Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London.

BACKGROUND: The Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 emerged and became the dominant circulating variant in the UK in late 2020. Current literature is unclear on whether the Alpha variant is associated with increased severity. We linked clinical data with viral genome sequence data to compare admitted cases between SARS-CoV-2 waves in London and to investigate the association between the Alpha variant and the severity of disease. METHODS: Clinical, demographic, laboratory and viral sequence data from electronic health record systems were collected for all cases with a positive SARS-CoV-2 RNA test between 13 March 2020 and 17 February 2021 in a multisite London healthcare institution. Multivariate analysis using logistic regression assessed risk factors for severity as defined by hypoxia at admission. RESULTS: There were 5810 SARS-CoV-2 RNA-positive cases of which 2341 were admitted (838 in wave 1 and 1503 in wave 2). Both waves had a temporally aligned rise in nosocomial cases (96 in wave 1 and 137 in wave 2). The Alpha variant was first identified on 15 November 2020 and increased rapidly to comprise 400/472 (85%) of sequenced isolates from admitted cases in wave 2. A multivariate analysis identified risk factors for severity on admission, such as age (OR 1.02, 95% CI 1.01 to 1.03, for every year older; p<0.001), obesity (OR 1.70, 95% CI 1.28 to 2.26; p<0.001) and infection with the Alpha variant (OR 1.68, 95% CI 1.26 to 2.24; p<0.001). CONCLUSIONS: Our analysis is the first in hospitalised cohorts to show increased severity of disease associated with the Alpha variant. The number of nosocomial cases was similar in both waves despite the introduction of many infection control interventions before wave 2.

The impact of hospital attendance on COVID-19 infection in cancer patients: an assessment of data from Guy's Cancer.

Objective: The authors monitored positivity rates of asymptomatic SARS-CoV-2 tests during the second wave of COVID-19 at Guy's Cancer Centre. Methods: Logistic regression was used to investigate factors associated with asymptomatic COVID-19 positivity rates between 1 December 2020 and 28 February 2021 (n = 1346). Results: Living 20-40 km and 40-60 km from the alpha variant was associated with a reduced chance of a positive SARS-CoV-2 test compared with 0-20 km (odds ratio [OR]: 0.20; CI: 0.07-0.53 and OR: 0.38; CI: 0.15-0.98, respectively). An increased number of tests was associated with an increased chance of a positive SARS-CoV-2 test (OR: 1.10; CI: 1.04-1.16). Conclusion: The COVID-19 positivity rate of asymptomatic cancer patients is partly due to increased testing, with some contribution from the proximity of the patient population to the epicenter of the alpha variant.

The UK's second wave of COVID-19 was partly driven by the emergence of the alpha variant in the southeast of England in November 2020, spreading farther to become the predominant variant across England in December 2020. The alpha variant is associated with a greater transmissibility rate, posing an increased risk to the vulnerable population. This raised concerns about the welfare of cancer patients, as the disease and its treatment can lower one's ability to fight infection. This resulted in some cancer treatments being interrupted or stopped on the grounds of clinical safety and some follow-up care being disrupted. In order to investigate the factors associated with asymptomatic COVID-19 positivity rates between 1 December 2020 and 28 February 2021, the authors gathered information on the number of tests taken per cancer patient at Guy's and extracted data from Guy's approved research database, which houses all routinely collected clinical data on cancer patients. This included demographic data, such as post code and age, as well as number of visits to the hospital. From their analysis, the authors concluded that living closer to the epicenter of the alpha variant was associated with a high positivity rate; also, the more tests taken, the more likely the patients are to test positive. Therefore, the authors can conclude that attending the hospital does not increase the risk of transmission.

Incidence, Risk Factors, and Effect on Allograft Survival of Glomerulonephritis Post-transplantation in a United Kingdom Population: Cohort Study.

Background: Post-transplant glomerulonephritis (PTGN) has been associated with inferior long-term allograft survival, and its incidence varies widely in the literature. Methods: This is a cohort study of 7,623 patients transplanted between 2005 and 2016 at four major transplant UK centres. The diagnosis of glomerulonephritis (GN) in the allograft was extracted from histology reports aided by the use of text-mining software. The incidence of the four most common GN post-transplantation was calculated, and the risk factors for disease and allograft outcomes were analyzed. Results: In total, 214 patients (2.8%) presented with PTGN. IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and membranoproliferative/mesangiocapillary GN (MPGN/MCGN) were the four most common forms of post-transplant GN. Living donation, HLA DR match, mixed race, and other ethnic minority groups were associated with an increased risk of developing a PTGN. Patients with PTGN showed a similar allograft survival to those without in the first 8 years of post-transplantation, but the results suggest that they do less well after that timepoint. IgAN was associated with the best allograft survival and FSGS with the worst allograft survival. Conclusions: PTGN has an important impact on long-term allograft survival. Significant challenges can be encountered when attempting to analyze large-scale data involving unstructured or complex data points, and the use of computational analysis can assist.

National Institute for Health Research Health Informatics Collaborative: development of a pipeline to collate electronic clinical data for viral hepatitis research.

OBJECTIVE: The National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) is a programme of infrastructure development across NIHR Biomedical Research Centres. The aim of the NIHR HIC is to improve the quality and availability of routinely collected data for collaborative, cross-centre research. This is demonstrated through research collaborations in selected therapeutic areas, one of which is viral hepatitis. DESIGN: The collaboration in viral hepatitis identified a rich set of datapoints, including information on clinical assessment, antiviral treatment, laboratory test results and health outcomes. Clinical data from different centres were standardised and combined to produce a research-ready dataset; this was used to generate insights regarding disease prevalence and treatment response. RESULTS: A comprehensive database has been developed for potential viral hepatitis research interests, with a corresponding data dictionary for researchers across the centres. An initial cohort of 960 patients with chronic hepatitis B infections and 1404 patients with chronic hepatitis C infections has been collected. CONCLUSION: For the first time, large prospective cohorts are being formed within National Health Service (NHS) secondary care services that will allow research questions to be rapidly addressed using real-world data. Interactions with industry partners will help to shape future research and will inform patient-stratified clinical practice. An emphasis on NHS-wide systems interoperability, and the increased utilisation of structured data solutions for electronic patient records, is improving access to data for research, service improvement and the reduction of clinical data gaps.