Routine placental histopathology findings from women testing positive for SARS-CoV-2 during pregnancy: Retrospective cohort comparative study.

OBJECTIVE: To assess the impact of maternal Coronavirus disease 2019 (COVID-19) infection on placental histopathological findings in an unselected population and evaluate the potential effect on the fetus, including the possibility of vertical transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). DESIGN: Retrospective cohort comparative study of placental histopathological findings in patients with COVID-19, compared with controls. SETTING: During the COVID-19 pandemic, placentas were studied from women at University College Hospital London who reported and/or tested positive for COVID-19. POPULATION: Of 10 508 deliveries, 369 (3.5%) women had COVID-19 during pregnancy, with placental histopathology available for 244 women. METHODS: Retrospective review of maternal and neonatal characteristics, where placental analysis had been performed. This was compared with available, previously published, histopathological findings from placentas of unselected women. MAIN OUTCOME MEASURES: Frequency of placental histopathological findings and relevant clinical outcomes. RESULTS: Histological abnormalities were reported in 117 of 244 (47.95%) cases, with the most common diagnosis being ascending maternal genital tract infection. There was no statistically significant difference in the frequency of most abnormalities compared with controls. There were four cases of COVID-19 placentitis (1.52%, 95% CI 0.04%-3.00%) and one possible congenital infection, with placental findings of acute maternal genital tract infection. The rate of fetal vascular malperfusion (FVM), at 4.5%, was higher compared with controls (p = 0.00044). CONCLUSIONS: In most cases, placentas from pregnant women infected with SARS-CoV-2 virus do not show a significantly increased frequency of pathology. Evidence for transplacental transmission of SARS-CoV-2 is lacking from this cohort. There is a need for further study into the association between FVM, infection and diabetes.

Machine learning forecasting for COVID-19 pandemic-associated effects on paediatric respiratory infections.

OBJECTIVE: The COVID-19 pandemic and subsequent government restrictions have had a major impact on healthcare services and disease transmission, particularly those associated with acute respiratory infection. This study examined non-identifiable routine electronic patient record data from a specialist children's hospital in England, UK, examining the effect of pandemic mitigation measures on seasonal respiratory infection rates compared with forecasts based on open-source, transferable machine learning models. METHODS: We performed a retrospective longitudinal study of respiratory disorder diagnoses between January 2010 and February 2022. All diagnoses were extracted from routine healthcare activity data and diagnosis rates were calculated for several diagnosis groups. To study changes in diagnoses, seasonal forecast models were fit to prerestriction period data and extrapolated. RESULTS: Based on 144 704 diagnoses from 31 002 patients, all but two diagnosis groups saw a marked reduction in diagnosis rates during restrictions. We observed 91%, 89%, 72% and 63% reductions in peak diagnoses of 'respiratory syncytial virus', 'influenza', 'acute nasopharyngitis' and 'acute bronchiolitis', respectively. The machine learning predictive model calculated that total diagnoses were reduced by up to 73% (z-score: -26) versus expected during restrictions and increased by up to 27% (z-score: 8) postrestrictions. CONCLUSIONS: We demonstrate the association between COVID-19 related restrictions and significant reductions in paediatric seasonal respiratory infections. Moreover, while many infection rates have returned to expected levels postrestrictions, others remain supressed or followed atypical winter trends. This study further demonstrates the applicability and efficacy of routine electronic record data and cross-domain time-series forecasting to model, monitor, analyse and address clinically important issues.

Intussusception and COVID-19 in Infants: Evidence for an Etiopathologic Correlation.

Nonrespiratory conditions related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have been largely described. Ileocolic intussusception has been reported in association with SARS-CoV-2 infection in 10 children, raising the possibility of an etiopathologic role for the virus, but none of these cases documented tissue pathology that would have supported SARS-CoV-2 intestinal inflammation. We report 2 cases of intussusception in patients with SARS-CoV-2 infection who were treated at different pediatric tertiary centers in Europe and provide evidence of the presence of the virus in mesenteric and intestinal tissues of the patients.

