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Cardiovascular disease (CVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random Forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China.

Arterial compliance (AC) is an important cardiovascular parameter characterizing mechanical properties of arteries. AC is significantly influenced by arterial wall structure and vasomotion, and it markedly influences cardiac load. A new method, based on a two-element Windkessel model, has been recently proposed for estimating AC as the ratio of the time constant τ of the diastolic blood pressure decay and peripheral vascular resistance derived from clinically available stroke volume measurements and selected peripheral blood pressure parameters which are less prone to peripheral distortions. The aim of this study was to validate AC estimation using a virtual population generated by in silico model of the systemic arterial tree. In the second part of study, we analysed causal coupling between AC oscillations and variability of its potential determinants – systolic blood pressure and heart rate in healthy young human subjects. The pool of virtual subjects (n=3818) represented an extensive AC distribution. AC was estimated from the peripheral blood pressure curve and by the standard method from the aortic blood pressure curve. The proposed method slightly overestimated AC set in the model but both ACs were strongly correlated (r=0.94, p<0.001). In real data, we observed that AC dynamics was coupled with basic cardiovascular parameters variability independently of the autonomic nervous system state. In silico analysis suggests that AC can be reliably estimated by noninvasive method. The analysis of short-term AC variability together with its determinants could improve our understanding of factors involved in AC dynamics potentially improving assessment of AC changes associated with atherosclerosis process.

Key Points Medical 3D printing refers to the fabrication of anatomical structures, typically derived from volumetric medical image data, and enables visual inspection and direct manipulation of hand-held models of human anatomy and pathology In cardiovascular 3D printing, advanced modern imaging such CT and MR is combined with dedicated 3D printing software and hardware Cardiovascular 3D printing enhances the diagnostic work-up of complex cardiovascular diseases, as well as surgical and interventional procedural planning and simulation 3D printing improves patient engagement in understanding their own diseases and participating in their own decision-making, and improves communication with patients and their families Widespread adoption of 3D printing is currently limited by the lack of robust evidence that systematically demonstrates effectiveness, and by the high costs and workflow complexity Cardiovascular 3D bioprinting and molecular 3D printing — which combine advanced manufacturing, cell biology, molecular biomarkers, and materials science — have not yet translated into clinical practice, but hold great promise for the future 3D printing applications for cardiovascular care range from models for education to planning and simulation of interventions and the generation of implantable devices. This Review summarizes the current cardiovascular 3D printing strategies and applications, including the workflow from image acquisition to the generation of a hand-held model, and highlights the future perspectives of cardiovascular 3D printing. 3D-printed models fabricated from CT, MRI, or echocardiography data provide the advantage of haptic feedback, direct manipulation, and enhanced understanding of cardiovascular anatomy and underlying pathologies. Reported applications of cardiovascular 3D printing span from diagnostic assistance and optimization of management algorithms in complex cardiovascular diseases, to planning and simulating surgical and interventional procedures. The technology has been used in practically the entire range of structural, valvular, and congenital heart diseases, and the added-value of 3D printing is established. Patient-specific implants and custom-made devices can be designed, produced, and tested, thus opening new horizons in personalized patient care and cardiovascular research. Physicians and trainees can better elucidate anatomical abnormalities with the use of 3D-printed models, and communication with patients is markedly improved. Cardiovascular 3D bioprinting and molecular 3D printing, although currently not translated into clinical practice, hold revolutionary potential. 3D printing is expected to have a broad influence in cardiovascular care, and will prove pivotal for the future generation of cardiovascular imagers and care providers. In this Review, we summarize the cardiovascular 3D printing workflow, from image acquisition to the generation of a hand-held model, and discuss the cardiovascular applications and the current status and future perspectives of cardiovascular 3D printing.

