Accuracy Of Sensor Research Articles

Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation.

Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery. We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL). We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%. Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.

Research about functions, fabrications and applications of the microstructures in sensors

Microstructures in sensors are of immense importance in technology and scientific research. Microstructures play a vital role in achieving enhanced sensitivity, accuracy, and miniaturization in sensors. Understanding their importance is crucial for advancing sensor technology in healthcare, environmental monitoring, robotics, and other fields. To conduct this research, a systematic review of academic and industry sources is performed. Existing studies, experimental techniques, and real-world applications are analyzed. The paper covers diverse topics related to microstructures in sensors. It highlights the importance of microstructures, explores types such as Micro-electromechanical Systems (MEMS), and nanomaterials, and discusses their unique characteristics. Fabrication techniques like lithography, thin-film deposition, and additive manufacturing are examined. In addition, the advantages and limitations of each method are discussed, emphasizing manufacturing challenges and opportunities. Additionally, the paper explores applications of microstructures in biomedical sensing, environmental monitoring, consumer electronics, and industrial automation. Existing research and success stories showcase their potential to revolutionize various sectors.

Achieving the Rewards of Smart Agriculture

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, “smart” technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. “Smart Agriculture/farming” (SA) or “Digital Agriculture” (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA’s ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users’ facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language.

In Situ Growth of Conductive Metal-Organic Framework onto Cu2O for Highly Selective and Humidity-Independent Hydrogen Sulfide Detection in Food Quality Assessment.

The sensitivity of chemiresistive gas sensors based on metal oxide semiconductors (MOSs) has been inherently affected by ambient humidity because their reactive oxygen species are easily hydroxylated by water molecules, which significantly reduces the accuracy of the gas sensors in food quality assessment. Although conventional metal organic frameworks (MOFs) can serve as coatings for MOSs for humidity-independent gas detection, they have to operate at high working temperatures due to their low or nonconductivity, resulting in high power consumption, significant manufacturing inconvenience, and short-term stability due to the oxidation of MOFs. Here, the conductive and thickness-controlled CuHHTP (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene)-coated Cu2O are developed by combining in situ etching and layer-by-layer liquid-phase growth method, which achieves humidity-independent detection of H2S at room temperature. The response to H2S only decreases by 2.6% below 75% relative humidity (RH), showing a 9.6-fold improvement than the bare Cu2O sensor, which is ascribed to the fact that the CuHHTP layer hinders the adsorption of water molecules. Finally, a portable alarm system is developed to monitor food quality by tracking released H2S. Compared with gas chromatography method, their relative error is within 9.4%, indicating a great potential for food quality assessment.

Analysis of Patient Health Using Arduino and Monitoring System

The proposed project is a health monitoring system using Arduino that measures key health parameters such as heart rate, blood oxygen level, and body temperature. The system consists of sensors such as DHT11 for temperature, MAX30100 for heart rate and blood oxygen level, and an Arduino Nano for processing the data. The measured results are displayed on an LCD screen and a buzzer sounds when the results are ready. The system has been designed with simplicity and ease-of-use in mind, making it accessible for personal use. While the system has limitations such as sensor accuracy and lack of IoT functionality, it still has potential for improving individual health monitoring and wellness. In the future, potential areas for development include the inclusion of more sensors, IoT functionality, and machine learning algorithms for personalized insights and recommendations. Overall, the proposed health monitoring system is a promising step towards empowering individuals to take a more active role in monitoring their health and wellness.

Computational design of ultra-robust strain sensors for soft robot perception and autonomy

Compliant strain sensors are crucial for soft robots’ perception and autonomy. However, their deformable bodies and dynamic actuation pose challenges in predictive sensor manufacturing and long-term robustness. This necessitates accurate sensor modelling and well-controlled sensor structural changes under strain. Here, we present a computational sensor design featuring a programmed crack array within micro-crumples strategy. By controlling the user-defined structure, the sensing performance becomes highly tunable and can be accurately modelled by physical models. Moreover, they maintain robust responsiveness under various demanding conditions including noise interruptions (50% strain), intermittent cyclic loadings (100,000 cycles), and dynamic frequencies (0–23 Hz), satisfying soft robots of diverse scaling from macro to micro. Finally, machine intelligence is applied to a sensor-integrated origami robot, enabling robotic trajectory prediction (<4% error) and topographical altitude awareness (<10% error). This strategy holds promise for advancing soft robotic capabilities in exploration, rescue operations, and swarming behaviors in complex environments.

Modeling of Sports Training Simulation Based on Energy Harvesting in Wireless Sensor Networks

For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.