The impact of the COVID-19 pandemic on antimicrobial prescribing at a specialist paediatric hospital: an observational study.

BACKGROUND: The COVID-19 pandemic has severely impacted healthcare delivery and there are growing concerns that the pandemic will accelerate antimicrobial resistance. OBJECTIVES: To evaluate the impact of the COVID-19 pandemic on antibiotic prescribing in a tertiary paediatric hospital in London, UK. METHODS: Data on patient characteristics and antimicrobial administration for inpatients treated between 29 April 2019 and Sunday 28 March 2021 were extracted from the electronic health record (EHR). Interrupted time series analysis was used to evaluate antibiotic days of therapy (DOT) and the proportion of prescribed antibiotics from the WHO 'Access' class. RESULTS: A total of 23 292 inpatient admissions were included. Prior to the pandemic there were an average 262 admissions per week compared with 212 during the pandemic period. Patient demographics were similar in the two periods but there was a shift in the specialities that patients had been admitted to. During the pandemic, there was a crude increase in antibiotic DOTs, from 801 weekly DOT before the pandemic to 846. The proportion of Access antibiotics decreased from 44% to 42%. However, after controlling for changes in patient characteristics, there was no evidence for the pandemic having an impact on antibiotic prescribing. CONCLUSIONS: The patient population in a specialist children's hospital was affected by the COVID-19 pandemic, but after adjusting for these changes there was no evidence that antibiotic prescribing was significantly affected by the pandemic. This highlights both the value of routine, high-quality EHR data and importance of appropriate statistical methods that can adjust for underlying changes to populations when evaluating impacts of the pandemic on healthcare.

Local and systemic responses to SARS-CoV-2 infection in children and adults.

It is not fully understood why COVID-19 is typically milder in children1-3. Here, to examine the differences between children and adults in their response to SARS-CoV-2 infection, we analysed paediatric and adult patients with COVID-19 as well as healthy control individuals (total n = 93) using single-cell multi-omic profiling of matched nasal, tracheal, bronchial and blood samples. In the airways of healthy paediatric individuals, we observed cells that were already in an interferon-activated state, which after SARS-CoV-2 infection was further induced especially in airway immune cells. We postulate that higher paediatric innate interferon responses restrict viral replication and disease progression. The systemic response in children was characterized by increases in naive lymphocytes and a depletion of natural killer cells, whereas, in adults, cytotoxic T cells and interferon-stimulated subpopulations were significantly increased. We provide evidence that dendritic cells initiate interferon signalling in early infection, and identify epithelial cell states associated with COVID-19 and age. Our matching nasal and blood data show a strong interferon response in the airways with the induction of systemic interferon-stimulated populations, which were substantially reduced in paediatric patients. Together, we provide several mechanisms that explain the milder clinical syndrome observed in children.

An after-action review for adoption of emerging technologies during the COVID-19 pandemic

IntroductionThe COVID-19 pandemic response has accelerated adoption of digital health technologies to support social distancing. In part, this involves repurposing technologies that were not originally developed for healthcare application. In the hospital setting, this includes the appropriation of ‘Off the Shelf’ (OTS) digital products that facilitate video-based clinical consultations, diagnostics and communication during ward rounds and multi-disciplinary team meetings. Such technologies were implemented within weeks at GOSH where video consultations were implemented for >90% of outpatient appointments.MethodsHere we present the findings from a Debrief-After Action Review (AAR) lasting two hours, supplemented with speculative questions on how lessons learned from the rapid deployment of one technology could inform the adoption practices of other emerging technologies.ResultsFifteen participants, who were members of a Transforming Care Links working group and interested in the impact of digital systems on patient care, contributed to the AAR. Five themes were identified from thematic analysis of this bottom-up approach: (i) Clinical: Planning and redesigning workflows with clear purpose, intent, and communication with clinical teams (ii) Technology: Infrastructure and equipment available across the team based on the needs of the workspace, with security and governance processes (iii) Capability mapping: Building core capability in a structured way across the entire team with space and time to trial out technologies as part of a progressive learning path and supported by clinical champions (iv) Benefits: Demonstrable benefits with new technology enabled ways of working based on preliminary small-scale deployments that deliver measurable value (v) Environment and Context: Context-specific workflow redesign for technology enabled interactions that consider optimum conditions of the physical environment.ConclusionThis user-centred approach identified routine training pathways, equity of access to training opportunities and equipment, a period of trialability and demonstrable benefits as enablers for the successful adoption of emerging technologies.

