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IntroductionHemorrhage remains a leading cause of death in civilian and military trauma. Hemorrhages also extend to military working dogs, who can experience injuries similar to those of the humans they work alongside. Unfortunately, current physiological monitoring is often inadequate for early detection of hemorrhage. Here, we evaluate if features extracted from the arterial waveform can allow for early hemorrhage prediction and improved intervention in canines.MethodsIn this effort, we extracted more than 1,900 features from an arterial waveform in canine hemorrhage datasets prior to hemorrhage, during hemorrhage, and during a shock hold period. Different features were used as input to decision tree machine learning (ML) model architectures to track three model predictors—total blood loss volume, estimated percent blood loss, and area under the time versus hemorrhaged blood volume curve.ResultsML models were successfully developed for total and estimated percent blood loss, with the total blood loss having a higher correlation coefficient. The area predictors were unsuccessful at being directly predicted by decision tree ML models but could be calculated indirectly from the ML prediction models for blood loss. Overall, the area under the hemorrhage curve had the highest sensitivity for detecting hemorrhage at approximately 4 min after hemorrhage onset, compared to more than 45 min before detection based on mean arterial pressure.ConclusionML methods successfully tracked hemorrhage and provided earlier prediction in canines, potentially improving hemorrhage detection and objectifying triage for veterinary medicine. Further, its use can potentially be extended to human use with proper training datasets.

Individual mobility vehicles are revolutionizing transportation in the urban environment. Among them, e-scooters are one of the fastest growing vehicles. However, these vehicles have some drawbacks such as incompatibility among different chargers or reduced autonomy, which especially affects e-scooters rental. Wireless charging is presented as a solution to these drawbacks since it allows the batteries to be charged without user intervention. This paper addresses the design process and implementation details related to a magnetic-resonance charger for an e-scooter. In contrast to the previous works, we present a comprehensive approach that addresses the design of the coil topology, the dimensions of the ferrite tiles, the definition of the gap and the optimised control for a CC-CV charge. The main contribution of the work is the consideration of all these issues jointly while we also take into account the vehicle’s materials and structure for an accurate design and implementation. The dimensions of the vehicle impose a strong restriction on the coil design. Therefore, a detailed analysis about the location of the secondary and primary coils in a real e-scooter is executed with Ansys Maxwell. The study about the geometries of ferrite tiles is also performed considering the particularities of this type of vehicle. This study led to an optimal solution for the coil geometries and ferrite placement that minimizes the costs. The proposed system has been validated with the implementation of a real prototype of 100 W, to which a CC-CV control has been incorporated to achieve a safe charge for different battery states. The control can be easily adapted for a wide range of e-scooters. In this way, the use of this type of chargers on public installations can be maximized.

Abstract Primary open angle glaucoma (POAG) features an optic neuropathy, elevated aqueous humor (AH) TGFβ2, and major risk factors of central corneal thickness (CCT), increasing age and intraocular pressure (IOP). We examined Tight skin (Tsk) mice to see if mutation of fibrillin-1, a repository for latent TGFβ, is associated with characteristics of human POAG. We measured: CCT by ocular coherence tomography (OCT); IOP; retinal ganglion cell (RGC) and optic nerve axon counts by microscopic techniques; visual electrophysiologic scotopic threshold responses (STR) and pattern electroretinogram (PERG); and AH TGFβ2 levels and activity by ELISA and MINK epithelial cell-based assays respectively. Tsk mice had open anterior chamber angles and compared with age-matched wild type (WT) mice: 23% thinner CCT (p

Electric Vehicles are a realistic and sustainable alternative to traditional vehicles. However, their autonomy is still perceived as a strong limitation. To overcome it, new and simpler methods for charging must be provided. Wireless charging is a feasible solution for this issue. In particular, the charge based on induction methods is the most mature wireless charging technology but it is still under study to improve its efficiency under any condition. To enhace this metric, a deep study about the losses of the charger must be performed. This paper presents a theoretical model for the losses in EV wireless charger so it can help to estimate the efficiency of this system before it is implemented. The main advantage of the proposed model relies on the fact that it can be used without relying on the voltage and current waveforms. Instead, datasheet parameters are used for this computation. The model, which is valid for multiple operational conditions, has been tested in a 1.8-kW prototype. The evaluation is completed with an exhaustive study of the efficiency when the chargers are not operating with nominal conditions, in which critical operating points are detected.

Abstract The relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities—linked to Neuron Projection—were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.