Key Points Human induced pluripotent stem cells (hiPSCs) can now be reprogrammed from different somatic cell sources and differentiated into common cardiovascular cell types, including cardiomyocytes, endothelial cells, and vascular smooth muscle cells hiPSC-derived cardiovascular cells recapitulate patient-specific and disease-specific phenotypes, which can be exploited to design individualized treatment strategies hiPSC derivatives have enabled the accurate modelling of numerous cardiovascular diseases, including cardiomyopathies, arrhythmia syndromes, cardiometabolic disorders, vascular diseases, and valvulopathies hiPSC-based platforms for drug discovery and cardiotoxicity testing are now being incorporated into major pharmaceutical drug development pipelines and standards of drug safety testing, respectively Further refinement in large-scale production of mature hiPSC-derived cardiovascular cells will be necessary to realize the potential of using hiPSCs to guide precision medicine Human induced pluripotent stem cells (hiPSCs) can be differentiated into many cardiovascular cell types, including cardiomyocytes and endothelial cells. hiPSC-derived cardiovascular cells can recapitulate patient-specific and disease-specific phenotypes. In this Review, Chen et al . discuss how hiPSCs can be used as a platform for cardiovascular drug development and disease modelling, and can facilitate individualized therapy in the era of precision medicine. The advent of human induced pluripotent stem cell (hiPSC) technology has revitalized the efforts in the past decade to realize more fully the potential of human embryonic stem cells for scientific research. Adding to the possibility of generating an unlimited amount of any cell type of interest, hiPSC technology now enables the derivation of cells with patient-specific phenotypes. Given the introduction and implementation of the large-scale Precision Medicine Initiative, hiPSC technology will undoubtedly have a vital role in the advancement of cardiovascular research and medicine. In this Review, we summarize the progress that has been made in the field of hiPSC technology, with particular emphasis on cardiovascular disease modelling and drug development. The growing roles of hiPSC technology in the practice of precision medicine will also be discussed.

Various approaches for estimating the flow rate of a rotary blood pump have been proposed for monitoring and control purposes. They have been evaluated under different test conditions and, therefore, a direct comparison among them is difficult. Furthermore, a limited performance has been reported for the areas where the pump flow and motor current present a non-monotonic relationship. In this regard, we selected most approaches that have been presented in literature and added a modified one, resulting in four estimators, which are either non-invasive or invasive, i.e., inlet and outlet pump pressure sensors are used. Data from in vitro and in vivo studies with the Deltastream pump DP2 were used to compare the estimators under the same test conditions. These data included both constant and varying pre- and afterload, contractility, viscosity, as well as pump speed settings. Bland–Altman plots were used to evaluate the performance of the estimators. The mean error of the overall estimated flow in vitro ranged from 0.002 to 0.38 L/min and the limits of agreement (LoA) between ± 2 L/min. During negative flows the mean error decreased by about 25% when the pump inlet pressure was added as an input. In vivo, the mean errors increased, while the LoA remained in the same range. An estimator based on pump pressure difference improves the reliability in areas where flow and current relationship is not monotonic. A trade-off between estimation accuracy and number of sensors was identified. The estimation objective and the potential errors should be considered when selecting an estimation approach and designing the pump systems.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease 1 , screening for cardiotoxicity 2 and decisions regarding the clinical management of patients with a critical illness 3 . However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training 4 , 5 . Here, to overcome this challenge, we present a video-based deep learning algorithm—EchoNet-Dynamic—that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos. A video-based deep learning algorithm—EchoNet-Dynamic—accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

To employ a reduced-order cardiovascular model as a digital twin for personalised medicine, it is essential to understand how uncertainties in the model’s input parameters affect its outputs. The aim is to identify a set of input parameters that can serve as clinical biomarkers, providing insight into a patient’s physiological state. Given the challenge of finding useful clinical data, careful consideration must be given to the experimental design used to acquire patient-specific input parameters. Model sloppiness—where numerous parameter combinations have minimal impact on model predictions, whilst only a few parameters significantly influence outcomes—is a critical concept in this context. In this paper, we conduct the first quantification of a cardiovascular system’s sloppiness to elucidate the structure of the input parameter space. By utilising Sobol indices and examining various synthetic cardiovascular measures with increasing invasiveness, we uncover how the personalisation process and the cardiovascular system’s sloppiness are contingent upon the chosen experimental design. Our findings reveal that continuous clinical measures induce system sloppiness and increase the number of personalisable biomarkers, whereas discrete clinical measurements produce a non-sloppy system with a reduced number of biomarkers. This study underscores the necessity for careful consideration of available clinical data as differing measurement sets can significantly impact model personalisation.