Encouraging activity and exercise among children and young people using computer vision and machine learning during gaming

IntroductionPopulation health and wellbeing is a priority in the UK, with new initiatives that empower children to live healthier lives. Excess weight has also been associated to worse outcomes during the COVID-19 pandemic period, complicated by reduced activity within the confinements of a home environment and coupled by increased screen time with remote classroom practices. As a result, children and young people now interact with computer interfaces in their home environment for education, gaming and healthcare purposes for prolonged periods and in new ways.MethodThere is a growing interest in Natural User Interfaces (NUIs) that use natural hand and body gestures to interact with computers. Advances to these technologies mean that they are now more accurate, easier to use and instead of requiring expensive depth cameras, can be operated using simple webcams. In this study, OpenCV library is used to track user movement by calculating the pixel difference between two frames and create a catalogue of exercises. We use PyTorch exercise recognition model to check the status of the user every 8 frames. These are recognised by using Convolutional Neural Networks (CNNs) with static training from datasets and offer users the option to create personalised exercises.ResultWe present University College London’s (UCL) Motion- Input supporting DirectX: Gestures for at-home exercises. This exercise module can recognise six repetitious static exercises, such as running on the spot, squatting, cycling on an exercise bike, and rowing on a rowing machine using a webcam. This is intended for integrated exercise triggers during gaming in place of a handheld control panel (i.e., jumping to trigger commands), remote coaching for fitness and bespoke treatment plans for physical rehabilitation.ConclusionWebcam-based computer vision exercise catalogues using everyday devices like webcams, hold the potential to encourage healthier and more active behaviours during screen-based activities.

Situated cognition 360° editor 2021: a solution for interactive medical training during the COVID-19 pandemic period

IntroductionImagine entering an operating theatre or developing clinical skills in empathy and communication through Virtual Reality. To enhance the experience of learning, novel methods using VR have been researched and simulated for clinicians. This is because some aspects of clinical training, like conducting procedures and effective team communication focus on ‘learning by doing’ which is difficult to recreate remotely. Here we present a proof-of-concept prototype of a 360°-video editor that augments 360° videos with media to create a mixed reality learning experience.MethodAn editor was built inside Unity to augment 360°-videos of real-world scenarios in healthcare with interactive data. Unity is a cross-platform games engine used to create two-dimension, three-dimension, virtual reality, and augmented reality games, as well as video players to play panoramic 360°-videos. The video player is attached to a Render Texture and a Skybox material that provides the spherical surface for the 360°-video achieving an immersive experience.ResultsThe editor comprises two software packages, one for the trainer, another for the learner. As a unique feature, we introduce clickable Hotspots. This enables users to annotate the 360° film by tagging specific artefacts in the environment and create a place-based interaction. These Hotspots are anchored to a position and can display text and images, and form part of a novel branched timeline of nested data. The intention being the trainer would create the film and annotate the environment with interactive media. This would then be available to the learner, who would use the player to view a bespoke teaching package.ConclusionSituated Cognition 360 Editor 2021 envisages trainers creating interactive 360°-video learning experiences using real life scenarios in healthcare. Future steps involve user experience evaluations, co-design and development of new learner interactions that deliver low cost, remote and easily deployed healthcare education through immersive learning environments.