Residence time is an important parameter linked to the water quality in an estuary. In this paper, we identify and analyze the main processes that affect the residence time of the Caloosahatchee River Estuary, a micro-tidal and mixed diurnal-semidiurnal estuary located in western Florida. Multiyear validated hydrodynamic hindcast results were coupled with an offline particle tracking model to compute the residence time of the estuary, which showed a strong seasonality driven by the river discharge. The residence time reduced with increasing river flow. The wind velocity and direction also affected the residence time. The influence of the wind was dependent on the magnitude of the river discharge. In general, upstream-directed wind increased residence time, while downstream-directed wind decreased residence time. Downstream wind during the dry period reduced residence time on average by a week. Processes such as water density gradient-induced circulation and particle buoyancy also influenced the residence time of the estuary. The outcomes of this study can be used to better understand the influence of the main physical processes affecting the residence time at other similar estuaries and to help in the management of the estuaries to improve their water quality.

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control algorithm prove to be crucial to the performance of the wireless transfer. Concerning the placement of the secondary coil, the paper proposes and validates a theoretical model to quantify the effects of the surrounding interfering materials. A CC-CV (Constant Current - Constant Voltage) control algorithm has been implemented to validate the theoretical results in these two charging phases. The control, the coils and the compensation networks have been designed to be simple and robust. The theoretical model, the charger and its control have proven to be effective in a real 84-W e-bike.

Abstract Background Olfactory receptor (OR) genes are the largest multi-gene family in the mammalian genome, with 874 in human and 1483 loci in mouse (including pseudogenes). The expansion of the OR gene repertoire has occurred through numerous duplication events followed by diversification, resulting in a large number of highly similar paralogous genes. These characteristics have made the annotation of the complete OR gene repertoire a complex task. Most OR genes have been predicted in silico and are typically annotated as intronless coding sequences. Results Here we have developed an expert curation pipeline to analyse and annotate every OR gene in the human and mouse reference genomes. By combining evidence from structural features, evolutionary conservation and experimental data, we have unified the annotation of these gene families, and have systematically determined the protein-coding potential of each locus. We have defined the non-coding regions of many OR genes, enabling us to generate full-length transcript models. We found that 13 human and 41 mouse OR loci have coding sequences that are split across two exons. These split OR genes are conserved across mammals, and are expressed at the same level as protein-coding OR genes with an intronless coding region. Our findings challenge the long-standing and widespread notion that the coding region of a vertebrate OR gene is contained within a single exon. Conclusions This work provides the most comprehensive curation effort of the human and mouse OR gene repertoires to date. The complete annotation has been integrated into the GENCODE reference gene set, for immediate availability to the research community.

This study compares the skills of two numerical models at the same horizontal resolution but based on different principles in representing meso- and sub-mesoscale ocean features. The first model, titled LD20-NEMO, was based on solving primitive equations of ocean dynamics. The second model, titled LD20-SDD, used a newer stochastic–deterministic downscaling (SDD) method. Both models had 1/20° resolution, the same meteo forcing, and used outputs from a data assimilating global model at 1/12° resolution available from Copernicus Marine Service (CMEMS). The LD20 models did not assimilate observational data but were physically aware of observations via the parent model. The LD20-NEMO only used a 2D set of data from CMEMS as the lateral boundary conditions. The LD20-SDD consumed the full 3D set of data from CMEMS and exploited the stochastic properties of these data to generate the downscaled field variables at higher resolution than the parent model. The skills of the three models were assessed against remotely sensed and in situ observations for the four-year period 2015–2018. The models showed similar skills in reproducing temperature and salinity, however the SDD version performed slightly better than the NEMO, and was more computationally efficient by a large margin.

Introduction: On the United States’ Organ Transplantation Waitlist, approximately 17 people die each day waiting for an organ. The situation continues to deteriorate as the discrepancy between harvested organs and the number of patients in need is increasing. Static cold storage is the clinical standard method for preserving a harvested organ but is associated with several drawbacks. Machine perfusion of an organ has been shown to improve preservation quality as well as preservation time over static cold storage. While there are machine perfusion devices clinically available, they are costly and limited to specific organs and preservation solutions. This study presents a versatile oxygenating perfusion system (VOPS) that supplies oxygen and pulsatile perfusion. Materials and Methods: Experiments evaluated the system’s performance with a human kidney mimicking hydraulic analog using multiple compressed oxygen supply pressures and aqueous solutions with viscosities ranging from 1 to 6.5 cP, which simulated viscosities of commonly used organ preservation solutions. Results and Conclusions: The VOPS produced mean flow rates ranging from 0.6 to 28.2 mL/min and perfusion pressures from 4.8 to 96.8 mmHg, which successfully achieved the desired perfusion parameters for human kidneys. This work provides evidence that the VOPS described herein has the versatility to perfuse organs using many of the clinically available preservation solutions.