A Flexible Wearable Sensor Based on Laser-Induced Graphene for High-Precision Fine Motion Capture for Pilots.

There has been a significant shift in research focus in recent years toward laser-induced graphene (LIG), which is a high-performance material with immense potential for use in energy storage, ultrahydrophobic water applications, and electronic devices. In particular, LIG has demonstrated considerable potential in the field of high-precision human motion posture capture using flexible sensing materials. In this study, we investigated the surface morphology evolution and performance of LIG formed by varying the laser energy accumulation times. Further, to capture human motion posture, we evaluated the performance of highly accurate flexible wearable sensors based on LIG. The experimental results showed that the sensors prepared using LIG exhibited exceptional flexibility and mechanical performance when the laser energy accumulation was optimized three times. They exhibited remarkable attributes, such as high sensitivity (~41.4), a low detection limit (0.05%), a rapid time response (response time of ~150 ms; relaxation time of ~100 ms), and excellent response stability even after 2000 s at a strain of 1.0% or 8.0%. These findings unequivocally show that flexible wearable sensors based on LIG have significant potential for capturing human motion posture, wrist pulse rates, and eye blinking patterns. Moreover, the sensors can capture various physiological signals for pilots to provide real-time capturing.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Calibration of accelerometer and magnetometer sensors of MWD systems in directional drilling application

Measurement while drilling (MWD) tools include accelerometers, magnetometers and temperature sensors. The accuracy of these sensors are highly important in measuring inclination and Azimuth angle parameters in a directional well. These factors can increase costs when drilling a directional well. During the past years, many efforts have been made to increase the accuracy of these tools. Accuracy of accelerometer and magnetometer sensors are influenced by scale, bias and non-orthogonal errors. Considering that MWD systems work in a wide range of temperatures (for example 0 °C–150 °C), the temperature parameter should also be added to the factors affecting the errors. Here, after making a sample of MWD tool, a method to calibrate the tool using mathematical model based on total field calibration algorithm, least square method and the transfer matrix is presented. Calibrated parameters at ambient temperature are calculated with acceptable speed. Then, a method for calculating temperature coefficients in the desired range is provided. Compared to the case where temperature coefficients are not considered, the obtained results show a significantly increased accuracy of Inclination and Azimuth parameters through the use of these coefficients.

Physical activity intensity classification during activities of daily living in older adults using accelerometers: Is the ear the new wrist?

Introduction&#x0D; Accurate measurement of physical activity (PA) in older adults is important, both in health research and personalized prevention. Accelerometers, used to overcome the limitations of self-reporting, were initially worn on the hips, but are increasingly worn on the non-dominant wrist. While this can improve wear compliance, the accuracy of PA intensity classification can be compromised. Given the high prevalence of mild to severe hearing loss in the older population, this study explores a novel approach: integrating an accelerometer into a hearing aid (ear sensor). We aimed to assess its accuracy and compare it to research-grade sensors worn at different locations.&#x0D; Methods 60 middle-aged to older adults (64.0 ± 8.0 years, 48% women) were included in this study. Each subject performed 12-13 different activities, which were pseudo-randomly selected from a list of 33 activities of daily living. Each activity lasted 8 min and included sedentary activities (e.g., lying, playing cards) low-intensity activities (e.g., hanging laundry), activities of changing intensity or without physical displacement (e.g., yoga, squats), indoor activities related to locomotion (e.g., walking, running), outdoor activities (e.g., walking uphill, cycling), and activities with aids (e.g., walking with a stroller). Oxygen consumption was measured via indirect calorimetry and used to classify activity intensity into sedentary behavior (SB, metabolic equivalent of task [MET] &lt; 1.5), light intensity PA (LPA, 1.5 ≤ MET &lt; 3.0), or moderate to vigorous intensity PA (MVPA, MET ≥ 3.0). The ear sensor was placed behind the left ear, while the research-grade sensors were placed on both wrists and ankles, on the hip, chest, and forehead. Estimation of PA intensity classes was done using mean amplitude deviations and ROC analyses. Contingency tables were used to determine classification accuracy.&#x0D; Results&#x0D; Overall accuracy of the ear sensor was 82.6%, performing better than both wrists (left 81.1%, right 76.0%) and both ankles (left 81.1%, right 81.9%), but worse than the forehead (83.6%), hip (85.6%) and the chest (85.9%). ROC analyses show that all sensors can effectively discriminate between sedentary vs. non-sedentary activities (AUC 0.97-0.98, exception ankles: AUC 0.95-0.96) and between MVPA vs. other (AUC 0.96-0.97, exception wrists: AUC 0.89-0.92).&#x0D; Discussion/Conclusion&#x0D; This study is the first to show that an accelerometer integrated into a hearing aid can accurately classify PA intensity and differentiate MVPA and sedentary behavior in older adults. It also confirms previous investigations showing that wrist-worn sensors – although increasingly being used to monitor PA – are less effective in capturing MVPA compared to sensors worn closer to the center of mass (including the head/ear in our study). Although the optimal wear site in older adults is a subject of ongoing debate, our data shows that a sensor integrated into a hearing aid offers a promising balance of classification accuracy and (possibly) user compliance. Further studies should explore integrating in-ear heart rate monitoring to enhance accuracy even further.