An exploration of 11 years of seasonality in diagnoses at GOSH

BackgroundWith the extensive impact of the COVID-19 pandemic and subsequent government interventions on the development, diagnosis and treatment of illnesses, building an understanding of ‘typical’ diagnosis trends at GOSH is critical for predicting future demands and potential clinical challenges. Seasonality analysis is an effective method with which one can explore, model and predict the occurrence of events over time when – as with many common diagnoses at GOSH – they generally exhibit a periodic trend over the year.MethodsTo investigate diagnosis seasonality at GOSH, we have extracted all diagnoses recorded in the Legacy and Epic systems, since the year 2010. We have developed an analytics pipeline that uses these data to compute historical rates for any given diagnosis, or group of diagnoses. Based on these diagnosis rates, our pipeline applies a widely used regressive, multiplicative, seasonal decomposition model with integrated model evaluation.ResultsFor the analysis, a total of 3,480,887 diagnosis events were considered across 29,529 patients between receiving a diagnosis between 1stJanuary 2010 and 30th September 2021. This exploration presents data on many of the common diagnoses at GOSH that exhibit a clear seasonal trend in combination with a statistically significant deviation from that trend since March 2020, likely due to the pandemic. In addition, we illustrate how the available data and model allow us to predict the diagnostic shortfall during the same period.

Impact of the COVID-19 pandemic on radiology appointments in a tertiary children's hospital: a retrospective study.

In this retrospective observational study, we evaluated the impact of the COVID-19 pandemic in London on paediatric radiology activity, as a surrogate of overall hospital activity. We showed a large reduction in overall outpatient imaging activity: 49 250 records occurred in the 371 days post COVID-19 period compared with an expected 67 806 records pre COVID-19 period, representing 18 556 'missed' records. Governmental restrictions were associated with reductions in activity, with the largest reduction in activity during tiers 3 and 4 restrictions. Rescheduling such missed outpatients' appointments represents considerable resource planning and the associated clinical impact on paediatric healthcare remains to be determined.

Clinical perception of effectiveness of virtual appointments and comparison with appointment outcomes at a specialist children's hospital.

INTRODUCTION: A transition from face-to-face to virtual consultations occurred in response to the COVID-19 pandemic. Evaluation of outcome data is essential for future healthcare modelling. METHODS: Clinicians at a children's hospital evaluated perceptions of face-to-face video and telephone appointments by questionnaire. Responses were compared with operational outcomes from June 2019 and June 2020. RESULTS: Ninety-three clinicians responded from 28 subspecialties. Virtual consultations increased from 6% (2019) to 67% (2020). No differences were found between appointment types for recording a medical and social history; a significant difference (p<0.001) was seen for the perceived ability to detect clinical signs, organise investigations and make a diagnosis. The proportion of appointments resulting in discharge compared with face-to-face visits was unchanged. The proportion of patients requiring further contact increased from 35% (32% face-to-face and 3% telephone) to 46% (14% face-to-face; 21% telephone and 11% video; chi-squared 426; p<0.0001).The percentage of patients offered an appointment following two 'was not brought' appointments increased from 71% (2019) to 81% (2020) and was most common following telephone appointments (20% face-to-face, 43% telephone and 18% video; chi-squared 474; p<0.0001). CONCLUSION: The perception of clinicians is that virtual appointments enabled continuity of paediatric care with improved clinical assessment capability and attendance during video consultations compared with telephone consultations.

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries.

Importance: Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective: To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants: This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures: Patient characteristics, clinical features, and medication use. Results: There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. Conclusions and Relevance: This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

Network graph representation of COVID-19 scientific publications to aid knowledge discovery.

INTRODUCTION: Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult. METHODS: A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network. RESULTS: The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool. CONCLUSION: Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.