Early identification of high-risk individuals for combination monoclonal antibody therapy is feasible by SARS-CoV-2 anti-spike antibody specific lateral flow assay

BackgroundCombination monoclonal antibody therapy has recently been approved for prevention and treatment of severe COVID-19 infection in the UK. Available data suggests benefit is limited to those yet to mount an effective immune response from natural infection or vaccination, but concern exists around ability to make timely assessment of immune status of community-based patients.MethodsHealthcare workers were invited to undergo paired laboratory-based and rapid point-of- care (POC) lateral flow anti-spike antibody testing. Three commercial POC tests were selected to represent currently available testing methods: a split IgM/IgG anti-spike antibody test, an anti-receptor binding domain total antibody test and an anti-spike neutralisation assay. Qualitative POC colourmetric band intensities were independently scored and correlated with quantitative IgG neutralising antibody titres (Abbott Architect SARS-CoV-2 IgG Quant II chemiluminescent microparticle immunoassay [CMIA]). CMIA titres were correlated with the World Health Organisation international reference standard for neutralising antibody. Negative controls were carried out using 2018 pre-pandemic sera and post-pandemic individuals with negative CMIA results (target population).Results190 individuals (median 40 years, IQR 29-49;76.2% female) underwent paired testing, with a further 40 pre-pandemic sera tested. Assays demonstrated high performance characteristics: split IgM/IgG assay sensitivity 96.2% (95%CI 92.4.5–98.5), specificity 100.0% (95%CI 91.8–100.0);anti-receptor binding domain total antibody assay sensitivity 100.0% (95%CI 95.5–100.0), specificity 100.0% (95%CI 69.2–100.0). The neutralising antibody assay had a specificity of 97.0% (95%CI 84.2–99.9%) and strongly correlated with neutralising antibody titre (p<0.001). Probability for matched paired results was significant (McNemar’s p<0.001) while band intensity correlated strongly with neutralising titres (p<0.0001). Positive and negative predictive values for total antibody and neutralising assays were both >99%.ConclusionsPOC assays were found to be reliable predictors of both antibody status and broadly of neutralising antibody titre. Anti-S POC assays have potential to act as suitable alternatives for rapid identification of community patient immune status at presentation.

Early identification of high-risk individuals for monoclonal antibody therapy and prophylaxis is feasible by SARS-CoV-2 anti-spike antibody specific lateral flow assay.

Monoclonal antibody therapy has been approved for prophylaxis and treatment of severe COVID-19 infection. Greatest benefit appears limited to those yet to mount an effective immune response from natural infection or vaccination, but concern exists around ability to make timely assessment of immune status of community-based patients where laboratory-based serodiagnostics predominate. Participants were invited to undergo paired laboratory-based (Abbott Architect SARS-CoV-2 IgG Quant II chemiluminescent microparticle immunoassay) and lateral flow assays (LFA; a split SARS-CoV-2 IgM/IgG and total antibody test) able to detect SARS-CoV-2 anti-spike antibodies. LFA band strength was compared with CMIA titer by log-linear regression. Two hundred individuals (median age 43.5 years, IQR 30-59; 60.5% female) underwent testing, with a further 100 control sera tested. Both LFA band strengths correlated strongly with CMIA antibody titers (P < 0.001). LFAs have the potential to assist in early identification of seronegative patients who may demonstrate the greatest benefit from monoclonal antibody treatment.

COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Assessing a novel, lab-free, point-of-care test for SARS-CoV-2 (CovidNudge): a diagnostic accuracy study.

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 94% [95% CI 85-98]; 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. FUNDING: National Institute of Health Research (NIHR) Imperial Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England, NIHR Biomedical Research Centre Oxford, and DnaNudge.

COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Pooled sputum to optimise the efficiency and utility of rapid, point-of-care molecular SARS-CoV-2 testing.

