ORCID Profile
0000-0002-4035-4562
Current Organisation
University of Oxford
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Cold Spring Harbor Laboratory
Date: 05-07-2021
DOI: 10.1101/2021.07.04.21259945
Abstract: Continuous positive airways pressure (CPAP) and high-flow nasal oxygen (HFNO) are considered ‘aerosol-generating procedures’ (AGPs) in the treatment of COVID-19. We aimed to measure air and surface environmental contamination of SARS-CoV-2 virus when CPAP and HFNO were used, compared with supplemental oxygen, to investigate the potential risks of viral transmission to healthcare workers and patients. 30 hospitalised patients with COVID-19 requiring supplemental oxygen, with a fraction of inspired oxygen ≥0.4 to maintain oxygen saturations ≥94%, were prospectively enrolled into an observational environmental s ling study. Participants received either supplemental oxygen, CPAP or HFNO (n=10 in each group). A nasopharyngeal swab, three air and three surface s les were collected from each participant and the clinical environment. RT qPCR analyses were performed for viral and human RNA, and positive/suspected-positive s les were cultured for the presence of biologically viable virus. Overall 21/30 (70%) of participants tested positive for SARS-CoV-2 RNA in the nasopharynx. In contrast, only 4/90 (4%) and 6/90 (7%) of all air and surface s les tested positive (positive for E and ORF1a) for viral RNA respectively, although there were an additional 10 suspected-positive s les in both air and surfaces s les (positive for E or ORF1a). CPAP/HFNO use or coughing was not associated with significantly more environmental contamination. Only one nasopharyngeal s le was culture positive. The use of CPAP and HFNO to treat moderate/severe COVID-19 was not associated with significantly higher levels of air or surface viral contamination in the immediate care environment.
Publisher: Cold Spring Harbor Laboratory
Date: 25-07-2020
DOI: 10.1101/2020.07.17.20155218
Abstract: ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 in iduals from 1701 partner institutions and networks across 60 countries. The comprehensive analyses detailed in this report includes hospitalised in iduals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 699,014 cases who meet eligibility criteria for this report, selected findings include: median age of 58 years, with an approximately equal (50/50) male:female sex distribution 29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age the most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time the five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports age-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above 16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%) antibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics use of corticosteroids was reported in 24% of all patients for whom data were available (677,012) in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids outcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: esearch/covid-19-clinical-research-resources/evidence-reports/
Publisher: Cold Spring Harbor Laboratory
Date: 16-08-2020
DOI: 10.1101/2020.08.14.20168088
Abstract: Severe COVID-19 is characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied simple machine learning techniques to a large prospective cohort of hospitalised patients with COVID-19 identify clinically meaningful sub-groups. We obtained structured clinical data on 59 011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25 477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33 534 cases recruited to ISARIC-4C, and in 4 445 cases recruited to a separate study of community cases. Unsupervised clustering identified distinct sub-groups. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were common, and a subgroup of patients reported few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom clusters were highly consistent in replication analysis using a further 35446 in iduals subsequently recruited to ISARIC-4C. Similar patterns were externally verified in 4445 patients from a study of self-reported symptoms of mild disease. The large scale of the ISARIC-4C study enabled robust, granular discovery and replication of patient clusters. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four patterns are usefully distinct from the core symptom groups: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms. These observations deepen our understanding of COVID-19 and will influence clinical diagnosis, risk prediction, and future mechanistic and clinical studies. Medical Research Council National Institute Health Research Well-come Trust Department for International Development Bill and Melinda Gates Foundation Liverpool Experimental Cancer Medicine Centre.
Publisher: Cold Spring Harbor Laboratory
Date: 27-09-2022
DOI: 10.1101/2022.09.25.22280081
Abstract: Optimising statistical power in early-stage trials and observational studies accelerates discovery and improves the reliability of results. Ideally, intermediate outcomes should be continuously distributed and lie on the causal pathway between an intervention and a definitive outcome such as mortality. In order to optimise power for an intermediate outcome in the RECOVERY trial, we devised and evaluated a modification to a simple, pragmatic measure of oxygenation function - the S a O 2 / F I O 2 (S/F) ratio. We demonstrate that, because of the ceiling effect in oxyhaemoglobin saturation, S/F ceases to reflect pulmonary oxygenation function at high values of S a O 2 . Using synthetic and real data, we found that the correlation of S/F with a gold standard ( P a O 2 / F I O 2 , P/F ratio) improved substantially when measurements with S a O 2 ≥ 0.94 are excluded (Spearman r , synthetic data: S/F : 0.31 S/F 94 : 0.85). We refer to this measure as S/F 94 . In order to test the underlying assumptions and validity of S/F 94 as a predictor of a definitive outcome (mortality), we collected an observational dataset including over 39,000 hospitalised patients with COVID-19 in the ISARIC4C study. We first demonstrated that S/F 94 is predictive of mortality in COVID-19. We then compared the s le sizes required for trials using different outcome measures ( S/F 94 , the WHO ordinal scale, sustained improvement at day 28 and mortality at day 28) ensuring comparable effect sizes. The smallest s le size was needed when S/F 94 on day 5 was used as an outcome measure. To facilitate future study design, we provide an online user interface to quantify real-world power for a range of outcomes and inclusion criteria, using a synthetic dataset retaining the population-level clinical associations in real data accrued in ISARIC4C ndpoints . We demonstrated that S/F 94 is superior to S/F as a measure of pulmonary oxygenation function and is an effective intermediate outcome measure in COVID-19. It is a simple and non-invasive measurement, representative of disease severity and provides greater statistical power to detect treatment differences than other intermediate endpoints.
Publisher: Wiley
Date: 05-09-2022
DOI: 10.1111/IRV.13039
Abstract: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID‐19‐hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory‐confirmed COVID‐19, in the ISARIC prospective cohort study database, meeting widely used case definitions. Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non‐laboratory‐confirmed test result were excluded. A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased. The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Jake Dunning.