Thirteen years after his seminal Review on flow-mediated endothelial mechanotransduction, Peter Davies reviews the complex spatiotemporal shear stress characteristics that can predict atherosclerosis susceptibility. He also examines endothelial flow-induced responses—collectively known as mechanotransduction—and the spatially decentralized mechanism of endothelial mechanotransduction. Endothelium lining the cardiovascular system is highly sensitive to hemodynamic shear stresses that act at the vessel luminal surface in the direction of blood flow. Physiological variations of shear stress regulate acute changes in vascular diameter and when sustained induce slow, adaptive, structural-wall remodeling. Both processes are endothelium-dependent and are systemically and regionally compromised by hyperlipidemia, hypertension, diabetes and inflammatory disorders. Shear stress spans a range of spatiotemporal scales and contributes to regional and focal heterogeneity of endothelial gene expression, which is important in vascular pathology. Regions of flow disturbances near arterial branches, bifurcations and curvatures result in complex spatiotemporal shear stresses and their characteristics can predict atherosclerosis susceptibility. Changes in local artery geometry during atherogenesis further modify shear stress characteristics at the endothelium. Intravascular devices can also influence flow-mediated endothelial responses. Endothelial flow-induced responses include a cell-signaling repertoire, collectively known as mechanotransduction, that ranges from instantaneous ion fluxes and biochemical pathways to gene and protein expression. A spatially decentralized mechanism of endothelial mechanotransduction is dominant, in which deformation at the cell surface induced by shear stress is transmitted as cytoskeletal tension changes to sites that are mechanically coupled to the cytoskeleton. A single shear stress mechanotransducer is unlikely to exist; rather, mechanotransduction occurs at multiple subcellular locations. Key Points Hemodynamic forces, and in particular shear stresses, are regulators of many physiologic and pathologic aspects of endothelial function in the cardiovascular system In vivo and in vitro global endothelial analyses reveal that endothelial phenotypes are heterogeneous over regional and focal length scales, which links flow characteristics to cardiovascular disease protection, susceptibility and development Endothelial responses are sensitive to variations in the characteristics of flow that generate shear stresses; regions with oscillating shear stress and flow reversal correspond with pathologic changes in the artery wall and are a risk factor for atherosclerosis-susceptibility When shear stresses deform the endothelium, a mechanical perturbation is communicated via the cytoskeleton to multiple sites of mechanotransduction, which include cell–matrix adhesion sites, intercellular junctions and the nuclear membrane Endothelial responses that are specific to shear stress offer potential therapeutic pharmacological targets, although a single mechanosensor is unlikely to exist Beneficial systemic effects include maintenance of arterial hemodynamics within normal limits through antihypertensive therapies, regular exercise to promote continuous adaptive remodeling and inhibition of endothelial dysfunction, and (when intervention is required) better design of intravascular devices to optimize flow characteristics

Arterial stiffness is an age-related disorder. In the medial layer of arteries, mechanical fracture due to fatigue failure for the pulsatile wall strain causes medial degeneration vascular remodeling. The alteration of extracellular matrix composition and arterial geometry result in structural arterial stiffness. Calcium deposition and other factors such as advanced glycation end product-mediated collagen cross-linking aggravate the structural arterial stiffness. On the other hand, endothelial dysfunction is a cause of arterial stiffness. The biological molecular mechanisms relating to aging are known to involve the progression of arterial stiffness. Arterial stiffness further applies stress on large arteries and also microcirculation. Therefore, it is closely related to adverse outcomes in cardiovascular and cerebrovascular system. Cardio-ankle vascular index (CAVI) is a promising diagnostic tool for evaluating arterial stiffness. The principle is based on stiffness parameter β, which is an index intended to assess the distensibility of carotid artery. Stiffness parameter β is a two-dimensional technique obtained from changes of arterial diameter by pulse in one section. CAVI applied the stiffness parameter β to all of the arterial segments between heart and ankle using pulse wave velocity. CAVI has been commercially available for a decade and the clinical data of its effectiveness has accumulated. The characteristics of CAVI differ from other physiological tests of arterial stiffness due to the independency from blood pressure at the time of examination. This review describes the pathophysiology of arterial stiffness and CAVI. Molecular mechanisms will also be covered.

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40–79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70–0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer–Lemeshow χ 2  = 86.1, P < 0.001) than PCE for whites did (Hosmer–Lemeshow χ 2  = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.