Objectives: We investigated the prevalence of anosmia and ageusia in adult patients with a laboratoryconfirmed diagnosis of infection with severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2). Methods: This was a retrospective observational analysis of patients infected with SARS-CoV-2 admitted to hospital or managed in the community and their household contacts across a London population during the period March 1st to April 1st, 2020. Symptomatology and duration were extracted from routinely collected clinical data and follow-up telephone consultations. Descriptive statistics were used. Results: Of 386 patients, 141 (92 community patients, 49 discharged inpatients) were included for analysis; 77/141 (55%) reported anosmia and ageusia, nine reported only ageusia and three only anosmia. The median onset of anosmia in relation to onset of SARS-CoV-2 disease (COVID-19) symptoms (as defined by the Public Health England case definition) was 4 days (interquartile range (IQR) 5). Median duration of anosmia was 8 days (IQR 16). Median duration of COVID-19 symptoms in community patients was 10 days (IQR 8) versus 18 days (IQR 13.5) in admitted patients. As of April 1, 45 patients had ongoing COVID-19 symptoms and/or anosmia; 107/141 (76%) patients had household contacts, and of 185 nontested household contacts 79 (43%) had COVID-19 symptoms with 46/79 (58%) reporting anosmia. Six household contacts had anosmia only. Conclusions: Over half of the positive patients reported anosmia and ageusia, suggesting that these should be added to the case definition and used to guide self-isolation protocols. This adaptation may be integral to case findings in the absence of population-level testing. Until we have successful populationlevel vaccination coverage, these steps remain critical in the current and future waves of this pandemic.

Background The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. Objective We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). Methods We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. Results Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. Conclusions This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.

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.

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.

Background Black, Asian and minority ethnic (BAME) populations are emerging as a vulnerable group in the severe acute respiratory syndrome coronavirus disease (SARS-CoV-2) pandemic. We investigated the relationship between ethnicity and health outcomes in SARS-CoV-2. Methods and findings We conducted a retrospective, observational analysis of SARS-CoV-2 patients across two London teaching hospitals during March 1 –April 30, 2020. Routinely collected clinical data were extracted and analysed for 645 patients who met the study inclusion criteria. Within this hospitalised cohort, the BAME population were younger relative to the white population (61.70 years, 95% CI 59.70–63.73 versus 69.3 years, 95% CI 67.17–71.43, p<0.001). When adjusted for age, sex and comorbidity, ethnicity was not a predictor for ICU admission. The mean age at death was lower in the BAME population compared to the white population (71.44 years, 95% CI 69.90–72.90 versus, 77.40 years, 95% CI 76.1–78.70 respectively, p<0.001). When adjusted for age, sex and comorbidities, Asian patients had higher odds of death (OR 1.99: 95% CI 1.22–3.25, p<0.006). Conclusions BAME patients were more likely to be admitted younger, and to die at a younger age with SARS-CoV-2. Within the BAME cohort, Asian patients were more likely to die but despite this, there was no difference in rates of admission to ICU. The reasons for these disparities are not fully understood and need to be addressed. Investigating ethnicity as a clinical risk factor remains a high public health priority. Studies that consider ethnicity as part of the wider socio-cultural determinant of health are urgently needed.