BACKGROUND: As SARS-CoV-2 testing expands, particularly to widespread asymptomatic testing, high sensitivity point-of-care PCR platforms may optimise potential benefits from pooling multiple patients' samples. METHOD: We tested patients and asymptomatic citizens for SARS-CoV-2, exploring the efficiency and utility of CovidNudge (i) for detection in individuals' sputum (compared to nasopharyngeal swabs), (ii) for detection in pooled sputum samples, and (iii) by modelling roll out scenarios for pooled sputum testing. RESULTS: Across 295 paired samples, we find no difference (p = 0.1236) in signal strength for sputum (mean amplified replicates (MAR) 25.2, standard deviation (SD) 14.2, range 0-60) compared to nasopharyngeal swabs (MAR 27.8, SD 12.4, range 6-56). At 10-sample pool size we find some drop in absolute strength of signal (individual sputum MAR 42.1, SD 11.8, range 13-60 vs. pooled sputum MAR 25.3, SD 14.6, range 1-54; p < 0.0001), but only marginal drop in sensitivity (51/53,96%). We determine a limit of detection of 250 copies/ml for an individual test, rising only four-fold to 1000copies/ml for a 10-sample pool. We find optimal pooled testing efficiency to be a 12-3-1-sample model, yet as prevalence increases, pool size should decrease; at 5% prevalence to maintain a 75% probability of negative first test, 5-sample pools are optimal. CONCLUSION: We describe for the first time the use of sequentially dipped sputum samples for rapid pooled point of care SARS-CoV-2 PCR testing. The potential to screen asymptomatic cohorts rapidly, at the point-of-care, with PCR, offers the potential to quickly identify and isolate positive individuals within a population "bubble".

Procalcitonin to Guide Antibacterial Prescribing in Patients Hospitalised with COVID-19.

Antibacterial prescribing in patients presenting with COVID-19 remains discordant to rates of bacterial co-infection. Implementing diagnostic tests to exclude bacterial infection may aid reduction in antibacterial prescribing. (1) Method: A retrospective observational analysis was undertaken of all hospitalised patients with COVID-19 across a single-site NHS acute Trust (London, UK) from 1 December 2020 to 28 February 2021. Electronic patient records were used to identify patients, clinical data, and outcomes. Procalcitonin (PCT) serum assays, where available on admission, were analysed against electronic prescribing records for antibacterial prescribing to determine relationships with a negative PCT result (<25 mg/L) and antibacterial course length. (2) Results: Antibacterial agents were initiated on admission in 310/624 (49.7%) of patients presenting with COVID-19. A total of 33/74 (44.5%) patients with a negative PCT on admission had their treatment stopped within 24 h. A total of 6/49 (12.2%) patients were started on antibacterials, but a positive PCT saw their treatment stopped. Microbiologically confirmed bacterial infection was low (19/594; 3.2%) and no correlation was seen between PCT and culture positivity (p = 1). Lower mortality (15.6% vs. 31.4%; p = 0.049), length of hospital stay (7.9 days vs. 10.1 days; p = 0.044), and intensive care unit (ICU) admission (13.9% vs. 40.8%; p = 0.001) was noted among patients with low PCT. (3) Conclusions: This retrospective analysis of community acquired COVID-19 patients demonstrates the potential role of PCT in excluding bacterial co-infection. A negative PCT on admission correlates with shorter antimicrobial courses, early cessation of therapy, and predicts lower frequency of ICU admission. Low PCT may support decision making in cessation of antibacterials at the 48-72 h review.

Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis.

BACKGROUND: We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across five London hospitals. METHODS: A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across five acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation. RESULTS: One hundred nineteen thousand five hundred eighty-four blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst all CoNS BSI were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p = 0.013), CoNS central line associated BSIs (CLABSI) (p < 0.01) and CoNS non-CLABSI (p < 0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p = 0.631) and S. aureus (p = 0.617) BSI did not vary significant throughout the study period. CONCLUSIONS: Significantly fewer than expected Enterobacterales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, with evidence of increased CLABSI, but also likely contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.