This study presents the Relocatable Operational Ocean Model (ReOMo), which can be used as a Crisis Ocean Modelling System in any region of the global ocean that is free from ice. ReOMo can be quickly nested into an existing coarser resolution (parent) model. The core components of ReOMo are the NEMO hydrodynamic model and Rose-Cylc workflow management software. The principal innovative feature of ReOMo is the use of the Nesting with Data Assimilation (NDA) algorithm, which is based on the model-to-model assimilation technique. The NDA utilises the full 3D set of field variables from the parent model rather than just the 2D boundary conditions. Therefore, ReOMo becomes physically aware of observations that have been assimilated and dynamically balanced in the external model. The NDA also reduces the spatial phase shift of ocean features known as the ‘double penalty effect’. In this study, ReOMo was implemented for the Lakshadweep Sea in the Indian Ocean at 1/20°, 1/60°, or 1/120° resolution with and without model-to-model data assimilation. ReOMo is computationally efficient, and it was validated against a number of observational data sets to show good skills with an additional benefit of having better resolution than the parent model.

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.

Central vascular access (CVA) may be critical for trauma care and stabilizing the casualty. However, it requires skilled personnel, often unavailable during remote medical situations and combat casualty care scenarios. Automated CVA medical devices have the potential to make life-saving therapeutics available in these resource-limited scenarios, but they must be properly designed. Unfortunately, currently available tissue phantoms are inadequate for this use, resulting in delayed product development. Here, we present a tissue phantom that is modular in design, allowing for adjustable flow rate, circulating fluid pressure, vessel diameter, and vessel positions. The phantom consists of a gelatin cast using a 3D-printed mold with inserts representing vessels and bone locations. These removable inserts allow for tubing insertion which can mimic normal and hypovolemic flow, as well as pressure and vessel diameters. Trauma to the vessel wall is assessed using quantification of leak rates from the tubing after removal from the model. Lastly, the phantom can be adjusted to swine or human anatomy, including modeling the entire neurovascular bundle. Overall, this model can better recreate severe hypovolemic trauma cases and subject variability than commercial CVA trainers and may potentially accelerate automated CVA device development.

The use of lightweight materials, such as ultra-high-strength aluminium alloys, is in high demand in the automotive and aerospace industries where weight savings are critical. The tool materials used for high-speed cutting of these aluminium alloys are subjected to severe conditions that promote premature failure of cutting tools. The application of polycrystalline diamond (PCD) coatings provides cutting tools with increased mechanical and thermal fatigue resistance and improved tribological performance. Despite these good properties, their high cost remains a major limitation in this sector. Super-hard Diamond-Like Carbon (DLC) coatings offer a technologically and economically feasible alternative to PCD-coated tools for cutting and machining non-ferrous materials. In this paper, the machining performance of coated and un-coated hard metal inserts in the turning of 7075 aluminium alloy has been explored. The surface quality of machined parts, the cutting tool wear resistance and the vibrations generated during turning of un-coated, PCD and super-hard thin DLC coatings on tungsten carbide inserts were compared. The results obtained demonstrate that DLC coatings are a potentially interesting alternative to PCD coatings for machining ultra-high-strength aluminium alloys, where surface component finish is a key factor.

New dams impact downstream ecosystems and water infrastructure; without cooperative and adaptive management, negative impacts can manifest. In large complex transboundary river basins without well codified operating rules and extensive historical data, it can be difficult to assess the benefits of cooperating, in particular in relation to new dams. This constitutes a barrier to harmonious development of river basins and could contribute to water conflict. This study proposes a generalised framework to assess the benefits of cooperation on the management of new dams in water resource systems that do not have formal sharing arrangements. Benefits are estimated via multi-criteria comparison of historical reservoir operations (usually relatively uncooperative) vs. adopting new cooperative rules which would achieve the best results for riparian countries as evaluated by a water resources simulator and its performance metrics. The approach is applied to the Pwalugu Multipurpose Dam (PMD), which is being built in Ghana in the Volta river basin. The PMD could impact downstream ecosystems and infrastructure in Ghana and could itself be impacted by how the existing upstream Bagre Dam is managed in Burkina Faso. Results show that with cooperation Ghana and Burkina Faso could both increase energy production although some ecosystem services loss would need to be mitigated. The study confirms that cooperative rules achieve higher overall benefits compared to seeking benefits only for individual dams or countries.

