Continuous Tracking Research Articles

Separation of ventilation and perfusion of electrical impedance tomography image streams using multi-dimensional ensemble empirical mode decomposition

Objective. In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach. This study analyses the potential of Multi-dimensional Ensemble Empirical Mode Decomposition by application of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results. In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on the Root Mean Square Error between the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland–Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance. Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

Live-Cell Imaging of MicroRNA Expression via Photoinduced Electron Transfer Controlled by Catalytic Hairpin Assembly.

MicroRNAs (miRNAs) serve as emerging biomarkers for a range of diseases, and their quantitative analysis draws increasing attention. Yet, current invasive methods limit continuous tracking within living cells. To overcome this, a nonenzymatic DNA-based nanoprobe is developed for dynamic, noninvasive miRNA tracking via live-cell imaging. This probe features a unique hairpin DNA structure with five guanines that act as internal quenchers, suppressing fluorescence from an attached fluorophore via photoinduced electron transfer. Target miRNA initiates toehold-mediated strand displacement, restoring, and amplifying the fluorescence signal. Additionally, by introducing a single mismatch to the hairpin DNA, the nanoprobe's sensitivity is significantly enhanced, lowering the detection limit to about 60 pM without compromising specificity. To optimize intracellular delivery for prolonged monitoring, the nanoprobe is encapsulated within multilamellar lipid nanovesicles, fluorescently labeled for dual-wavelength ratiometric analysis. The proposed nanoprobe demonstrates a significant advance in live-cell miRNA detection, promising enhanced in situ analysis for a better understanding of miRNAs' pathophysiological function.

Off-plane technique ultrasound-guided pericardiocentesis via anterior approach: short communication.

The pericardiocentesis procedure is common, often performed via the subxiphoid approach, although other transthoracic approaches have been described. This short communication describes an off-plane technique ultrasound-guided pericardiocentesis using an anterior approach, performed using a linear transducer and guided in real-time by ultrasound, offering the advantage of continuous needle tracking to reduce complications associated with this approach such as pneumothorax, inadvertent cardiac puncture, and injury to the left internal mammary artery (LIMA).

Alkyl Chains Tune Molecular Orientations to Enable Dual Passivation in Inverted Perovskite Solar Cells.

Nonradiative recombination losses occurring at the interface pose a significant obstacle to achieve high-efficiency perovskite solar cells (PSCs), particularly in inverted PSCs. Passivating surface defects using molecules with different functional groups represents one of the key strategies for enhancing PSCs efficiency. However, a lack of insight into the passivation orientation of molecules on the surface is a challenge for rational molecular design. In this study, aminothiol hydrochlorides with different alkyl chains but identical electron-donating (-SH) and electron-withdrawing (-NH3 +) groups were employed to investigate the interplay between molecular structure, orientation, and interaction on perovskite surface. The 2-Aminoethane-1-thiol hydrochloride with shorter alkyl chains exhibited a preference of parallel orientations, which facilitating stronger interactions with the surface defects through strong coordination and hydrogen bonding. The resultant perovskite films following defect passivation demonstrate reduced ion migration, inhibition of nonradiative recombination, and more n-type characteristics for efficient electron transfer. Consequently, an impressive power conversion efficiency of 25 % was achieved, maintaining 95 % of its initial efficiency after 500 hours of continuous maximum power point tracking.

Alkyl Chains Tune Molecular Orientations to Enable Dual Passivation in Inverted Perovskite Solar Cells

AbstractNonradiative recombination losses occurring at the interface pose a significant obstacle to achieve high‐efficiency perovskite solar cells (PSCs), particularly in inverted PSCs. Passivating surface defects using molecules with different functional groups represents one of the key strategies for enhancing PSCs efficiency. However, a lack of insight into the passivation orientation of molecules on the surface is a challenge for rational molecular design. In this study, aminothiol hydrochlorides with different alkyl chains but identical electron‐donating (−SH) and electron‐withdrawing (−NH3+) groups were employed to investigate the interplay between molecular structure, orientation, and interaction on perovskite surface. The 2‐Aminoethane‐1‐thiol hydrochloride with shorter alkyl chains exhibited a preference of parallel orientations, which facilitating stronger interactions with the surface defects through strong coordination and hydrogen bonding. The resultant perovskite films following defect passivation demonstrate reduced ion migration, inhibition of nonradiative recombination, and more n‐type characteristics for efficient electron transfer. Consequently, an impressive power conversion efficiency of 25 % was achieved, maintaining 95 % of its initial efficiency after 500 hours of continuous maximum power point tracking.

