ABSTRACT
BackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes: 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. InterpretationWe present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources. FundingMedecins Sans Frontieres, India. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of "SARS-CoV-2" AND ["biomarker" OR "prognosis"]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development. Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge. Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.
Subject(s)
COVID-19 , Severe Acute Respiratory Syndrome , DeathABSTRACT
Background: Use of heterologous prime-boost COVID-19 vaccine schedules could facilitate mass COVID-19 immunisation, however we have previously reported that heterologous schedules incorporating an adenoviral-vectored vaccine (ChAd, Vaxzevria, Astrazeneca) and an mRNA vaccine (BNT, Comirnaty, Pfizer) at a 4-week interval are more reactogenic than homologous schedules. Here we report the immunogenicity of these schedules. Methods: Com-COV (ISRCTN: 69254139, EudraCT: 2020-005085-33) is a participant-blind, non-inferiority trial evaluating vaccine reactogenicity and immunogenicity. Adults ≥ 50 years, including those with well-controlled comorbidities, were randomised across eight groups to receive ChAd/ChAd, ChAd/BNT, BNT/BNT or BNT/ChAd, administered at 28- or 84-day intervals.The primary endpoint is geometric mean ratio (GMR) of serum SARS-CoV-2 anti-spike IgG levels (ELISA) at one-month post boost between heterologous and homologous schedules given the same prime vaccine. We tested non-inferiority of GMR using a margin of 0.63. The primary analysis was on a per-protocol population, who were seronegative at baseline. Safety analyses were performed amongst participants receiving at least one dose of study vaccines.Findings: In February 2021, 830 participants were enrolled and randomised, including 463 with a 28-day prime-boost interval whose results are reported in this paper. Participant mean age was 57.8 years, 45.8% were female, and 25.3% from ethnic minorities.The geometric mean concentration (GMC) of day 28 post-boost SARS-CoV-2 anti-spike IgG in ChAd/BNT recipients (12,906 ELU/ml) was non-inferior to that in ChAd/ChAd recipients (1,392 ELU/ml) with a geometric mean ratio (GMR) of 9.2 (one-sided 97.5% CI: 7.5, ∞). In participants primed with BNT, we failed to show non-inferiority of the heterologous schedule (BNT/ChAd, GMC 7,133 ELU/ml) against the homologous schedule (BNT/BNT, GMC 14,080 ELU/ml) with a GMR of 0.51 (one-sided 97.5% CI: 0.43, ∞). Geometric mean of T cell response at 28 days post boost in the ChAd/BNT group was 185 SFC/106 PBMCs (spot forming cells/106 peripheral blood mononuclear cells) compared to 50, 80 and 99 SFC/106 PBMCs for ChAd/ChAd, BNT/BNT, and BNT/ChAd, respectively. There were four serious adverse events across all groups, none of which were considered related to immunisation.Interpretation: Despite the BNT/ChAd regimen not meeting non-inferiority criteria, the GMCs of both heterologous schedules were higher than that of a licensed vaccine schedule (ChAd/ChAd) with proven efficacy against COVID-19 disease and hospitalisation. These data support flexibility in the use of heterologous prime-boost vaccination using ChAd and BNT COVID-19 vaccines.Trial Registration: The trial is registered at www.isrctn.com as ISRCTN: 69254139.Funding: Funded by the UK Vaccine Task Force (VTF) and National Institute for Health Research (NIHR)Declaration of Interest: MDS acts on behalf of the University of Oxford as an Investigator on studies funded or sponsored by vaccine manufacturers including AstraZeneca, GlaxoSmithKline, Pfizer, Novavax, Janssen, Medimmune, and MCM vaccines. He receives no personal financial payment for this work. JSN-V-T is seconded to the Department of Health and Social Care, England. AMC and DMF are investigators on studies funded by Pfizer and Unilever. They receive no personal financial payment for this work. AF is a member of the Joint Committee on Vaccination and Immunisation and Chair of the WHO European Technical Advisory Group of Experts (ETAGE) on Immunisation. He is an investigator and/or provides consultative advice on clinical trials and studies of COVID-19 vaccines produced by AstraZeneca, Janssen, Valneva, Pfizer and Sanofi and of other vaccines from these and