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The coronavirus disease 2019 (COVID-19) pandemic has challenged the ability of Emergency Medical Services (EMS) providers to maintain personal safety during the treatment and transport of patients potentially infected. Increased rates of COVID-19 infection in EMS providers after patient care exposure, and notably after performing aerosol-generating procedures (AGPs), have been reported. With an already strained workforce seeing rising call volumes and increased risk for AGP-requiring patient presentations, development of novel devices for the protection of EMS providers is of great importance.
Based on the concept of a negative pressure room, the AerosolVE BioDome is designed to encapsulate the patient and contain aerosolized infectious particles produced during AGPs, making the cabin of an EMS vehicle safer for providers. The objective of this study was to determine the efficacy and safety of the tent in mitigating simulated infectious particle spread in varied EMS transport platforms during AGP utilization.
Fifteen healthy volunteers were enrolled and distributed amongst three EMS vehicles: a ground ambulance, an aeromedical-configured helicopter, and an aeromedical-configured jet. Sodium chloride particles were used to simulate infectious particles and particle counts were obtained in numerous locations close to the tent and around the patient compartment. Counts near the tent were compared to ambient air with and without use of AGPs (non-rebreather mask, continuous positive airway pressure [CPAP] mask, and high-flow nasal cannula [HFNC]).
For all transport platforms, with the tent fan off, the particle generator alone, and with all AGPs produced particle counts inside the tent significantly higher than ambient particle counts (P <.0001). With the tent fan powered on, particle counts near the tent, where EMS providers are expected to be located, showed no significant elevation compared to baseline ambient particle counts during the use of the particle generator alone or with use of any of the AGPs across all transport platforms.
Development of devices to improve safety for EMS providers to allow for use of all available therapies to treat patients while reducing risk of communicable respiratory disease transmission is of paramount importance. The AerosolVE BioDome demonstrated efficacy in creating a negative pressure environment and workspace around the patient and provided significant filtration of simulated respiratory droplets, thus making the confined space of transport vehicles potentially safer for EMS personnel.
The coronavirus disease 2019 (COVID-19) pandemic has created challenges in maintaining the safety of prehospital providers caring for patients. Reports have shown increased rates of Emergency Medical Services (EMS) provider infection with COVID-19 after patient care exposure, especially while utilizing aerosol-generating procedures (AGPs). Given the increased risk and rising call volumes for AGP-necessitating complaints, development of novel devices for the protection of EMS clinicians is of great importance.
Drawn from the concept of the powered air purifying respirator (PAPR), the AerosolVE helmet creates a personal negative pressure space to contain aerosolized infectious particles produced by patients, making the cabin of an EMS vehicle safer for providers. The helmet was developed initially for use in hospitals and could be of significant use in the prehospital setting. The objective of this study was to determine the efficacy and safety of the helmet in mitigating simulated infectious particle spread in varied EMS transport platforms during AGP utilization.
Fifteen healthy volunteers were enrolled and distributed amongst three EMS vehicles: a ground ambulance, a medical helicopter, and a medical jet. Sodium chloride particles were used to simulate infectious particles, and particle counts were obtained in numerous locations close to the helmet and around the patient compartment. Counts near the helmet were compared to ambient air with and without use of AGPs (non-rebreather mask [NRB], continuous positive airway pressure mask [CPAP], and high-flow nasal cannula [HFNC]).
Without the helmet fan on, the particle generator alone and with all AGPs produced particle counts inside the helmet significantly higher than ambient particle counts. With the fan on, there was no significant difference in particle counts around the helmet compared to baseline ambient particle counts. Particle counts at the filter exit averaged less than one despite markedly higher particle counts inside the helmet.
Given the risk to EMS providers by communicable respiratory diseases, development of devices to improve safety while still enabling use of respiratory therapies is of paramount importance. The AerosolVE helmet demonstrated efficacy in creating a negative pressure environment and provided significant filtration of simulated respiratory droplets, thus making the confined space of transport vehicles potentially safer for EMS personnel.
Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.
Funding: Sweden’s Innovation Agency and Stockholm County Council
Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces.
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.
Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials for new applications, learning new relationships from existing data resources, and building fast predictive models. We highlight key materials informatics successes from the atomic-scale modeling community, and discuss the ecosystem of open data, software, services, and infrastructure that have led to broad adoption of materials informatics approaches. We then examine emerging opportunities for informatics in materials science and describe an ideal data ecosystem capable of supporting similar widespread adoption of materials informatics, which we believe will enable the faster design of materials.
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