Proof of concept: real-time viability and metabolic profiling of probiotics with isothermal microcalorimetry.

Isothermal microcalorimetry (IMC) is a potent analytical method for the real-time assessment of microbial metabolic activity, which serves as an indicator of microbial viability. This approach is highly relevant to the fields of probiotics and Live Biotherapeutic Products (LBPs), offering insights into microbial viability and growth kinetics. One important characteristic of IMC is its ability to measure microbial metabolic activity separately from cellular enumeration. This is particularly useful in situations where continuous tracking of bacterial activity is challenging. The focus on metabolic activity significantly benefits both probiotic research and industrial microbiology applications. IMC's versatility in handling different media matrices allows for the implementation of viability assessments under conditions that mirror those found in various industrial environments or biological models. In our study, we provide a proof of concept for the application of IMC in determining viability and growth dynamics and their correlation with bacterial count in probiotic organisms. Our findings reinforce the potential of IMC as a key method for process enhancement and accurate strain characterization within the probiotic sector. This supports the broader objective of refining the systematic approach and methods used during the development process, thereby providing detailed insights into probiotics and LBPs.

The surface morphologies and internal pores evolution of a CuSn10 alloy fabricated by laser powder bed fusion

The fabrication of high quality copper alloys using laser powder bed fusion (LPBF) technology remains challenging due to their low laser absorption and high thermal conductivity. LPBF often involves intrinsic defects, such as rough surface and internal pores, which largely deteriorate the performance of as-built copper alloy components. Here we studied the effect of scanning speed on the evolution of surface morphologies and internal pores of LPBF-fabricated CuSn10 alloy. The melt flow behaviors were studied by both experiments and in situ observations where combined a high-speed camera with an infrared thermal imager. With increasing the scanning speed, the morphology of melt track evolves from a continuous track to a discontinuous one, whereas the internal defects change from a great number of spherical pores to many irregular pores. A higher scanning speed decreases the peak temperature of the melt pool and causes less overlaps between neighboring melt tracks, thus deteriorating wetting behaviors. It is expected that the obtained results are beneficial for guiding the additive manufacturing of copper alloy parts with high metallurgical quality and thus promoting their industrial applications.

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

Performance Characteristics of the NeuroEXPLORER, a Next-Generation Human Brain PET/CT Imager.

The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The system's performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

Silver coordination-induced n-doping of PCBM for stable and efficient inverted perovskite solar cells

The bidirectional migration of halides and silver causes irreversible chemical corrosion to the electrodes and perovskite layer, affecting long-term operation stability of perovskite solar cells. Here we propose a silver coordination-induced n-doping of [6,6]-phenyl-C61-butyric acid methyl ester strategy to safeguard Ag electrode against corrosion and impede the migration of iodine within the PSCs. Meanwhile, the coordination between DCBP and silver induces n-doping in the PCBM layer, accelerating electron extraction from the perovskite layer. The resultant PSCs demonstrate an efficiency of 26.03% (certified 25.51%) with a minimal non-radiative voltage loss of 126 mV. The PCE of resulting devices retain 95% of their initial value after 2500 h of continuous maximum power point tracking under one-sun irradiation, and > 90% of their initial value even after 1500 h of accelerated aging at 85 °C and 85% relative humidity.

