Extended cohort for e-health, environment and DNA (exceed) COVID-19 focus

Background: New data collection in established longitudinal population studies provides an opportunity for studying the risk factors and sequelae of the novel coronavirus disease 2019 (COVID-19), plus the indirect impacts of the COVID-19 pandemic on wellbeing. The Extended Cohort for E-health, Environment and DNA (EXCEED) cohort is a population-based cohort (N>11,000), recruited from 2013 in Leicester, Leicestershire and Rutland. EXCEED includes consent for electronic healthcare record (EHR) linkage, spirometry, genomic data, and questionnaire data.

Infection control policies during the COVID-19 pandemic were effective in limiting morbidity and mortality associated with nosocomial viral transmission at a large NHS respiratory department

IntroductionPrevention of nosocomial transmission was a priority for NHS hospital teams during the SARS-COV-2 pandemic. However, infection control policies were developed in the face of uncertainty about duration of infectivity, routes of transmission, and safety of shared admission spaces. We retrospectively reviewed all hospital admissions to the University Hospitals of Leicester (UHL) respiratory department, which managed more than 30% of UHL patients with a diagnosis of COVID-19 between March 2020 and March 2021to determine the proportion of cases with laboratory evidence of healthcare associated infection (HCAI) and mortality within 28 days of PCR conversionMethodsThis was a retrospective cohort study performed using a bespoke database collating COVID-19 throat swab (TS) PCR results for UHL (COVTRACK). Nosocomial transmission was identified by demonstrating PCR conversions during admission and categorized into definite (conversion time > 14 days) or probable (conversion time 8–14 days). In depth records based analysis was undertaken for patients admitted to respiratory medicine (RM) and deceased within 28 days after conversion.ResultsOut of 10485 patients admitted to the Respiratory Department at UHL, 2054 (19.6%) were COVID-19 spell positive, including 57 with probable (41) or definite HCAI (16). 23 patients (7 with definite HCAI) died within 28 days of PCR conversion (0.22%, of total admitted, 1.1% of COVID19 positive), with 21 (91%) deaths in the 2nd wave. Compared with non-COVID admissions not acquiring nosocomial infection, HCAI was significantly associated with older age (mean difference (95%CI) 11.5 (7.5–15.5) years), length of stay (median LOS 18 Vs 1 day) and multiple ward occupancy (median 3 vs 1 ward);all analyses p<0.001.DiscussionOur analysis suggests HCAI with SARS-COV-2 contributed a very small fraction of COVID-19 related morbidity and mortality at our department and in the majority the trajectory of care was not changed. Despite the high numbers of highly infectious cases during the 1st and 2nd wave, we successfully implemented a suite of infection control measures that effectively mitigated risk. High throughput in admission areas, multiple ward moves, and prolonged hospital stay were significant risk factors associated with HCAI.

Diagnosis of COVID-19 by exhaled breath analysis using gas chromatography mass spectrometry

Background: A novel human coronavirus, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), emerged in China in late 2019 and has since claimed more than one million lives. COVID-19 infection is perceived to be seasonally recurrent and a rapid non-invasive biomarker to accurately diagnose patients early-on in their disease course will be necessary to meet the operational demands for COVID-19 control in the coming years.Objective: To evaluate the role of exhaled breath volatile breath biomarkers in identifying patients with suspected or confirmed COVID 19 infection, based on their underlying reverse transcriptase polymerase chain reaction (RT-PCR) status. Methods: We conducted an observational study at Glenfield Hospital, Leicester, United Kingdom, recruiting adult patients with suspected or confirmed COVID19 pneumonia. Breath samples were collected using a standard breath collection bag, modified with appropriate filters to comply with local infection control recommendations and samples were analysed using gas chromatography mass spectrometry (GC-MS).Findings: 81 patients were recruited, of whom 52/81 (64%) have subsequently tested positive for COVID19. A LASSO regression analysis, with the dependent variable as PCR status was run. A set of seven features were extracted that had non-zero regression coefficients in at least 70 out of 100 runs of 10-fold cross validation. Compound identities were confirmed using the Metabolomics Standards Initiative (MSI). These were benzaldehyde, 1-propanol (MSI level 1), 3,6-methylundecane (MSI level 2), camphene and beta-cubebene (MSI level 1 and 2 respectively). Iodobenzene was also extracted, likely of exogenous origin, and an unidentified compound. A logistic regression model was fitted with the dependent variable as PCR status and independent variables as the seven features selected by the LASSO model. Partial Least Squares Discriminant Analysis (PLSDA) and Principal Component Analysis (PCA) were applied to the seven features, with the dependent variable as PCR status. The AUC for the first discriminant function score was 0.836 (95% CI: 0.745-0.928), Sensitivity was 0.68 (95% CI 0.551-0.809), Specificity was 0.857 (95% CI 0.728-0.987), positive predictive value (PPV) was 0.895 (95% CI 0.797-0.992) and negative predictive value (NPV) was 0.6 (95% CI 0.448-0.752). The AUC for the first PCA was 0.799 (95% CI: 0.698-0.900), Sensitivity was 0.7 (95% CI 0.573-0.827), Specificity was 0.786 (95% CI 0.634-0.938), PPV was 0.854 (95% CI 0.745-0.962) and NPV was 0.595 (95% CI 0.436-0.753).Conclusions: breath analysis has promising combined sensitivity and specificity in detecting COVID19, raising the possibility of mass rapid testing, pending external validation of the identified biomarkers.