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Background Health-care workers constitute a high-risk population for acquisition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Capacity for acute diagnosis via PCR testing was limited for individuals with mild to moderate SARS-CoV-2 infection in the early phase of the COVID-19 pandemic and a substantial proportion of health-care workers with suspected infection were not tested. We aimed to investigate the performance of point-of-care and laboratory serology assays and their utility in late case identification, and to estimate SARS-CoV-2 seroprevalence. Methods We did a prospective multicentre cohort study between April 8 and June 12, 2020, in two phases. Symptomatic health-care workers with mild to moderate symptoms were eligible to participate 14 days after onset of COVID-19 symptoms, as per the Public Health England (PHE) case definition. Health-care workers were recruited to the asymptomatic cohort if they had not developed PHE-defined COVID-19 symptoms since Dec 1, 2019. In phase 1, two point-of-care lateral flow serological assays, the Onsite CTK Biotech COVID-19 split IgG/IgM Rapid Test (CTK Bitotech, Poway, CA, USA) and the Encode SARS-CoV-2 split IgM/IgG One Step Rapid Test Device (Zhuhai Encode Medical Engineering, Zhuhai, China), were evaluated for performance against a laboratory immunoassay (EDI Novel Coronavirus COVID-19 IgG ELISA kit [Epitope Diagnostics, San Diego, CA, USA]) in 300 samples from health-care workers and 100 pre-COVID-19 negative control samples. In phase 2 (n=6440), serosurveillance was done among 1299 (93·4%) of 1391 health-care workers reporting symptoms, and in a subset of asymptomatic health-care workers (405 [8·0%] of 5049). Findings There was variation in test performance between the lateral flow serological assays; however, the Encode assay displayed reasonable IgG sensitivity (127 of 136; 93·4% [95% CI 87·8–96·9]) and specificity (99 of 100; 99·0% [94·6–100·0]) among PCR-proven cases and good agreement (282 of 300; 94·0% [91·3–96·7]) with the laboratory immunoassay. By contrast, the Onsite assay had reduced sensitivity (120 of 136; 88·2% [95% CI 81·6–93·1]) and specificity (94 of 100; 94·0% [87·4–97·8]) and agreement (254 of 300; 84·7% [80·6–88·7]). Five (7%) of 70 PCR-positive cases were negative across all assays. Late changes in lateral flow serological assay bands were recorded in 74 (9·3%) of 800 cassettes (35 [8·8%] of 400 Encode assays; 39 [9·8%] of 400 Onsite assays), but only seven (all Onsite assays) of these changes were concordant with the laboratory immunoassay. In phase 2, seroprevalence among the workforce was estimated to be 10·6% (95% CI 7·6–13·6) in asymptomatic health-care workers and 44·7% (42·0–47·4) in symptomatic health-care workers. Seroprevalence across the entire workforce was estimated at 18·0% (95% CI 17·0–18·9). Interpretation Although a good positive predictive value was observed with both lateral flow serological ...

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.

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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.