3 Clinical implications of the differential antibody response in mild-moderate SARS-CoV-2: a prospective multi-centre cross-sectional study

IntroductionSerological testing can augment delayed case identification programmes for Severe Acute Respiratory Syndrome coronoravirus-2 (SARS-CoV-2). Immunoassays employ anti-nucleocapsid (anti-NP;the majority) or potentially neutralising anti-spike (including anti-receptor binding domain;anti-RBD) antibody targets, yet correlation between assays and variability arising from disease symptomatology remains unclear. We explore these possibly differential immune responses across the disease spectrum.MethodsA multicentre prospective study was undertaken via a SARS-CoV-2 delayed case identification programme (08 May-11 July 2020). Matched samples were tested for anti-NP and anti-RBD (utilising an ‘inhouse’ double-antigen bridged assay), reactivity expressed as test/cut-off binding ratios (BR) and results compared. A multivariate linear regression model analysed age, sex, symptomatology, PCR positivity, anti-NP, and anti-RBD BRs. Participants were followed up for possible reinfection.Results902 individuals underwent matched testing;109 were SARS-CoV-2 PCR swab positive. Anti-NP, anti-RBD immunoassay agreement was 87.5% (95% CI 85.3–89.6), with BRs strongly correlated (R=0.75). PCR confirmed cases were more frequently identified by anti-RBD (sensitivity 108/109, 99.1%, 95% CI 95.0–100.0) than anti-NP (102/109, 93.6%, 95% CI 87.2–97.4). Anti-RBD identified an additional 83/325 (25.5%) cases in those seronegative for anti-NP. Presence of anti-NP (p<0.0001), fever (p=0.005), or anosmia (p=0.002) were all significantly associated with an increased anti-RBD BR. Age was associated with reduced anti-RBD BR (p=0.052). Three cases with evidence of seroconversion (anti-RBD seropositive) presented with subsequent reactive PCR results, two of which coincided with first time onset of Public Heath England SARS-CoV-2 symptoms.ConclusionsSARS-CoV-2 anti-RBD shows significant correlation with anti-NP for absolute seroconversion, and BRs. Higher BRs are seen in symptomatic individuals with significantly higher levels seen in those with fever and anosmia. The degree of discordant results (12.5%) limits the use of anti-NP as a stand-alone for delayed case finding programmes. Similarly, this discordance limits the utility of non-neutralising anti-NP assays in place of potentially neutralising anti-RBD to infer possible immunity.** this abstract presentation was awarded an Honourable Mention

SARS-CoV-2 lateral flow assays for possible use in national covid-19 seroprevalence surveys (React 2): diagnostic accuracy study.

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.

Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing.

BACKGROUND: To explore and describe the current literature surrounding bacterial/fungal coinfection in patients with coronavirus infection. METHODS: MEDLINE, EMBASE, and Web of Science were searched using broad-based search criteria relating to coronavirus and bacterial coinfection. Articles presenting clinical data for patients with coronavirus infection (defined as SARS-1, MERS, SARS-CoV-2, and other coronavirus) and bacterial/fungal coinfection reported in English, Mandarin, or Italian were included. Data describing bacterial/fungal coinfections, treatments, and outcomes were extracted. Secondary analysis of studies reporting antimicrobial prescribing in SARS-CoV-2 even in absence of coinfection was performed. RESULTS: 1007 abstracts were identified. Eighteen full texts reporting bacterial/fungal coinfection were included. Most studies did not identify or report bacterial/fungal coinfection (85/140; 61%). Nine of 18 (50%) studies reported on COVID-19, 5/18 (28%) on SARS-1, 1/18 (6%) on MERS, and 3/18 (17%) on other coronaviruses. For COVID-19, 62/806 (8%) patients were reported as experiencing bacterial/fungal coinfection during hospital admission. Secondary analysis demonstrated wide use of broad-spectrum antibacterials, despite a paucity of evidence for bacterial coinfection. On secondary analysis, 1450/2010 (72%) of patients reported received antimicrobial therapy. No antimicrobial stewardship interventions were described. For non-COVID-19 cases, bacterial/fungal coinfection was reported in 89/815 (11%) of patients. Broad-spectrum antibiotic use was reported. CONCLUSIONS: Despite frequent prescription of broad-spectrum empirical antimicrobials in patients with coronavirus-associated respiratory infections, there is a paucity of data to support the association with respiratory bacterial/fungal coinfection. Generation of prospective evidence to support development of antimicrobial policy and appropriate stewardship interventions specific for the COVID-19 pandemic is urgently required.

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. METHOD: Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. RESULTS: Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8-91.1 and 90.0%, 95% CI 81.2-95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1-94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7-88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. CONCLUSION: We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.