A simple and computationally efficient method is presented for creating a high-resolution regional (child) model nested within a coarse-resolution, good-quality data-assimilating (parent) model. The method, named Nesting with Downscaling and Data Assimilation (NDA), reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift from reality. Usually coarser resolution models, e.g., global scale, are used to provide boundary conditions for the nested child model. The basic idea of the NDA method is to use a complete 3D set of output data from the parent model using a process which is similar to data assimilation of observations into an ocean model. In this way, the child model is physically aware of observations via the parent model. The method allows for avoiding a complex process of assimilating the same observations which were already assimilated into the parent model. The NDA method is illustrated in several simple 2D synthetic cases where the true solution is known. The NDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE, typically by a factor of two to five, occasionally more.

V2G (Vehicle-to-grid) technology will report important benefits for the operation and safety of the grid. In order to facilitate the expansion of the V2G technology in a future, it is recommended to offer the drivers with easy to use methods to charge and discharge their EV batteries. In this sense, wireless chargers are expected to play a relevant role in the future electrical networks as it reduces the users intervention. The development of this kind of system is still open to improve them in terms of their operation, their compliance and their control. An important issue for the evaluation of these systems is the efficiency, which measures the power losses occurring in the system. This paper addresses a deep study about the losses in a bidirectional wireless charger. Then, it provides with a mathematical model to characterize them. This model is validated by means of experimental results conducted in a 3.7-kW prototype.

Data assimilation methods are an invaluable tool for operational ocean models. These methods are often based on a variational approach and require the knowledge of the spatial covariances of the background errors (differences between the numerical model and the true values) and the observation errors (differences between true and measured values). Since the true values are never known in practice, the error covariance matrices containing values of the covariance functions at different locations, are estimated approximately. Several methods have been devised to compute these matrices, one of the most widely used is the one developed by Hollingsworth and Lönnberg (H-L). This method requires to bin (combine) the data points separated by similar distances, compute covariances in each bin and then to find a best fit covariance function. While being a helpful tool, the H-L method has its limitations. We have developed a new mathematical method for computing the background and observation error covariance functions and therefore the error covariance matrices. The method uses functional analysis which allows to overcome some shortcomings of the H-L method, for example, the assumption of statistical isotropy. It also eliminates the intermediate steps used in the H-L method such as binning the innovations (differences between observations and the model), and the computation of innovation covariances for each bin, before the best-fit curve can be found. We show that the new method works in situations where the standard H-L method experiences difficulties, especially when observations are scarce. It gives a better estimate than the H-L in a synthetic idealised case where the true covariance function is known. We also demonstrate that in many cases the new method allows to use the separable convolution mathematical algorithm to increase the computational speed significantly, up to an order of magnitude. The Projection Method (PROM) also allows computing 2D and 3D covariance functions in addition to the standard 1D case.

Abstract Outflow resistance in the aqueous drainage tract distal to trabecular meshwork is potentially an important determinant of intraocular pressure and success of trabecular bypass glaucoma surgeries. It is unclear how distal resistance is modulated. We sought to establish: (a) multimodal 2-photon deep tissue imaging and 3-dimensional analysis of the distal aqueous drainage tract (DT) in transgenic mice in vivo and ex vivo; (b) criteria for distinguishing the DT from blood and lymphatic vessels; and (c) presence of a DT wall organization capable of contractility. DT lumen appeared as scleral collagen second harmonic generation signal voids that could be traced back to Schlemm’s canal. DT endothelium was Prox1-positive, CD31-positive and LYVE-1-negative, bearing a different molecular signature from blood and true lymphatic vessels. DT walls showed prominent filamentous actin (F-actin) labeling reflecting cells in a contracted state. F-actin co-localized with mesenchymal smooth muscle epitopes of alpha-smooth muscle actin, caldesmon and calponin, which localized adjacent and external to the endothelium. Our findings support a DT wall organization resembling that of blood vessels. This reflects a capacity to contract and support dynamic alteration of DT caliber and resistance analogous to the role of blood vessel tone in regulating blood flow.

Identifying and annotating functional elements in the human genome remains a challenging but important task. Here the authors propose a priority annotation score to rank identifications and suggest how proteogenomics evidence can be interpreted and what additional information substantiates protein-coding potential for annotation.

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

In this paper, a real time sliding mode control scheme for a variable speed wind turbine that incorporates a doubly feed induction generator is described. In this design, the so-called vector control theory is applied, in order to simplify the system electrical equations. The proposed control scheme involves a low computational cost and therefore can be implemented in real-time applications using a low cost Digital Signal Processor (DSP). The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. A new experimental platform has been designed and constructed in order to analyze the real-time performance of the proposed controller in a real system. Finally, the experimental validation carried out in the experimental platform shows; on the one hand that the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to the uncertainties that usually appear in the real systems.

