Observational study on the neonatal outcome during the COVID-19 pandemic in Germany.

AIM: We aimed to determine stillbirth, preterm birth, perinatal complications, and the developmental outcome of children born preterm during the COVID-19 pandemic in Germany. METHODS: National data from the perinatal survey of preterm and term infants born in 2017-2020 between 22 March and 31 December were evaluated. Neurodevelopment of preterm infants at 2 years corrected age was tested with the Parent Report of Children's Abilities-Revised questionnaire and by clinical testing with Bayley scales, either before or during the COVID-19 pandemic. Statistical significance was calculated using a Pearson's chi-square-independence test and a linear regression model. RESULTS: In 2020, there was an increase of stillbirths of 0.02% (p = 0.01) and a decrease in preterm births by 0.38% (p < 0.001). No changes were found in a representative subgroup of infants with regard to neurodevelopmental scores (mental developmental index and psychomotor developmental index) or in parent survey data (non-verbal cognition scale and language development scale). CONCLUSION: Increasing rates of stillbirths and decreasing preterm births in Germany were observed. Existing networks might stabilise neurodevelopment of preterm infants during the COVID-19 pandemic.

Demonstration of Remote Control of Critical Care Ventilators and the Integrated Clinical Environment

Introduction: The COVID-19 pandemic posed numerous challenges to patient care, including extensive PPE use, patient care in isolation rooms, inadequate numbers of intensivists particularly in rural communities, use of unfamiliar ventilators that would be partially remedied by the ability to remotely control lung ventilation. The goals of the project were to study the intended use, risk management, usability, cybersecurity for remote control of ventilators and demonstrate the use of a single interface for several different ventilators. Method(s): Clinical scenarios were developed including remote control of the ventilator from an antechamber of an isolation room, nursing station within the same ICU, and remote control from across the country. A risk analysis and was performed and a risk management plan established using the AAMI Consensus Report--Emergency Use Guidance for Remote Control of Medical Devices. A cybersecurity plan is in progress. Testing was done at the MDPNP laboratory. We worked with Nihon Kohden OrangeMed NKV-550, Santa Ana, CA, and Thornhill Medical MOVES SLC, Toronto, Canada. Both companies modified their devices to allow remote control by and application operating on DocBox's Apiary platform. Apiary is a commercially available ICE solution, DocBox Inc, Waltham, MA. An expert panel was created to provide guidance on the design of a single common, simple to use graphical user interface (GUI) for both ventilators. Manufacturers' ventilation modes were mapped to ISO 19223 vocabulary, data was logged using ISO/IEEE 11073-10101 terminology using AAMI 2700-2-1, Medical Devices and Medical Systems - Essential safety and performance requirements for equipment comprising the patient-centric integrated clinical environment (ICE): Part 2-1: Requirements for forensic data logging. Result(s): We demonstrated that both ventilators can be controlled and monitored using common user interface within an institution and across the country. Pressure and flow waveforms were available for the NKV-550 ventilator, and usual ventilator measurements were displayed in near-real time. The interface allowed changing FiO2, ventilation mode, respiratory rate, tidal volume, inspiratory pressure, and alarm settings. At times, increased network latency negatively affected the transmission of waveforms. Conclusion(s): We were able to demonstrate remote control of 2 ventilators with a common user interface. Further work needs to be done on cybersecurity, effects of network perturbations, safety of ventilator remote control, usability implications of having a common UI for different devices needs to be investigated.

National emergency tele-critical care network emergency medical services pilot: Lessons learned

INTRODUCTION: The National Emergency Tele-Critical Care Network (NETCCN) was developed to address limited and geographically unevenly distributed critical care (CC) providers during COVID-19 pandemic surges. Although designed for on-demand access to CC experts, for pandemic response, NETCCN continues to evolve and must be flexible and adaptable to future mass casualty/disasters. We report a pilot using tele-critical care (TCC) through NETCCN in emergency medical services (EMS). METHODS: We deployed a mobile device enabled cloud based, easy to use and learn, secure, HIPAA compliant TCC app developed for NETCCN in an emergency medical services (EMS) pilot designed to facilitate rapid communication via text, voice, video and file sharing between paramedics in the field, and emergency medicine specialists. A 30-minute session trained participants on the app, including account creation, login, and functions. EMS providers were encouraged to replace existing telephone communication triage protocols with the NETCCN mobile app. We collected the number/nature of consults, and narrative feedback. RESULTS: The pilot ran for 30 days and was used on average 3 times/week. No patient data was entered into the system, and the app was solely used for its communication features. The most common use case was terminating resuscitation. Debrief and feedback confirmed that the app was easy to use, not significantly affected by connectivity issues, and elicited several barriers to adoption by EMS providers: 1) manual input of patient data 2) perception of being micromanaged. Overall impression of the app and its utility was positive by both remote and EMS providers, and discussion elicited strategies to improve adoption: 1) incorporate TCC into protocols for interfacility critical care transport 2) automate patient data entry (e.g. scan driver's license)). CONCLUSIONS: We demonstrated that the NETCCN TCC app is quickly and easily usable in the EMS setting, but that further optimization is required to promote adoption. Novel non-disaster use cases like this can provide means to stabilize and sustain a system designed primarily for infrequent “as needed” response. Additionally, feedback and problem solving for these novel use cases can be an effective way to enhance system flexibility with dividends for future disaster use.

A privacy preserving system for AI-assisted video analytics

The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to be appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper, we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. The system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure. © 2021 IEEE.