Enhancing the recognition accuracy of tactile sensor through electrode and triboelectric material interface structure management strategy

As each material has various capabilities to lose or gain electrons, unique triboelectric signals are generated when the Triboelectric nanogenerator (TENG) device touches different objects. Thus, the device is widely used in tactile sensors for material identification. At present, the improvement of identification accuracy is solved from the perspective of designing sensor structure and working mode. Here, we explore an electrode/triboelectric material interface structure management strategy that can improve sensor output performance and identification accuracy. It would facilitate the formation of [Cu(NH3)4]2+ ligands along {111} crystal plane when the copper sheet was immersed in ammonia solution, resulting in the establishment of rough surface, corresponding surface roughness is enhanced from 1.506 µm to 3.441 µm. The as-fabricated TENG device is mainly composed of two parts: an electrode material (ammonia-etched copper sheet) and a triboelectric layer (polydimethylsiloxane (PDMS) layer) embedded in etched holes. Significantly, the triboelectric signal relates strongly to the specific electrode/PDMS interface structure and target material surface charge condition, establishing a rough PDMS/electrode interface can increase the maximum voltage by 40–80% and the transferred charges by 3–180% under different stresses. With the help of machine learning, the construction of a electrode/PDMS interface structure could further enhance accuracy rate from 98.3% to 99.6%. Meanwhile, a material perception system integrated with a TENG-based sensor, data processing and display modules was developed, which can read the real-time triboelectric signal, and an acceptable recognition result can be achieved. This work could extend material perception system to machine intelligence field.

Capacitive Bionic Magnetic Sensors Based on One-Step Biointerface Preparation.

Magnetic biomolecule-based bionic magnetic field sensors are anticipated to open up novel pathways for magnetic field detection. The detection range and accuracy of current bionic magnetic field sensors are limited, and little work is based on the capacitive response principle. We successfully developed a biochemical interface with an extralarge target-receptor size ratio, which can be manufactured in a single step for weak magnetic field detection across a wide frequency range, and we used electrochemical capacitance as a magnetic field change conduction strategy. The thickness-controllable nanoscale bovine serum albumin/graphene layer on an indium tin oxide working electrode combines with the one-step preparation method to immobilize the MagR/Cry4 complex. This capacitive bionic magnetic sensor can achieve the detection range of 0-120 mT. This biointerface design strategy obtains the further improvement of the performance of this bionic magnetic field sensor. Furthermore, the biointerface construction and optimization methodology in this proposal has potential applications in the design of other medical biosensors.

Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites.

Reliable Temperature Measurement in Radiation-Intensive Environments

Radiation-resistant temperature sensors are vital for ensuring reliability in radiation-intensive environments, where the highly energetic and penetrating nature of radiation can significantly impact electronic devices and sensors. In such environments, like those near intense radiation sources or in challenging radiation-rich settings, such as space, gamma radiation can lead to erroneous measurements or equipment failures. Radiation-resistant sensors play a crucial role in maintaining measurement accuracy as they are designed to minimize interference caused by radiation, protecting electronic components and providing precise and reliable temperature readings. Their resilience to radiation-induced effects ensures data durability, reducing the need for frequent replacements, and enhancing the overall reliability of measurements in these demanding conditions. In this paper, we present and analyze two different configurations, aiming to address the challenges posed by radiation in sensitive environments. By exploring these novel approaches, we seek to enhance the robustness and accuracy of temperature sensors in radiation-intensive settings, enabling reliable data collection and facilitating successful operations in challenging radiation-rich conditions. The comparative analysis of these configurations will shed light on their performance and effectiveness in mitigating radiation-induced effects, thereby contributing to the advancement of radiation-resistant temperature sensing technologies.