Wave power conversion systems are nonlinear dynamical systems that must endure strong uncertainties. Efficiency is a key issue for these systems, and the application of robust control algorithms can improve it considerably. Wave power generation plants are typically built using variable speed generators, such as the doubly fed induction generator (DFIG). These generators, compared with fixed speed generators, are very versatile since the turbine speed may be adjusted to improve the efficiency of the whole system. Nevertheless, a suitable speed controller is required for these systems, which must be able to avoid the stalling phenomenon and track the optimal reference for the turbine. This paper proposes a sliding mode control scheme aimed at oscillating water column (OWC) generation plants using Wells turbines and DFIGs. The contributions of the paper are (1) an adaptive sliding mode control scheme that does not require calculating the bounds of the system uncertainties, (2) a Lyapunov analysis of stability for the control algorithm against system uncertainties and disturbances, and (3) a validation of the proposed control scheme through several simulation examples with the Matlab/Simulink suite. The performance results, obtained by means of simulations, for a wave power generation plant (1) evidence that this control scheme improves the power generation of the system and (2) prove that this control scheme is robust in the presence of disturbances.

The initial condition for the simulation of a seismically-induced tsunami for a rapid, assumed instantaneous, vertical seafloor displacement is given by the Kajiura low-pass filter integral. This work proposes a new efficient and accurate approach for its numerical evaluation, valid when the sea floor displacement is discretized as a set of rectangular contributions. We compare several truncated quadrature formulae, selecting the optimal one. We verify that we can satisfactorily approximate the initial sea level perturbation as a linear combination of those induced by the elementary sea floor displacements. The methodology is tested on the tsunamigenic Kuril earthquake doublet – a megathrust and an outer-rise – occurred in the Central Kuril Islands in late 2006 and early 2007. We also confirm the importance of the horizontal contribution to the tsunami generation and we consider a simple model of the inelastic deformation of the wedge, on a realistic bathymetry. The proposed approach results accurate and fast enough to be considered relevant for practical applications, and a tool is provided to create tsunami unit source databases for a given region of interest. [ABSTRACT FROM AUTHOR]

IntroductionAlthough cardiovascular surgery societies in Europe and the USA constantly strive for the exchange of knowledge and best practices in coronary artery bypass grafting (CABG), the available evidence on whether such efforts result in similar patient outcomes is limited. Therefore, in the present analysis, we sought to compare patient profiles and overall survival outcomes for up to 3 years between large European and US patient cohorts who underwent isolated CABG.MethodsPatients from the European DuraGraft Registry (n = 2,522) who underwent isolated CABG at 45 sites in eight different European countries between 2016 and 2019 were compared to randomly selected patients from the US STS database who were operated during the same period (n = 294,725). Free conduits (venous and arterial grafts) from the DuraGraft Registry patients were intraoperatively stored in DuraGraft, an endothelial damage inhibitor, before anastomosis, whereas grafts from the STS Registry patients in standard-of-care solutions (e.g., saline). Propensity score matching (PSM) models were used to account for differences in patient baseline and surgical characteristics, using a primary PSM with 35 variables (2,400 patients matched) and a secondary PSM with 25 variables (2,522 patients matched, sensitivity analysis). The overall survival for up to 3 years after CABG was assessed as the primary endpoint.ResultsThe comparison of patient profiles showed significant differences between the European and US cohorts. The European patients had more left main disease, underwent more off-pump CABG, and received more arterial grafts together with more complete arterial grafting procedures. In contrast, the US patients received more distal anastomoses with more saphenous vein grafts (SVGs) that were mainly harvested endoscopically. Such differences, however, were well balanced after PSM for the mortality comparison. Mortality comparison at 30 days, 12 months, and 24 months between the European and US patients was 2.38% vs. 1.96%, 4.32% vs. 4.79%, and 5.38% vs. 6.96%, respectively. At 36 months, the mortality was significantly lower in the European patients than that of their US counterparts (7.37% vs. 9.65%; p-value = 0.016). The estimated hazard ratio (HR) was 1.29 (95% CI 1.05–1.59).ConclusionThis large-scale transatlantic comparative analysis shows that there are some significant differences in patient profiles between large cohorts of European and US patients. These differences were adjusted by using PSM for the mortality analysis. No significant difference in mortality was detected between groups through 2 years, but survival was significantly better in the European DuraGraft Registry patients at 3 years post-CABG.