Wearable tech, AI and clinical teams combine to change the face of clinical trial monitoring
23 January 2023
Publish date: 14 April 2021
An observational study of patients at UCLH and North Middlesex University Hospital published this week in The Lancet Infectious Diseases suggests that the B.1.17. variant of Covid-19 – sometimes known as the UK or Kent variant – is not associated with more severe illness and death, but appears to lead to higher virus load.
The emergence of variants has raised concerns that they could spread more easily and be more deadly, and that vaccines developed based on the original strain might be less effective against them. Preliminary data on B.1.1.7. indicated that it is more transmissible, with some evidence suggesting it could also be associated with increased hospitalisations and deaths. However, because the variant was identified only recently, these studies were limited by the amount of data available.
Findings from the new study, led by Dr Eleni Nastouli of UCLH and UCL Great Ormond Street Institute of Child Health which spanned the period between September and December 2020, when B.1.1.7. emerged and began to spread across parts of England, provide important insights into its characteristics that will help inform public health, clinical, and research responses to this and other COVID-19 variants.
The whole-genome sequencing and cohort study involved Covid-19 patients admitted to UCLH and North Middlesex University Hospital between 9 November and 20 December 2020. This was a critical time point when both the original and B.1.1.7. variants were circulating in London, the vaccination programme was just starting, and before a significant surge in cases in early 2021 caused a strain on the NHS.
The authors compared illness severity in people with and without B.1.1.7 and calculated viral load. Among 341 patients who had their COVID-19 test swabs sequenced, 58% (198/341) had B.1.1.7 and 42% (143/341) had a non-B.1.1.7. infection (two patients’ data were excluded from further analysis).
No evidence of an association between the variant and increased disease severity was detected, with 36% (72/198) of B.1.1.7. patients becoming severely ill or dying, compared with 38% (53/141) of those with a non-B.1.1.7 strain.
Patients with the variant tended to be younger, with 55% (109/198) of infections in people under 60 compared with 40% (57/141) for those who did not have B.1.1.7. Infections with B.1.1.7. occurred more frequently in ethnic minority groups, accounting for 50% (86/172) of cases that included ethnicity data, compared with 29% (35/120) for non-B.1.1.7 strains.
Those with B.1.1.7 were no more likely to experience severe disease after accounting for hospital, sex, age, ethnicity and underlying conditions.
Those with B.1.1.7. were no more likely to die than patients with a different strain, with 16% (31/198) of B.1.1.7. patients dying within 28 days compared with 17% (24/141) for those with a non-B.1.1.7. infection.
More patients with B.1.1.7 were given oxygen than those with a non-B.1.1.7. strain (44%, 88/198 vs 30%, 42/141, respectively). However, the authors say this is not a clear measure of disease severity, as patients may have received nasal prong oxygen for reasons unrelated to COVID-19, or as a consequence of underlying conditions.
To gain insights into the transmissibility of B.1.1.7., the authors used data generated by PCR testing of patient swabs to predict their viral load – the amount of virus in a person’s nose and throat. The data analysed – known as PCR Ct values and genomic read depth – indicated that B.1.1.7. samples tended to contain greater quantities of virus than non-B.1.1.7. swabs.
Dr Nastouli said: “One of the real strengths of our study is that it ran at the same time that B.1.1.7. was emerging and spreading throughout London and the south of England. Analysing the variant before the peak of hospital admissions and any associated strains on the health service gave us a crucial window of time to gain vital insights into how B.1.1.7. differs in severity or death in hospitalised patients from the strain of the first wave. Our study is the first in the UK to utilise whole genome sequencing data generated in real time and embedded in an NHS clinical service and integrated granular clinical data.
“We hope that this study provides an example of how such studies can be done for the benefit of patients throughout the NHS. As more variants continue to emerge, using this approach could help us better understand their key characteristics and any additional challenges that they may pose to public health.”
Dr Mariyam Mirfenderesky, Consultant Microbiologist at NMUH, said: “Collaborative clinical networks such as ours have demonstrated how powerful they can be in monitoring changing sector epidemiology, and the evolving clinical picture in relation to SARS-C0V-2. Real-time whole genome sequencing was used to inform local transmission events, facilitating a unique understanding of the evolving pandemic within our hospitals.”
Dr Catherine Houlihan, Consultant Virologist at UCLH, said: “We took the opportunity to combine the numbers of patients we were seeing with the B.1.1.7 variant in our two hospitals which provided enough statistical power to examine the severity in this hospitalised population. Since we expect more variants over the next months to year, including potentially, the South African variant, we aim to continue to provide this important and timely public health research.”
Dr Jeronimo Moreno-Cuesta, Dr Nish Arulkumaran and Professor Mervyn Singer, Consultants in Critical Care at NMUH and UCLH, said: “Whilst it is reassuring that there is no evidence of increased mortality associated with the B.1.1.7 variant, healthcare resource and patient mortality remains substantial. These data are fundamental in informing hospital and intensive care unit planning in the event of another surge. As the emergence of other variants may have implications patient management and outcome, ongoing clinical vigilance and research are imperative.”
Professor Deenan Pillay, Professor in Virology at UCL, said: “It is essential to rapidly determine the clinical implications of new viral variants as they emerge. Placed within the context of other findings, our results suggest that even though the B.1.1.7 variants may lead to higher rates of hospitalisation, our current clinical practice can manage these variants as well as earlier circulating COVID-19 viruses. Ongoing trials of new therapies aim to improve outcomes further. These important findings were made possible through the ability to capture real time clinical and laboratory data within UCLH and North Middlesex Hospital through ongoing investment by the NIHR UCLH/UCL Biomedical Research Centre and the Engineering and Physical Sciences Research Council -funded i-sense consortium on Early Warning Sensing Systems for Infectious Diseases.”
Professor Rachel McKendry, Professor at the UCL London Centre of Nanotechnology and Director of iSense: "This important study highlights the power of genomics and interdisciplinary science to track the impact of emerging SARS-CoV- 2 variants, and builds on our strategic collaboration between the i-sense EPSRC IRC in Early Warning Sensing Systems for Infectious Diseases and the Advanced Pathogen Diagnostics Unit (APDU)."
The authors acknowledge some limitations to their study. Disease severity was captured within 14 days of a positive COVID-19 test, so patients who may have deteriorated after 14 days may have been missed in the analysis, though the authors sought to mitigate this by capturing deaths at 28 days. The analyses also did not take account of any other treatments that patients were receiving – such as steroids, antiviral medications, or convalescent plasma – or the possibility that some patients may have received ventilation for reasons other than Covid-19.
A separate observational study published at the same time in The Lancet Public Health using data logged by 37,000 UK users of a self-reporting COVID-19 symptom app found no evidence that B.1.1.7. altered symptoms or likelihood of experiencing long Covid.
Image: Josh / Adobe Stock
23 January 2023
10 January 2023