other manufacturers including GSK, VPI, Takeda and Bionet Asia. He receives no personal remuneration or benefits for any of this work. SNF acts on behalf of University Hospital Southampton NHS Foundation Trust as an Investigator and/or providing consultative advice on clinical trials and studies of COVID-19 and other vaccines funded or sponsored by vaccine manufacturers including Janssen, Pfizer, AstraZeneca, GlaxoSmithKline, Novavax, Seqirus, Sanofi, Medimmune, Merck and Valneva vaccines and antimicrobials. He receives no personal financial payment for this work. PTH acts on behalf of St. George’s University of London as an Investigator on clinical trials of COVID-19 vaccines funded or sponsored by vaccine manufacturers including Janssen, Pfizer, AstraZeneca, Novavax and Valneva. He receives no personal financial payment for this work. CAG acts on behalf of University Hospitals Birmingham NHS Foundation Trust as an Investigator on clinical trials and studies of COVID-19 and other vaccines funded or sponsored by vaccine manufacturers including Janssen, Pfizer, AstraZeneca, Novavax, CureVac, Moderna, and Valneva vaccines, and receives no personal financial payment for this work. VL acts on behalf of University College London Hospitals NHS Foundation Trust as an Investigator on clinical trials of COVID-19 vaccines funded or sponsored by vaccine manufacturers including Pfizer, AstraZeneca and Valneva. He receives no personal financial payment for this work. TL is named as an inventor on a patent application covering this SARS-CoV-2 vaccine and is an occasional consultant to Vaccitech unrelated to this work. Oxford University has entered into a partnership with AstraZeneca for further development of ChAdOx1 nCoV-19Ethical Approval: The trial was reviewed and approved by the South-Central Berkshire Research Ethics Committee (21/SC/0022), the University of Oxford, and the Medicines and Healthcare Products Regulatory Agency MHRA). An independent data safety monitoring board (DSMB) reviewed safety data, and local trial- site physicians provided oversight of all adverse events in real-time.
Subject(s)
COVID-19ABSTRACT
Background: With the SARS-CoV-2 pandemic gripping most of the globe, healthcare and economic recovery strategies are being explored currently as a matter of urgency. Methods: We adopted the epidemiological model which informs United Kingdom government's national policy on the SARS-CoV-2 pandemic and used it in a local context and show how projections in terms of presentations of symptomatic patients in a variety of settings differ depending on local demographics. Setting: England, United Kingdom, population level modelled 3.2m Results: We clearly demonstrate that there is significant difference in the way the modelling behaves in terms of potential subsequent waves for levels of demand on the health and care systems. This provides evidence of differing timeline of peak demands and clearly shows varying levels of demand throughout the four modelled sub-geographies. Conclusions: The study results clearly outline the expediency of national modelling being adopted to reflect local health and care systems. We urge readers to ensure that any national policy is appropriately adopted to suit local health and care systems. In addition, we share our methodology to ensure other professionals could replicate this study elsewhere. Background National governments are forced to take urgent action with national policy restricting social interactions and businesses to temporarily close. In order to fully understand how the limited health and care resources could be targeted in response to this pandemic, we need to create a thorough understanding of the impact of national policy on local health and care systems. The guiding principle and understanding here is that health and care services are provided locally not nationally, not in aggregate but to individuals. Therefore, we conducted a study using existing models which are used to inform national policy and adapted these to consider local nuances to see whether such projective model would behave differently; showing how projections in terms of presentations of symptomatic patients in a variety of settings differ depending on local demographics. This v2 includes some minor revisions and clarifications as well as some additions in terms of relevant references.