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Background Patients with Inflammatory bowel disease (IBD) receiving anti-TNF or JAK-inhibitor therapy have attenuated responses to COVID-19 vaccination. We aimed to determine how IBD treatments affect neutralising antibody responses against the currently dominant Omicron BA.4/5 variants. Methods We prospectively recruited 329 adults (68 healthy controls (HC) and 261 IBD) who had received three doses of COVID-19 vaccine at nine UK centres. The IBD population was established (>12 weeks therapy) on either thiopurine (n=60), infliximab (IFX) (n=43), thiopurine & IFX combination (n=46), ustekinumab (n=43), vedolizumab (n=46) or tofacitinib (n=23). Pseudoneutralisation assays were performed and the half maximal inhibitory concentration (NT50) of participant sera was calculated. The primary outcome was anti-SARS-CoV-2 neutralising response against wild-type (WT) virus and the BA.4/5 variant after the second and third doses of anti-SARS-CoV-2 vaccine, stratified by immunosuppressive therapy, adjusting for prior infection, ethnicity, vaccine type and age. Results Heterologous (two doses adenovirus vaccine, third dose mRNA vaccine) and homologous (three doses mRNA vaccine) vaccination strategies significantly increased neutralising titres against both WT SARS-CoV-2 virus and the BA.4/5 variants in HCs and IBD (fig 1). Antibody titres against BA.4/5 were significantly lower than antibodies against WT virus in both groups (Geometric Mean Ratio (GMR) [95% CI], 0.11 [0.09, 0.15], P

Background: COVID-19 vaccine-induced antibody responses are reduced in patients with inflammatory bowel disease (IBD) taking infliximab or tofacitinib after two vaccine doses. We sought to determine whether immunosuppressive treatments were associated with reduced antibody and T cell responses after a third vaccine dose. Methods: 352 adults (72 healthy controls and 280 IBD) from the prospectively recruited study cohort were sampled 28-49 days after a third dose of SARS-CoV-2 vaccine. IBD medications studied included thiopurines (n=65), infliximab (n=46), thiopurine/infliximab combination therapy (n=49), ustekinumab (n=44), vedolizumab (n=50) or tofacitinib (n=26). SARS-CoV-2 spike antibody binding and T cell responses were measured. Findings: Geometric mean [geometric SD] anti-S1 RBD antibody concentrations increased in all study groups following a third dose of vaccine, but were significantly lower in patients treated with infliximab (2736.8 U/mL [4.3]; P

BackgroundAccurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).MethodsProspective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.FindingsData were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, degree of breathlessness, fatigue, and SpO2 at rest (AUC: 0.843), Validation NPV of ‘low risk’ 99.4%.InterpretationBoth RECAP models are a valid tool in the assessment of COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored at home and SpO2 is available, RECAP-O2 is useful to assess the need for further treatment escalation.Research in context panelEvidence before the studyThis study was conceived during the first COVID-19 wave in the UK (March - April 2020), as members of the research team contributed to the development of national clinical guidelines for COVID-19 management in the community and to the Oxford COVID-19 rapid review to track signs and symptoms of COVID-19 internationally. The review was carried out according to Cochrane Collaboration standards for rapid reviews and identified systematic reviews and large-scale observational studies describing the signs and symptoms of COVID-19. Evidence gathered showed worsening of COVID-19 symptoms around the 7th day of disease and challenges in identifying patients with higher likelihood of severity to increase their monitoring. To this end, tools such NEWS2 have been used in the UK to assess COVID-19 patients in primary care, but they do not capture the characteristics of COVID-19 infection and/or are not suitable for community remote assessment. Several COVID-19 risk scores have been developed. QCOVID provides a risk of mortality considering patients’ existing risk factors but does not include acute signs and symptoms. ISARIC 4C Deterioration model has been specifically developed for hospital settings. In England, the NHS has implemented the Oximetry @home strategy to monitor patients with acute COVID-19 deemed at risk (older than 64 years old or with comorbidities) by providing pulse oximeters; however, the criteria for monitoring or for escalation of care have not been validated. There is, therefore, the need to develop a risk prediction score to establish COVID-19 patients’ risk of deterioration to be used in the community for both face to face or remote consultation.Added value of this studyWe developed and validated two COVID-19 specific risk prediction scores. One to be used in the initial remote assessment of patients with acute COVID-19 to assess need for monitoring (RECAP-GP). The second one to assess the need for further treatment escalation and includes peripheral saturation of oxygen among the model predictors (RECAP-O2). To our knowledge, this is the first COVID-19 specific risk prediction score to assess and monitor COVID-19 patients’ risk of deterioration remotely. This will be a valuable resource to complement the use of oximetry in the community clinical decision-making when assessing a patient with acute COVID-19.Implications of all available evidenceTo manage pandemic waves and their demand on healthcare, acute COVID-19 patients require close monitoring in the community and prompt escalation of their treatment. Guidance available so far relies on unvalidated tools and clinician judgement to assess deterioration. COVID-19 specific community-based risk prediction scores such as RECAP may contribute to reducing the uncertainty in the assessment and monitoring of COVID-19 patients, increase safety in clinical practice and improve outcomes by facilitating appropriate treatment escalation.

Following publication of the original article [1], the authors identified an error in the author name of Federica Zito Marino. ● The incorrect author name is: Federico Zito Marino ● The correct author name is: Federica Zito Marino The author group has been updated above and the original article [1] has been corrected.

Introduction Novel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer. Methods Begg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired84 cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate 85 cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference). Results When comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods. Conclusion Statistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modified for low prevalence of the target condition.