Systematic screening on admission for SARS-CoV-2 to detect asymptomatic infections.

The proportion of asymptomatic carriers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains elusive and the potential benefit of systematic screening during the SARS-CoV-2-pandemic is controversial. We investigated the proportion of asymptomatic inpatients who were identified by systematic screening for SARS-CoV-2 upon hospital admission. Our analysis revealed that systematic screening of asymptomatic inpatients detects a low total number of SARS-CoV-2 infections (0.1%), questioning the cost-benefit ratio of this intervention. Even when the population-wide prevalence was low, the proportion of asymptomatic carriers remained stable, supporting the need for universal infection prevention and control strategies to avoid onward transmission by undetected SARS-CoV-2-carriers during the pandemic.

Independent, external validation of clinical prediction rules for the identification of extended-spectrum ß-lactamase-producing Enterobacterales, University Hospital Basel, Switzerland, January 2010 to December 2016

BackgroundAlgorithms for predicting infection with extended-spectrum ß-lactamase-producing Enterobacterales (ESBL-PE) on hospital admission or in patients with bacteraemia have been proposed, aiming to optimise empiric treatment decisions.AimWe sought to confirm external validity and transferability of two published prediction models as well as their integral components.MethodsWe performed a retrospective case-control study at University Hospital Basel, Switzerland. Consecutive patients with ESBL-producing Escherichia coli or Klebsiella pneumoniae isolated from blood samples between 1 January 2010 and 31 December 2016 were included. For each case, three non-ESBL-producing controls matching for date of detection and bacterial species were identified. The main outcome measure was the ability to accurately predict infection with ESBL-PE by measures of discrimination and calibration.ResultsOverall, 376 patients (94 patients, 282 controls) were analysed. Performance measures for prediction of ESBL-PE infection of both prediction models indicate adequate measures of calibration, but poor discrimination (area under receiver-operating curve: 0.627 and 0.651). History of ESBL-PE colonisation or infection was the single most predictive independent risk factor for ESBL-PE infection with high specificity (97%), low sensitivity (34%) and balanced positive and negative predictive values (80% and 82%).ConclusionsApplying published prediction models to institutions these were not derived from, may result in substantial misclassification of patients considered as being at risk, potentially leading to wrong allocation of antibiotic treatment, negatively affecting patient outcomes and overall resistance rates in the long term. Future prediction models need to address differences in local epidemiology by allowing for customisation according to different settings.