BackgroundAccurate antibody tests are essential to monitor the SARS-CoV-2 pandemic. Lateral flow immunoassays (LFIAs) can deliver testing at scale. However, reported performance varies, and sensitivity analyses have generally been conducted on serum from hospitalised patients. For use in community testing, evaluation of finger-prick self-tests, in non-hospitalised individuals, is required.MethodsSensitivity analysis was conducted on 276 non-hospitalised participants. All had tested positive for SARS-CoV-2 by reverse transcription PCR and were ≥21 days from symptom onset. In phase I, we evaluated five LFIAs in clinic (with finger prick) and laboratory (with blood and sera) in comparison to (1) PCR-confirmed infection and (2) presence of SARS-CoV-2 antibodies on two ‘in-house’ ELISAs. Specificity analysis was performed on 500 prepandemic sera. In phase II, six additional LFIAs were assessed with serum.Findings95% (95% CI 92.2% to 97.3%) of the infected cohort had detectable antibodies on at least one ELISA. LFIA sensitivity was variable, but significantly inferior to ELISA in 8 out of 11 assessed. Of LFIAs assessed in both clinic and laboratory, finger-prick self-test sensitivity varied from 21% to 92% versus PCR-confirmed cases and from 22% to 96% versus composite ELISA positives. Concordance between finger-prick and serum testing was at best moderate (kappa 0.56) and, at worst, slight (kappa 0.13). All LFIAs had high specificity (97.2%–99.8%).InterpretationLFIA sensitivity and sample concordance is variable, highlighting the importance of evaluations in setting of intended use. This rigorous approach to LFIA evaluation identified a test with high specificity (98.6% (95%CI 97.1% to 99.4%)), moderate sensitivity (84.4% with finger prick (95% CI 70.5% to 93.5%)) and moderate concordance, suitable for seroprevalence surveys.

Background Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing. Methods Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing. Findings We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied by group (self-referred healthcare workers 93% [95% CI 84-98]; patients in the emergency department 100% [48-100]; and hospital inpatient admissions 100% [29-100]). Specificity was consistent between groups (self-referred health-care workers 100% [95% CI 98-100%]; patients in the emergency department 100% [69-100]; and hospital inpatient admissions 100% [96-100]). Point of care testing performance was similar during a period of high background prevalence of laboratory positive tests (25% [95% 20-31] in April, 2020) and low prevalence (3% [95% 1-9] in inpatient screening). Amplification of viral nucleocapsid (n1, n2, and n3) and envelope protein gene (e-gene) were most sensitive for detection of spiked SARS-CoV-2 RNA. Interpretation The CovidNudge platform was a sensitive, specific, and rapid point of care test for the presence of SARS-CoV-2 without laboratory handling or sample pre-processing. The device, which has been implemented in UK hospitals since May, 2020, could enable rapid decisions for clinical care and testing programmes.

Objective To evaluate the performance of new lateral flow immunoassays (LFIAs) suitable for use in a national coronavirus disease 2019 (covid-19) seroprevalence programme (real time assessment of community transmission 2—React 2). Design Diagnostic accuracy study. Setting Laboratory analyses were performed in the United Kingdom at Imperial College, London and university facilities in London. Research clinics for finger prick sampling were run in two affiliated NHS trusts. Participants Sensitivity analyses were performed on sera stored from 320 previous participants in the React 2 programme with confirmed previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Specificity analyses were performed on 1000 prepandemic serum samples. 100 new participants with confirmed previous SARS-CoV-2 infection attended study clinics for finger prick testing. Interventions Laboratory sensitivity and specificity analyses were performed for seven LFIAs on a minimum of 200 serum samples from participants with confirmed SARS-CoV-2 infection and 500 prepandemic serum samples, respectively. Three LFIAs were found to have a laboratory sensitivity superior to the finger prick sensitivity of the LFIA currently used in React 2 seroprevalence studies (84%). These LFIAs were then further evaluated through finger prick testing on participants with confirmed previous SARS-CoV-2 infection: two LFIAs (Surescreen, Panbio) were evaluated in clinics in June-July 2020 and the third LFIA (AbC-19) in September 2020. A spike protein enzyme linked immunoassay and hybrid double antigen binding assay were used as laboratory reference standards. Main outcome measures The accuracy of LFIAs in detecting immunoglobulin G (IgG) antibodies to SARS-CoV-2 compared with two reference standards. Results The sensitivity and specificity of seven new LFIAs that were analysed using sera varied from 69% to 100%, and from 98.6% to 100%, respectively (compared with the two reference standards). Sensitivity on finger prick testing was 77% (95% confidence interval 61.4% to 88.2%) for Panbio, 86% (72.7% to 94.8%) for Surescreen, and 69% (53.8% to 81.3%) for AbC-19 compared with the reference standards. Sensitivity for sera from matched clinical samples performed on AbC-19 was significantly higher with serum than finger prick at 92% (80.0% to 97.7%, P=0.01). Antibody titres varied considerably among cohorts. The numbers of positive samples identified by finger prick in the lowest antibody titre quarter varied among LFIAs. Conclusions One new LFIA was identified with clinical performance suitable for potential inclusion in seroprevalence studies. However, none of the LFIAs tested had clearly superior performance to the LFIA currently used in React 2 seroprevalence surveys, and none showed sufficient sensitivity and specificity to be considered for routine clinical use.

Child Health GP Hubs increase the connections between secondary and primary care, reduce secondary care usage and receive high patient satisfaction ratings while providing learning for professionals.

Carboxypeptidase E is a peptide processing enzyme, involved in cleaving numerous peptide precursors, including neuropeptides and hormones involved in appetite control and glucose metabolism. Exome sequencing of a morbidly obese female from a consanguineous family revealed homozygosity for a truncating mutation of the CPE gene (c.76_98del; p.E26RfsX68). Analysis detected no CPE expression in whole blood-derived RNA from the proband, consistent with nonsense-mediated decay. The morbid obesity, intellectual disability, abnormal glucose homeostasis and hypogonadotrophic hypogonadism seen in this individual recapitulates phenotypes in the previously described fat/fat and Cpe knockout mouse models, evidencing the importance of this peptide/hormone-processing enzyme in regulating body weight, metabolism, and brain and reproductive function in humans.

Background There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand. Methods We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators. Results We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Conclusions Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one.

Implementing a Check and Correct checklist led to an improvement in the quality of prescription writing. Although a change in culture may be needed to maximise its potential, we would recommend its more widespread use and evaluation.

This study considers different ways of maximising learning opportunities during ward rounds, with particular emphasis on the strengths and challenges of the paediatric environment. The focus is on the most common types of ward round - in acute units involving predominantly trainees - but we hope there will also be much that will interest those who work in other settings such as community clinics. Alongside a review of the best available evidence from the literature, and underpinned by educational theory, suggestions for maximising learning on ward rounds are presented. Many of these ideas were generated from working in small groups with over 90 experienced paediatricians, each with particular experience and interest in medical education, as part of the Royal College of Paediatrics and Child Health's Paediatric Educators' Programme, the PEP.