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

The performance of interacting metal surfaces having sliding motion is enhanced by fabricating microfeatures on-top of the surfaces. Laser powder bed fusion (LPBF) is used to fabricate the microfeatures of IN625 on-top of a flat surface. In LPBF, geometrical characteristics of microfeatures are based on the energy supplied and the volume of material melted (layer thickness) at that instant of time. There is very few research reported in open literature on the effect of layer thickness on microfeatures geometry during LPBF. In this work, a series of single tracks is fabricated with different combinations of laser power and scanning speed for 75 μm, 100 μm, and 125 μm layer thickness and its effect on single track geometrical characteristics and microstructure was evaluated. It has been found that laser power and scanning speed have a significant influence on the track geometry irrespective of layer thickness. The minimum linear energy density of 2.5 J/mm is considered to fabricate the continuous track formation for investigated layer thickness. From statistical analysis, it is observed that the laser power and scanning speed shows higher contribution on track width and remelting depth, respectively. Whereas, layer thickness shows a higher influence on track height and contact angle of single track as compared to laser power and scanning speed. In 125 μm layer thickness, the volume of material melting is increased which encourages the gravity effect to deform the melt pool and produces larger track width compare to 100 μm layer thickness. Further, the cell spacing is gradually increased with layer thickness resulting increase in the solidification period. The shape controllability of the single track is significantly increased with layer thickness, but as keeps on increasing layer thickness due to the larger volume of molten metal, the melt pool losses ability to maintain convex shapes. An Artificial Neural Network (ANN) model is developed to understand the relationship between the LPBF process parameters and single track geometrical characteristics to predict the responses for a wide range of process parameters. The multiple response neural network model shows excellent agreement with experimental results with R2 value greater than 0.95.

Data Science in Cybersecurity to Detect Malware-Based Domain Generation Algorithm: Improvement, Challenges, and Prospects

Nowadays, the malware communicates with command and control servers using domains generated algorithmically. Domain generation algorithms ( DGAs) are continually evolving, which degrades the accuracy of the existing methods calls for the continuous tracking of how DGAs develop and their detection methods and calls for a good evaluation of the stage to open horizons for new detection methods. Data science plays a key role in cybersecurity by providing methods for detecting and analyzing network traffic data, including DGAs, and helping to improve the overall security of computer systems and networks. It can also be used to analyze large datasets of domain names and to develop and optimize solutions for DGA detection, by applying techniques such as machine learning, deep learning, and genetic algorithms, which have shown their effectiveness in detecting new and unknown DGAs. This paper reviews the role of data science in cybersecurity systems to detect DGAs. Hence, it also brings together publicly available domain name datasets and data science techniques utilized in recent DGA detection systems to highlight current issues and potential directions. This article additionally explains issues related to DGA detection. This will assist researchers in improving the current DGA detection algorithms as well as creating new powerful models.

Opportunistic screening for long-term muscle wasting in critically ill patients: insights from an acute pancreatitis cohort.

To assess the feasibility of long-term muscle monitoring, we implemented an AI-guided segmentation approach on clinically indicated Computed Tomography (CT) examinations conducted throughout the hospitalization period of patients admitted to the intensive care unit (ICU) with acute pancreatitis (AP). In addition, we aimed to investigate the potential of muscle monitoring for early detection of patients at nutritional risk and those experiencing adverse outcomes. This cohort served as a model for potential integration into clinical practice. Retrospective cohort study including 100 patients suffering from AP that underwent a minimum of three CT scans during hospitalization, totaling 749 assessments. Sequential segmentation of psoas muscle area (PMA) was performed and was relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan was calculated. Subgroup and outcome analyses were performed including ANOVA. Discriminatory power of muscle decay rates was evaluated using ROC analysis. Monitoring PMA decay revealed significant long-term losses of 48.20% throughout the hospitalization period, with an average daily decline of 0.98%. Loss rates diverged significantly between survival groups, with 1.34% PMA decay per day among non-survivors vs. 0.74% in survivors. Overweight patients exhibited significantly higher total PMA losses (52.53 vs. 42.91%; p = 0.02) and average PMA loss per day (of 1.13 vs. 0.80%; p = 0.039). The first and the maximum decay rate, in average available after 6.16 and 17.03days after ICU admission, showed convincing discriminatory power for survival in ROC analysis (AUC 0.607 and 0.718). Both thresholds for maximum loss (at 3.23% decay per day) and for the initial loss rate (at 1.98% per day) proved to be significant predictors of mortality. The innovative AI-based PMA segmentation method proved robust and effortless, enabling the first comprehensive assessment of muscle wasting in a large cohort of intensive care pancreatitis patients. Findings revealed significant muscle wasting (48.20% on average), particularly notable in overweight individuals. Higher rates of initial and maximum muscle loss, detectable early, correlated strongly with survival. Integrating this tool into routine clinical practice will enable continuous muscle status tracking and early identification of those at risk for unfavorable outcomes.

