: perfusion on different regions, the status of microvascular structures, the presence of fibrosis, and the relative volume of extracellular space. This information is obtained by inferring the spatiotemporal dynamics of the contrast in the myocardial tissue from the acquired images. The evaluation of these physiological parameters plays an important role in the assessment of myocardial viability. However, the nature of cardiac physiology poses great challenges in the estimation of these parameters. Briefly, these are currently estimated qualitatively via visual inspection of images and comparison of relative brightness between different regions of the heart. Therefore, there is a great urge for techniques that can help to quantify cardiac perfusion. In this work, we propose a new mathematical model based on multidomain flow in porous media. The model is based on a system of partial differential equations. Darcy's law is used to obtain the pressure and velocity distribution. CA dynamics is described by reaction-diffusion-advection equations in the intravascular space and in the interstitial space. The interaction of fibrosis and the CA is also considered. The new model treats the domains as anisotropic media and imposes a closed loop of intravascular flow, which is necessary to reproduce the recirculation of the CA. The model parameters were adjusted to reproduce clinical data. In addition, the model was used to simulate different scenarios: normal perfusion; endocardial ischemia due to stenosis in a coronary artery in the epicardium; and myocardial infarct. Therefore, the computational model was able to correlate anatomical features, stenosis and the presence of fibrosis, with functional ones, cardiac perfusion. Altogether, the results suggest that the model can support the process of non-invasive cardiac perfusion quantification.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans.

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Computational model-based assessment of baroreflex function from response to Valsalva maneuver.

Functional metrics of autonomic control of heart rate, including baroreflex sensitivity, have been shown to be strongly associated with cardiovascular risk. A decrease in baroreflex sensitivity with aging is hypothesized to represent a contributing causal factor in the etiology of primary hypertension. To assess baroreflex function in human subjects, two complementary methods to simulate the response in heart rate elicited by the Valsalva maneuver were developed and applied to data obtained from a cohort of healthy normal volunteers. The first method is based on representing the baroreflex arc as a simple linear filter, transforming changes in arterial pressure to changes in R-R interval. The second method invokes a physiologically based model for arterial mechanics, afferent baroreceptor strain-dependent firing, and control of heart rate via central autonomic response to changes in afferent inputs from aortic and carotid sensors. Analysis based on the linear filter model reveals that the effective response time of the baroreflex arc tends to increase with age in healthy subjects and that the response time/response rate is a predictor of resting systolic pressure. Similar trends were obtained based on the physiologically based model. Analysis of the Valsalva response using the physiologically based model further reveals that different afferent inputs from the carotid sinus and the aortic arch baroreceptors govern different parts of the heart rate response. The observed relationship between baroreflex sensitivity and systolic pressure is surprising because hypertensive subjects were excluded from the study, and there was no observed relationship between arterial pressure and age. NEW & NOTEWORTHY We introduce two methods to assess baroreflex function from data recorded from human subjects performing the Valsalva maneuver. Results demonstrate that the baroreflex response time tends to increase with age in healthy subjects, that response time represents a predictor of resting systolic pressure, and that the Valsalva response reveals different effects mediated by baroreceptors in the carotid sinus compared with those in the aortic arch.

Computational Assessment of Blood Flow Heterogeneity in Peritoneal Dialysis Patients' Cardiac Ventricles.

Dialysis prolongs life but augments cardiovascular mortality. Imaging data suggests that dialysis increases myocardial blood flow (BF) heterogeneity, but its causes remain poorly understood. A biophysical model of human coronary vasculature was used to explain the imaging observations, and highlight causes of coronary BF heterogeneity. Post-dialysis CT images from patients under control, pharmacological stress (adenosine), therapy (cooled dialysate), and adenosine and cooled dialysate conditions were obtained. The data presented disparate phenotypes. To dissect vascular mechanisms, a 3D human vasculature model based on known experimental coronary morphometry and a space filling algorithm was implemented. Steady state simulations were performed to investigate the effects of altered aortic pressure and blood vessel diameters on myocardial BF heterogeneity. Imaging showed that stress and therapy potentially increased mean and total BF, while reducing heterogeneity. BF histograms of one patient showed multi-modality. Using the model, it was found that total coronary BF increased as coronary perfusion pressure was increased. BF heterogeneity was differentially affected by large or small vessel blocking. BF heterogeneity was found to be inversely related to small blood vessel diameters. Simulation of large artery stenosis indicates that BF became heterogeneous (increase relative dispersion) and gave multi-modal histograms. The total transmural BF as well as transmural BF heterogeneity reduced due to large artery stenosis, generating large patches of very low BF regions downstream. Blocking of arteries at various orders showed that blocking larger arteries results in multi-modal BF histograms and large patches of low BF, whereas smaller artery blocking results in augmented relative dispersion and fractal dimension. Transmural heterogeneity was also affected. Finally, the effects of augmented aortic pressure in the presence of blood vessel blocking shows differential effects on BF heterogeneity as well as transmural BF. Improved aortic blood pressure may improve total BF. Stress and therapy may be effective if they dilate small vessels. A potential cause for the observed complex BF distributions (multi-modal BF histograms) may indicate existing large vessel stenosis. The intuitive BF heterogeneity methods used can be readily used in clinical studies. Further development of the model and methods will permit personalized assessment of patient BF status.

Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter.

Core body temperature (TC) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (TS), heat flux (HF), and heart rate (HR) to accurately estimate TC using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ±â€¯4 yr, height 1.75 ±â€¯0.10 m, body mass 76.4 ±â€¯10.7 kg, and body fat 23.4 ±â€¯5.8%, mean ±â€¯standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between TC and TS, HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ±â€¯0.04 °C; bias -0.01 ±â€¯0.09 °C), rib (RMSE 0.18 ±â€¯0.09 °C; bias -0.03 ±â€¯0.09 °C), and sternum (RMSE 0.20 ±â€¯0.10 °C; bias -0.04 ±â€¯0.13 °C) were found to have the lowest error values when using TS, HF, and HR but, using only two of these measures provided similar accuracy.

The Use of Biophysical Flow Models in the Surgical Management of Patients Affected by Chronic Thromboembolic Pulmonary Hypertension.

Introduction: Chronic Thromboembolic Pulmonary Hypertension (CTEPH) results from progressive thrombotic occlusion of the pulmonary arteries. It is treated by surgical removal of the occlusion, with success rates depending on the degree of microvascular remodeling. Surgical eligibility is influenced by the contributions of both the thrombus occlusion and microvasculature remodeling to the overall vascular resistance. Assessing this is challenging due to the high inter-individual variability in arterial morphology and physiology. We investigated the potential of patient-specific computational flow modeling to quantify pressure gradients in the pulmonary arteries of CTEPH patients to assist the decision-making process for surgical eligibility. Methods: Detailed segmentations of the pulmonary arteries were created from postoperative chest Computed Tomography scans of three CTEPH patients. A focal stenosis was included in the original geometry to compare the pre- and post-surgical hemodynamics. Three-dimensional flow simulations were performed on each morphology to quantify velocity-dependent pressure changes using a finite element solver coupled to terminal 2-element Windkessel models. In addition to transient flow simulations, a parametric modeling approach based on constant flow simulations is also proposed as faster technique to estimate relative pressure drops through the proximal pulmonary vasculature. Results: An asymmetrical flow split between left and right pulmonary arteries was observed in the stenosed models. Removing the proximal obstruction resulted in a reduction of the right-left pressure imbalance of up to 18%. Changes were also observed in the wall shear stresses and flow topology, where vortices developed in the stenosed model while the non-stenosed retained a helical flow. The predicted pressure gradients from constant flow simulations were consistent with the ones measured in the transient flow simulations. Conclusion: This study provides a proof of concept that patient-specific computational modeling can be used as a noninvasive tool for assisting surgical decisions in CTEPH based on hemodynamics metrics. Our technique enables determination of the proximal relative pressure, which could subsequently be compared to the total pressure drop to determine the degree of distal and proximal vascular resistance. In the longer term this approach has the potential to form the basis for a more quantitative classification system of CTEPH types.

Wearable physiological monitoring for human thermal-work strain optimization.

Safe performance limits of soldiers and athletes have typically relied on predictive work-rest models of ambient conditions, average work intensity, and characteristics of the population. Bioengineering advances in noninvasive sensor technologies, including miniaturization, reduced cost, power requirements, and comfort, now make it possible to produce individual predictions of safe thermal-work limits. These precision medicine assessments depend on the development of thoughtful algorithms based on physics and physiology. Both physiological telemetry and thermal-strain indexes have been available for >50 years, but greater computing power and better wearable sensors now make it possible to provide actionable information at the individual level. Core temperature can be practically estimated from time series heart rate data and, using an adaptive physiological strain index, provides meaningful predictions of safe work limits that cannot be predicted from only core temperature or heart rate measurements. Early adopters of this technology include specialized occupations where individuals operate in complete encapsulation such as chemical protective suits. Emerging technologies that focus on heat flux measurements at the skin show even greater potential for estimating thermal-work strain using a parsimonious sensor set. Applications of these wearable technologies include many sports and military training venues where inexperienced individuals can learn effective work pacing strategies and train to safe personal limits. The same strategies can also provide a technologically based performance edge for experienced workers and athletes faced with novel and nonintuitive physiological challenges, such as health care providers in full protective clothing treating Ebola patients in West Africa in 2014. NEW & NOTEWORTHY This mini-review details how the application of computational techniques borrowed from signal processing and control theory can provide meaningful advances for the applied physiological problem of real-time thermal-work strain monitoring. The work examines the development of practical core body temperature estimation techniques and how these can be used in combination with current and updated thermal-work strain indexes to provide objective state assessments and to optimize work rest schedules for a given task.