Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients.

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring.

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Reliability and performance of the IRRAflow® system for intracranial lavage and evacuation of hematomas-A technical note.

BACKGROUND: Intraventricular hemorrhage (IVH) is a severe condition with poor outcomes and high mortality. IRRAflow® (IRRAS AB) is a new technology introduced to accelerate IVH clearance by minimally invasive wash-out. The IRRAflow® system performs active and controlled intracranial irrigation and aspiration with physiological saline, while simultaneously monitoring and maintaining a stable intracranial pressure (ICP). We addressed important aspects of the device implementation and intracranial lavage. METHOD: To allow versatile investigation of multiple device parameters, we designed an ex vivo lab setup. We evaluated 1) compatibility between the IRRAflow® catheter and the Silverline f10 bolt (Spiegelberg), 2) the physiological and hydrodynamic effects of varying the IRRAflow® settings, 3) the accuracy of the IRRAflow® injection volumes, and 4) the reliability of the internal ICP monitor of the IRRAflow®. RESULTS: The IRRAflow® catheter was not compatible with Silverline bolt fixation, which was associated with leakage and obstruction. Design space exploration of IRRAflow® settings revealed that appropriate settings included irrigation rate 20 ml/h with a drainage bag height at 0 cm, irrigation rate 90 ml/h with a drainage bag height at 19 cm and irrigation rate 180 ml/h with a drainage bag height at 29 cm. We found the injection volume performed by the IRRAflow® to be stable and reliable, while the internal ICP monitor was compromised in several ways. We observed a significant mean drift difference of 3.16 mmHg (variance 0.4, p = 0.05) over a 24-hour test period with a mean 24-hour drift of 3.66 mmHg (variance 0.28) in the pressures measured by the IRRAflow® compared to 0.5 mmHg (variance 1.12) in the Raumedic measured pressures. CONCLUSION: Bolting of the IRRAflow® catheter using the Medtronic Silverline® bolt is not recommendable. Increased irrigation rates are recommendable followed by a decrease in drainage bag level. ICP measurement using the IRRAflow® device was unreliable and should be accompanied by a control ICP monitor device in clinical settings.

Microfluidic Sensing Textile for Continuous Monitoring of Sweat Glucose at Rest.

Wearable sweat sensors have received considerable attention due to their great potential for noninvasive continuous monitoring of an individual's health status applications. However, the low secretion rate and fast evaporation of sweat pose challenges in collecting sweat from sedentary individuals for noninvasive analysis of body physiology. Here, we demonstrate wearable textiles for continuous monitoring of sweat at rest using the combination of a heating element and a microfluidic channel to increase localized skin sweat secretion rates and combat sweat evaporation, enabling accurate and stable monitoring of trace amounts of sweat. The Janus sensing yarns with a glucose sensing sensitivity of 36.57 mA cm-2 mM-1 are embroidered into the superhydrophobic heated textile to collect sweat directionally, resulting in improved sweat collection efficiency of up to 96 and 75% retention. The device also maintains a highly durable sensing performance, even in dynamic deformation, recycling, and washing. The microfluidic sensing textile can be further designed into a wireless sensing system that enables sedentary-compatible sweat analysis for the continuous, real-time monitoring of body glucose levels at rest.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

[Research Progress on End-Tidal Carbon Dioxide Detection Technology Based on Non-Dispersive Infrared Method].

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal. Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice. With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology, its application in clinical practice in China has become increasingly widespread in recent years. The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle, gas sampling methods, key technologies, and technological progress of end-tidal carbon dioxide detection technology. It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

Development of a Web-Based Oxygenation Dashboard for Preterm Neonates: A Quality Improvement Initiative.

BACKGROUND: Preterm neonates are extensively monitored to require strict oxygen target attainment for optimal outcomes. In daily practice, detailed oxygenation data are hardly used and crucial patterns may be missed due to the snapshot presentations and subjective observations. This study aimed to develop a web-based dashboard with both detailed and summarized oxygenation data in real-time and to test its feasibility to support clinical decision making. METHODS: Data from pulse oximeters and ventilators were synchronized and stored to enable real-time and retrospective trend visualizations in a web-based viewer. The dashboard was designed based on interviews with clinicians. A preliminary version was evaluated during daily clinical rounds. The routine evaluation of the respiratory condition of neonates (gestational age < 32 weeks) with respiratory support at the NICU was compared to an assessment with the assistance of the dashboard. RESULTS: The web-based dashboard included data on the oxygen saturation (SpO2), fraction of inspired oxygen (FiO2), SpO2/FiO2 ratio, and area < 80% and > 95% SpO2 curve during time intervals that could be varied. The distribution of SpO2 values was visualized as histograms. In 65% of the patient evaluations (n = 86) the level of hypoxia was assessed differently with the use of the dashboard. In 75% of the patients the dashboard was judged to provide added value for the clinicians in supporting clinical decisions. CONCLUSIONS: A web-based customized oxygenation dashboard for preterm neonates at the NICU was developed and found feasible during evaluation. More clear and objective information was found supportive for clinicians during the daily rounds in tailoring treatment strategies.