Adaptive SARIMA modelling for continuous chamber temperature tracking in ultra-low temperature freezers

Ultra-low temperature (ULT) freezers are vital for storing perishable biological materials, requiring continuous monitoring of chamber thermal conditions to ensure sample integrity. A reliable dynamic data-driven model is key for smart proactive surveillance of the ULT freezers through real-time status tracking. However, the dynamic nature of ULT freezers, influenced by external disturbances, demands adaptive models to sustain performance. Complex models thus face challenges due to time-consuming parameter updates. To ensure model prediction performance while minimising model updating time, this study proposes a Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based approach for short-term ULT freezer chamber temperature prediction. This method solely relies on historical chamber temperature data, eliminating the need for additional measurements. Moreover, two adaptive duty cycle length (DCL) selection strategies and a one-step ahead DCL estimation method are developed to address auto-correlation issues caused by recurring duty cycles. Comparative analyses with reference models demonstrate that the SARIMA model, incorporating future DCL effects, excels in one-step prediction and multi-step forecast under stable and variant DCL periods. Moreover, it outperforms the reference models in auto-correlation representation, affirming its reliability in predicting future states. The proposed modelling approach promises an effective method for freezing chamber temperature prediction, enabling continuous status tracking and early fault detection. Moreover, it holds great potential for modelling other critical temperatures in refrigeration systems and facilitating flexible energy operations.

A Long-Term Video Tracking Method for Group-Housed Pigs

Pig tracking provides strong support for refined management in pig farms. However, long and continuous multi-pig tracking is still extremely challenging due to occlusion, distortion, and motion blurring in real farming scenarios. This study proposes a long-term video tracking method for group-housed pigs based on improved StrongSORT, which can significantly improve the performance of pig tracking in production scenarios. In addition, this research constructs a 24 h pig tracking video dataset, providing a basis for exploring the effectiveness of long-term tracking algorithms. For object detection, a lightweight pig detection network, YOLO v7-tiny_Pig, improved based on YOLO v7-tiny, is proposed to reduce model parameters and improve detection speed. To address the target association problem, the trajectory management method of StrongSORT is optimized according to the characteristics of the pig tracking task to reduce the tracking identity (ID) switching and improve the stability of the algorithm. The experimental results show that YOLO v7-tiny_Pig ensures detection applicability while reducing parameters by 36.7% compared to YOLO v7-tiny and achieving an average video detection speed of 435 frames per second. In terms of pig tracking, Higher-Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTP), and Identification F1 (IDF1) scores reach 83.16%, 97.6%, and 91.42%, respectively. Compared with the original StrongSORT algorithm, HOTA and IDF1 are improved by 6.19% and 10.89%, respectively, and Identity Switch (IDSW) is reduced by 69%. Our algorithm can achieve the continuous tracking of pigs in real scenarios for up to 24 h. This method provides technical support for non-contact pig automatic monitoring.

A review of investigation of the metabolic rate effects on human thermal comfort

The precise analysis of the usage of thermal indices to evaluate and create an acceptable space is a research hotspot as thermal comfort increasingly comes to be the center of attention. The accurate measurement and calculation of the metabolic rate should be a top priority in research because it is the most important component of thermal indexmodels. This study includes a literature survey of the relevant fields to properly comprehend the present developments and flaws in calculating the metabolic rate. The ultimate objective is to apply the growth of human metabolic science to logical urban development. First, the interaction between the thermal environment and thermal comfort was analyzed, and then, the influence of the human metabolic rate on thermal comfort was discussed. The results show that the dynamic change of metabolic rate is the key human factor that directly affects the thermal regulation, and indirectly affects the evaluation accuracy of the thermal evaluation index standard. Through an inductive analysis of metabolic rate measurement methods that are widely used in various environments, this study identifies several issues with the current utilization of portable metabolic monitoring instruments in dynamic thermal comfort research, highlighting their limitations in addressing accuracy, portability, economy, and other relevant aspects. An essential future effort in this study path will be the continued research and development of a more portable, more affordable, more direct, and more efficient customized metabolic rate monitoring equipment. In addition, the fact that metabolic rate is represented as a constant or its impact on thermal comfort as a single factor in existing thermal index models also frequently results in incorrect findings in outdoor research. The fundamental condition for enhancing thermal comfort index forecast accuracy is accurate and continuous tracking of metabolic rate.

