Stability of One-Step Spray-on Splint for Lower Extremity Fractures During Splinting, MEDEVAC, and Impact.

INTRODUCTION: Military transport can induce whole-body vibrations, and combat almost always involves high impact between lower extremities and the ground. Therefore, robust splinting technology is necessary for lower extremity fractures in these settings. Our team compared a novel one-step spray-on foam splint (FastCast) to the current military standard structured aluminum malleable (SAM) splint. MATERIALS AND METHODS: Ten cadaveric specimens were subjected to complete tibia/fibula osteotomy. Specimens were fitted with custom accelerometer and gyroscope sensors superior and inferior to the fracture line. Each specimen underwent fracture and splinting from a standard of care SAM splint and an experimental FastCast spray foam splint in a randomized order. Each specimen was manually transported to an ambulance and then released from a 1 meter height to simulate impact. The custom sensors recorded accelerations and rotations throughout each event. Repeated-measures Friedman tests were used to assess differences between splint method within each event and between sensors within each splint method. RESULTS: During splinting, overall summation of change and difference of change between sensors for accelerations and rotations were greater for SAM splints than FastCast across all axes (P ≤ 0.03). During transport, the range of acceleration along the linear superior/inferior axis was greater for SAM splint than FastCast (P = 0.02), as was the range of rotation along the transverse plane (P < 0.01). On impact, the summation of change observed was greater for SAM splint than FastCast with respect to acceleration and rotation on the posterior/anterior and superior/inferior axes (P ≤ 0.03), and the cumulative difference between superior and inferior sensors was greater for SAM than FastCast with respect to anterior-axis rotation (P < 0.05). CONCLUSION: FastCast maintains stabilization of fractured lower extremities during transport and impacts to a significantly greater extent than SAM splints. Therefore, FastCast can potentially reduce the risk of fracture complications following physical stressors associated with combat and extraction.

Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual's ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual's compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models' coefficients and minimal processor cycles to complete the computations.

Identification of Sleep Patterns via Clustering of Hypnodensities.

Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms to hypnodensities graphs generated by a pre-trained neural network. In a population of 100 subjects we identify two stable clusters whose characteristics we visualize graphically and through estimates of total sleep time. We also find that the hypnodensity representation of the sleep stages produces more robust clustering results than the same methods applied to traditional hypnograms.

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Benefits and Challenges of Remote Patient Monitoring as Perceived by Health Care Practitioners: A Systematic Review.

BACKGROUND: Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies. METHODS: A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review. RESULTS: A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns. CONCLUSION: Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered.

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions.

OBJECTIVE: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. METHODS: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. RESULTS: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. CONCLUSION: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. SIGNIFICANCE: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms.

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation - when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor of 194 subjects. The model accurately predicts CRM when tested on data from 22 additional human subjects obtained from Lower Body Negative Pressure (LBNP) emulation of hemorrhage, attaining a mean squared error (MSE) of 0.0238 over the full range of values, including those from subjects with both low and high tolerance to central hypovolemia.

Data Compression via Low Complexity Delta Transition Lossless Encoding for Remote Physiological and Environmental Monitoring.

Continuous remote physiologic and environmental monitoring, employing an ever-increasing array of sensors, is now commonplace. Given the significant amount of data being digitized, two common sources of energy consumption can be targeted to improve device runtime: data storage and data transmission. One embedded method to maximize device runtime is inline low energy data compression. Herein we present a low complexity data encoding scheme. We list and characterize the parameters necessary for encoding. The encoding method is then evaluated and tuned using contrived data with varying degrees of covariance, as well as open-source electrocardiography (ECG) data. Finally, the encoding method is evaluated with tri-axial accelerometry and ECG data previously collected on a Mount Everest Expedition using a remote physiologic monitor that was specifically designed for long autonomous runtimes. With the described low overhead delta transition lossless encoding method, the Mt. Everest device runtime would have doubled from two to four weeks of continuous recording. Finally, this approach would be beneficial given a requirement to transmit data wirelessly in real time, since the total transmission power and energy would be reduced by an amount related to the compression ratio.

Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals.

Physiologic monitoring enables scientists and physicians to study both normal and pathologic signals of the body. While wearable technologies are available today, many of these technologies are limited to data collection only. Embedded processors have minimal computational capabilities. We propose an efficient implementation of the Stockwell Transform which can enable real-time time-frequency analysis of biological signals in a microcontroller. The method is built upon the fact that the Stockwell Transform can be implemented as a compact filter bank with pre-computed filter taps. Additionally, due to the long tails of the gaussian windowing function, low amplitude filter taps can be removed. The method was implemented on a TI MSP430 processor. Simulated ECG data was fed into the processor to demonstrate performance and evaluate computational efficiency.

Mild steel welding fume causes manganese accumulation and subtle neuroinflammatory changes but not overt neuronal damage in discrete brain regions of rats after short-term inhalation exposure.

Serious questions have been raised by occupational health investigators regarding a possible causal association between neurological effects in welders and the presence of manganese (Mn) in welding fume. Male Sprague-Dawley rats were exposed by inhalation to 40 mg/m(3) of gas metal arc-mild steel (MS) welding fume for 3 h/day for 10 days. Generated fume was collected in the animal chamber during exposure, and particle size, composition, and morphology were characterized. At 1 day after the last exposure, metal deposition in different organ systems and neurological responses in dopaminergic brain regions were assessed in exposed animals. The welding particles were composed primarily of a complex of iron (Fe) and Mn and were arranged as chain-like aggregates with a significant number of particles in the nanometer size range. Mn was observed to translocate from the lungs to the kidney and specific brain regions (olfactory bulb, cortex, and cerebellum) after MS fume inhalation. In terms of neurological responses, short-term MS fume inhalation induced significant elevations in divalent metal ion transporter 1 (Dmt1) expression in striatum and midbrain and significant increases in expression of proinflammatory chemokines (Ccl2, Cxcl2) and cytokines (IL1beta, TNFalpha) in striatum. In addition, mRNA and protein expression of glial fibrillary acidic protein (GFAP) was significantly increased in striatum after MS fume exposure. However, the 10-day MS welding fume inhalation did not cause any changes in dopamine and its metabolites or GABA in dopaminergic brain regions nor did it produce overt neural cell damage as assessed by histopathology. In summary, short-term MS welding fume exposure led to translocation of Mn to specific brain regions and induced subtle changes in cell markers of neuroinflammatory and astrogliosis. The neurofunctional significance of these findings currently is being investigated in longer, more chronic welding fume exposure studies.

Assuring community emergency care capacity with collaborative Internet tools: the Milwaukee experience.

Hospital overcrowding and diversion of ambulances from emergency departments are being recognized as increasing problems in the health care system. This article, a descriptive narrative, examines the various factors contributing to the problem and describes how collaborative approaches to public health issues can be applied. It describes Milwaukee's experience with a collaborative approach. The use of a technological tool to assist with tracking and reporting on ambulance diversion and emergency department overload is explained, and data are provided to show the impact of various methods to blunt the impact of the flu season on diversion frequency. The article encourages use of similar collaborative approaches and Internet-based technology to address other public health problems.

The Frontlines of Medicine Project: a proposal for the standardized communication of emergency department data for public health uses including syndromic surveillance for biological and chemical terrorism.

The Frontlines of Medicine Project is a collaborative effort of emergency medicine (including emergency medical services and clinical toxicology), public health, emergency government, law enforcement, and informatics. This collaboration proposes to develop a nonproprietary, "open systems" approach for reporting emergency department patient data. The common element is a standard approach to sending messages from individual EDs to regional oversight entities that could then analyze the data received. ED encounter data could be used for various public health initiatives, including syndromic surveillance for chemical and biological terrorism. The interlinking of these regional systems could also permit public health surveillance at a national level based on ED patient encounter data. Advancements in the Internet and Web-based technologies could allow the deployment of these standardized tools in a rapid time frame.