RPM MEASUREMENT COMPARISON USING A THERMOMETER AND LM393 MICROCONTROLLER

RPM measurement is a crucial measurement tool for assessing a machine's rotation quality and ensuring that it meets the DC motor manufacturer's specifications. The quality of rotation of electric motors and combustion motors can both be observed through RPM measurements. The precision with which the LM393 sensor measures the rotation of a DC motor engine is the problem formulation in this study. Thus, the purpose of this study is to evaluate the rotation measurement accuracy of the LM393 Microcontroller sensor and compare it to a traditional tachometer. In order to conduct rotational variations on the DC motor, the following voltages will be applied: 0.93V, 2.83V, 4.25V, 7.05V, and 9.07V. Next, a tachometer and an LM393 microcontroller are used to measure the DC motor rotation. The gathered data will be made available for verification. Based on the measurement results, the corresponding data differences are -5.560 RPM, -1.577 RPM, -2.182 RPM, -3 RPM, and 0.334 RPM. Therefore, measurement validation was done based on the disparity in readings, yielding results of 0.252%, -1.45%, 0.12%, 0.038%, and 0.0725%. Based on validation results showing a presentation of no more than 10%, the LM393 measuring tool for DC motor RPM measurement is deemed valid and suitable for use.

An architectural framework of elderly healthcare monitoring and tracking through wearable sensor technologies

The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.

A new SOI sensor design for detecting cancer using hybrid waveguide with higher sensitivity than both strip and slot waveguides

A new highly accurate optical biomedical sensor is proposed for cancer detection characterized by high sensitivity, small footprint, low cost, and low limit of detection. The sensor is based on double-ring resonators made of silicon on insulator. The type of the waveguide is critical in determining the sensor performance. To compromise the advantages and disadvantages of strip and slot waveguides, a mixed design of both has been introduced in literature at the expense of increased footprint compared to traditional sensors. Indeed, almost 27-fold footprint increase is required to improve the sensitivity by only one third of that of slot waveguide’s sensitivity. In this paper, we introduce a new design that keeps the same footprint of traditional sensors, while achieving high sensitivity. This sensitivity depends on the resonance wavelength shift due to different refractive indices of the biosample. It has the value 109.8 nm/RIU compared to 55.57 nm/RIU and 129.621 nm/RIU for strip and slot waveguides, respectively. The hybrid waveguide quality factor is 537.7 while the quality factors of the strip and the slot waveguides are 627.99 and 380.76, respectively. In addition, the new design achieves the minimum limit of detection (0.0255) when compared to that of traditional designs. Furthermore, a new method of detection is proposed with the same design, providing a higher sensitivity over both traditional waveguide types with a value of 15.3, compared to 13.2 and 11.5 for strip and slot waveguides, respectively. In this method, the sensitivity relies on various values of output transmitted light at the same wavelength due to altering the biocell refractive index. The biosensor output equation is developed. In addition, the relationship between the supermodes and the sensitivity is determined at variance conditions. It is found that there is an inverse relation between them.

Twelve-month follow-up results from the SIRONA 2 clinical trial.

In the SIRONA 2 trial, the safety and efficacy of pulmonary artery (PA) pressure (PAP)-guided heart failure (HF) management using a novel PAP sensor were assessed at 30 and 90days, respectively, and both endpoints were met. The current study examines the prespecified secondary endpoints of safety and accuracy of the PA sensor along with HF hospitalizations and mortality, HF symptoms, functional capacity, quality of life, and patient compliance through 12months. SIRONA 2 is a prospective, multi-centre, open-label, single-arm trial evaluating the Cordella™ PA Sensor System in 70 patients with New York Heart Association (NYHA) functional class III HF with a prior HF hospitalization and/or increase of N-terminal pro-brain natriuretic peptide within 12months of enrolment. Sensor accuracy was assessed and compared with measurements obtained by standard right heart catheterization (RHC). Safety was defined as freedom from prespecified adverse events associated with use of the Cordella PA Sensor System and was assessed in all patients who entered the cath lab for PA sensor implant. HF hospitalizations and mortality, HF symptoms, functional capacity, quality of life, and patient compliance were also assessed. At 12months, there was good agreement between the Cordella PA Sensor System and RHC, with the average difference for mean PAP being 2.9±7.3mmHg. The device safety profile was excellent with 98.4% freedom from device/system-related complications. There were no pressure sensor failures. HF hospitalizations and mortality were low with a rate of 0.33 event per patient year. Symptoms as assessed by NYHA (P<0.0001) and functional capacity as measured by 6min walk test (P=0.02) were significantly improved. Patients' adherence to daily transmissions of PAP and vital signs measurements was 95%. Long-term follow-up of the SIRONA 2 trial supports the safety and accuracy of the Cordella PA Sensor System in enabling comprehensive HF management in NYHA class III HF patients.