"NeuroVanguard": a contemporary strategy in neuromonitoring for severe adult brain injury patients.

Severe acute brain injuries, stemming from trauma, ischemia or hemorrhage, remain a significant global healthcare concern due to their association with high morbidity and mortality rates. Accurate assessment of secondary brain injuries severity is pivotal for tailor adequate therapies in such patients. Together with neurological examination and brain imaging, monitoring of systemic secondary brain injuries is relatively straightforward and should be implemented in all patients, according to local resources. Cerebral secondary injuries involve factors like brain compliance loss, tissue hypoxia, seizures, metabolic disturbances and neuroinflammation. In this viewpoint, we have considered the combination of specific noninvasive and invasive monitoring tools to better understand the mechanisms behind the occurrence of these events and enhance treatment customization, such as intracranial pressure monitoring, brain oxygenation assessment and metabolic monitoring. These tools enable precise intervention, contributing to improved care quality for severe brain injury patients. The future entails more sophisticated technologies, necessitating knowledge, interdisciplinary collaboration and resource allocation, with a focus on patient-centered care and rigorous validation through clinical trials.

Evaluation of the accuracy of a UWB tracker for in-home positioning for older adults.

The population of older adults is rapidly growing. In-home monitoring systems have been used to support aging-in-place. Ambient sensors or wearable localizers can be used but may be too low resolution, while camera systems are invasive to privacy. Ultra-wideband (UWB) localization offers precise positioning by placing anchors throughout the house and wearing a tag that is tracked by the anchors. In this study, the accuracy of UWB for indoor tracking was evaluated in a motion capture gait lab and in a mock condo in the Glenrose Rehabilitation Hospital. First, the configuration of UWB was tested, changing factors related to sampling time, anchor placement and line-of-sight. Comparing these factors to the configurations recommended by the manufacturer guidelines, accuracies remained within 14 cm. We then performed static and dynamic accuracy tests, with dynamic testing comprised of rolling and walking motions. In the motion capture lab, we found localization accuracies of 7.0 ± 11.1 cm while in the mock condo, we found accuracies of 27.3 ± 12.9 cm. Dynamic testing with rolling motions had an average of 19.1 ± 1.6 cm while walking was 20.5 ± 4.2 cm. The mean accuracy of UWB is within the 30 cm target for indoor localization.

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an "Internet of Things"-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA's score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

Flexible Textile Sensors-Based Smart T-Shirt for Respiratory Monitoring: Design, Development, and Preliminary Validation.

Respiratory rate (fR) monitoring through wearable devices is crucial in several scenarios, providing insights into well-being and sports performance while minimizing interference with daily activities. Strain sensors embedded into garments stand out but require thorough investigation for optimal deployment. Optimal sensor positioning is often overlooked, and when addressed, the quality of the respiratory signal is neglected. Additionally, sensor metrological characterization after sensor integration is often omitted. In this study, we present the design, development, and feasibility assessment of a smart t-shirt embedded with two flexible sensors for fR monitoring. Guided by a motion capture system, optimal sensor design and position on the chest wall were defined, considering both signal magnitude and quality. The sensors were developed, embedded into the wearable system, and metrologically characterized, demonstrating a remarkable response to both static (sensitivity 9.4 Ω⋅%-1 and 9.1 Ω⋅%-1 for sensor A and sensor B, respectively) and cyclic loads (min. hysteresis span 20.4% at 36 bpm obtained for sensor A). The feasibility of the wearable system was assessed on healthy volunteers both under static and dynamic conditions (such as running, walking, and climbing stairs). A mean absolute error of 0.32 bpm was obtained by averaging all subjects and tests using the combination of the two sensors. This value was lower than that obtained using both sensor A (0.53 bpm) and sensor B (0.78 bpm) individually. Our study highlights the importance of signal amplitude and quality in optimal sensor placement evaluation, as well as the characterization of the embedded sensors for metrological assessment.

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

The current status of noninvasive intracranial pressure monitoring: A literature review.