Optimizing Camera Motion with MCTS and Target Motion Modeling in Multi-Target Active Object Tracking

In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is to achieve continuous tracking of targets through real-time control of camera. This form of active camera control can be applied to unmanned aerial vehicles (UAV), intelligent robots, and sports events. Our work is conducted in an environment featuring multiple cameras and targets, where our goal is to maximize target coverage. Contrasting with previous research, our work introduces additional degrees of freedom for the cameras, allowing them not only to rotate but also to move along boundary lines. In addition, we model the motion of target to predict the future position of the target in environment. With target’s future position, we use Monte Carlo Tree Search (MCTS) method to find the optimal action of camera. Since the action space is large, we propose to leverage the action selection from multi-agent reinforcement learning (MARL) network to prune the search tree of Monte Carlo Tree Search method, so as to find the optimal action more efficiently. We establish a multi-target 2D environment to simulate several sports games, and experimental results demonstrate that our method can effectively improve the target coverage. The code is available at: http://github.com/HopeChanger/ActiveObjectTracking .

Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.

Image differences between the eyes can cause interocular discrepancies in the speed of visual processing. Millisecond-scale differences in visual processing speed can cause dramatic misperceptions of the depth and three-dimensional direction of moving objects. Here, we develop a monocular and binocular continuous target-tracking psychophysics paradigm that can quantify such tiny differences in visual processing speed. Human observers continuously tracked a target undergoing Brownian motion with a range of luminance levels in each eye. Suitable analyses recover the time course of the visuomotor response in each condition, the dependence of visual processing speed on luminance level, and the temporal evolution of processing differences between the eyes. Importantly, using a direct within-observer comparison, we show that continuous target-tracking and traditional forced-choice psychophysical methods provide estimates of interocular delays that agree on average to within a fraction of a millisecond. Thus, visual processing delays are preserved in the movement dynamics of the hand. Finally, we show analytically, and partially confirm experimentally, that differences between the temporal impulse response functions in the two eyes predict how lateral target motion causes misperceptions of motion in depth and associated tracking responses. Because continuous target tracking can accurately recover millisecond-scale differences in visual processing speed and has multiple advantages over traditional psychophysics, it should facilitate the study of temporal processing in the future.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Medical treatment of heart failure with renin–angiotensin–aldosterone system inhibitors and beta-blockers in aortic stenosis: association with long-term outcome after aortic valve replacement

Abstract Aims There is a lack of robust data on the optimal medical treatment of heart failure in patients with severe aortic stenosis, with no randomized controlled trials guiding treatment. The study aimed to study the association between exposure to renin–angiotensin–aldosterone system (RAS) inhibitors or beta-blockers and outcome after aortic valve replacement in patients with aortic stenosis and heart failure. Methods and results The study included all patients with heart failure undergoing aortic valve replacement for aortic stenosis in Sweden between 2008 and 2016 (n = 4668 patients). Exposure to treatment was assessed by a continuous tracking of drug dispensations, and outcome events were all-cause mortality and hospitalization for heart failure collected from national patient registries. After adjustment for age, sex, atrial fibrillation, hypertension, diabetes mellitus, and prior myocardial infarction, Cox regression analysis showed that RAS inhibition was associated with a lower risk of all-cause mortality in patients with reduced left ventricular ejection fraction (LV-EF) [hazard ratio (HR) 0.58, 95% confidence interval (CI) 0.51–0.65] and preserved LV-EF (HR 0.69, 95% CI 0.56–0.85). Beta-blockade was associated with a lower risk of all-cause mortality in patients with reduced LV-EF (HR 0.81, 95% CI 0.71–0.92), but not in preserved LV-EF (HR 0.87, 95% CI 0.69–1.10). There was no association between RAS inhibition or beta-blockade and the risk of hospitalization for heart failure. Conclusion The RAS inhibition was associated with a lower all-cause mortality after valve replacement in patients with both reduced and preserved LV-EF. Beta-blockade was associated with lower all-cause mortality only in patients with reduced LV-EF.