Background Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. Methods This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). Findings 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30•8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1•457 [1•408-1•509]), greater deprivation (1•002 [1•001-1•003]), Asian (1•211 [1•128-1•299]) or mixed ethnicity (1•317 [1•080-1•605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5•433 [4•618-6•392]). Later date of discharge was associated with a lower odds of death (0•977 [0•976-0•978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52•2% in the first week of March to 16•8% in the last week of May. Interpretation Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours.

BackgroundNasal chondromesenchymal hamartoma (NCMH) is a very rare, benign tumour of the sinonasal tract usually presenting in infants. We present a systematic review of NCMH cases alongside a case report of an adult with asymptomatic NCMH.MethodsA systematic review was conducted in accordance with PRISMA guidelines. A PubMed, EMBASE and manual search through references of relevant publications was used to identify all published case-reports of NCMH. Data was collected from each case-report on: patient demographics, laterality, size and location of NCMH, presentation, co-morbidities, investigations, treatment and follow-up.ResultsThe systematic review identified 48 patients (including ours): 33 male, 15 female. Mean age was 9.6 years (range: 1 day–69 years) with the majority aged 1 year or younger at presentation (n = 18). Presentations included: nasal congestion (n = 17), nasal mass (n = 15) and eye signs (n = 12). NCMH also involved the paranasal sinuses (n = 26), orbit (n = 16) and skull-base (n = 14). All patients underwent operative resection of NCMH. A small 2014 case-series found DICER1 mutations in 6 NCMH patients, establishing a link to the DICER1 tumour spectrum.ConclusionsNCMH is a rare cause of nasal masses in young children and adults. In light of the newly established link between NCMH and DICER1 mutations surgeons should be vigilant for associated DICER1 tumours, as NCMH may be the ‘herald tumour’ of this disease spectrum.

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Background: A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from MarchÀJuly 2020. Methods: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. Findings: There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. Interpretation: There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

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BackgroundThe COVID-19 pandemic has stretched EDs globally, with many regions in England challenged by the number of COVID-19 presentations. In order to rapidly share learning to inform future practice, we undertook a thematic review of ED operational experience within England during the pandemic thus far.MethodsA rapid phenomenological approach using semistructured telephone interviews with ED clinical leads from across England was undertaken between 16 and 22 April 2020. Participants were recruited through purposeful sampling with sample size determined by data saturation. Departments from a wide range of geographic distribution and COVID-19 experience were included. Themes were identified and included if they met one of three criteria: demonstrating a consistency of experience between EDs, demonstrating a conflict of approach between emergency departments or encapsulating a unique solution to a common barrier.ResultsSeven clinical leads from type 1 EDs were interviewed. Thematic redundancy was achieved by the sixth interview, and one further interview was performed to confirm. Themes emerged in five categories: departmental reconfiguration, clinical pathways, governance and communication, workforce and personal protective equipment.ConclusionThis paper summarises learning and innovation from a cross-section of EDs during the first UK wave of the COVID-19 pandemic. Common themes centred around the importance of flexibility when reacting to an ever-changing clinical challenge, clear leadership and robust methods of communication. Additionally, experience in managing winter pressures helped inform operational decisions, and ED staff demonstrated incredible resilience in demanding working conditions. Subsequent surges of COVID-19 infections may occur within a more challenging context with no guarantee that there will be an associated reduction in A&E attendance or cessation of elective activity. Future operational planning must therefore take this into consideration.

IntroductionWe aimed to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England.MethodsThis was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics data set for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1 March 2020 and 31 March 2021 were included. In-hospital mortality was the primary outcome of interest. The second wave was identified as starting on 1 September 2020. Multilevel logistic regression modelling was used to investigate the relationship between mortality and demographic, comorbidity and temporal covariates.ResultsOver the 13 months, 374 244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93 701 (25%) died in hospital. Adjusted mortality rates fell from 40%–50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, other Asian and black African ethnic groups.ConclusionsThere was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study.