Elevated intracranial pressure (ICP) is a life-threatening condition that must be promptly diagnosed. However, the gold standard methods for ICP monitoring are invasive, time-consuming, and they involve certain risks. To address these risks, many noninvasive approaches have been proposed. This study undertakes a literature review of the existing noninvasive methods, which have reported promising results. The experimental base on which they are established, however, prevents their application in emergency conditions and thus none of them are capable of replacing the traditional invasive methods to date. On the other hand, contemporary methods leverage Machine Learning (ML) which has already shown unprecedented results in several medical research areas. That said, only a few publications exist on ML-based approaches for ICP estimation, which are not appropriate for emergency conditions due to their restricted capability of employing the medical imaging data available in intensive care units. The lack of such image-based ML models to estimate ICP is attributed to the scarcity of annotated datasets requiring directly measured ICP data. This ascertainment highlights an active and unexplored scientific frontier, calling for further research and development in the field of ICP estimation, particularly leveraging the untapped potential of ML techniques.

A multimodal stacked ensemble model for cardiac output prediction utilizing cardiorespiratory interactions during general anesthesia.

This study examined the possibility of estimating cardiac output (CO) using a multimodal stacking model that utilizes cardiopulmonary interactions during general anesthesia and outlined a retrospective application of machine learning regression model to a pre-collected dataset. The data of 469 adult patients (obtained from VitalDB) with normal pulmonary function tests who underwent general anesthesia were analyzed. The hemodynamic data in this study included non-invasive blood pressure, plethysmographic heart rate, and SpO2. CO was recorded using Vigileo and EV1000 (pulse contour technique devices). Respiratory data included mechanical ventilation parameters and end-tidal CO2 levels. A generalized linear regression model was used as the metalearner for the multimodal stacking ensemble method. Random forest, generalized linear regression, gradient boosting machine, and XGBoost were used as base learners. A Bland-Altman plot revealed that the multimodal stacked ensemble model for CO prediction from 327 patients had a bias of - 0.001 L/min and - 0.271% when calculating the percentage of difference using the EV1000 device. Agreement of model CO prediction and measured Vigileo CO in 142 patients reported a bias of - 0.01 and - 0.333%. Overall, this model predicts CO compared to data obtained by the pulse contour technique CO monitors with good agreement.

Advancements in optical fiber-based wearable sensors for smart health monitoring.

Healthcare system is undergoing a significant transformation from a traditional hospital-centered to an individual-centered one, as a result of escalating chronic diseases, ageing populations, and ever-increasing healthcare costs,. Wearable sensors have become widely used in health monitoring systems since the COVID-19 pandemic. They enable continuous measurement of important health indicators like body temperature, wrist pulse, respiration rate, and non-invasive bio fluids like saliva and perspiration. Over the last few decades, the development has mostly concentrated on electrochemical and electrical wearable sensors. However, due to the drawbacks of such sensors, such as electronic waste, electromagnetic interference, non-electrical security, and poor performance, researchers are exhibiting a strong interest in optical principle-based systems. Fiber-based optical wearables are among the most promising healthcare systems because of advancements in high-sensitivity, durable, multiplexed sensing, and simple integration with flexible materials to improve wearability and simplicity. We present an overview of recent developments in optical fiber-based wearable sensors, focusing on two mechanisms: wavelength interrogation and intensity modulation for the detection of body temperature, pulse rate, respiration rate, body movements, and biomedical noninvasive fluids, with a thorough examination of their benefits and drawbacks. This review also focuses on improving working performance and application techniques for healthcare systems, including the integration of nanomaterials and the usage of the Internet of Things (IoT) with signal processing. Finally, the review concludes with a discussion of the future possibilities and problems for optical fiber-based wearables.

From wires to waves, a novel sensor system for in vivo pressure monitoring.

Pressure monitoring in various organs of the body is essential for appropriate diagnostic and therapeutic purposes. In almost all situations, monitoring is performed in a hospital setting. Technological advances not only promise to improve clinical pressure monitoring systems, but also engage toward the development of fully implantable systems in ambulatory patients. Such systems would not only provide longitudinal time monitoring to healthcare personnel, but also to the patient who could adjust their way-of-life in response to the measurements. In the past years, we have developed a new type of piezoresistive pressure sensor system. Different bench tests have demonstrated that it delivers precise and reliable pressure measurements in real-time. The potential of this system was confirmed by a continuous recording in a patient that lasted for almost a day. In the present study, we further characterized the functionality of this sensor system by conducting in vivo implantation experiments in nine female farm pigs. To get a step closer to a fully implantable system, we also adapted two different wireless communication solutions to the sensor system. The communication protocols are based on MICS (Medical Implant Communication System) and BLE (Bluetooth Low Energy) communication. As a proof-of-concept, implantation experiments in nine female pigs demonstrated the functionality of both systems, with a notable technical superiority of the BLE.