SARS-CoV-2 antigen tests for screening of healthcare workers; experience with over 48,000 combined antigen tests and RT-PCR tests

BACKGROUND

To prevent spread to patients and co-workers, health care workers (HCWs) infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) should quickly be identified. Although real time polymerase chain reaction (RT-PCR) is the gold standard, this test takes several hours, during which a HCW is unable to work. Antigen (Ag) tests may be an efficacious means of screening HCWs since they are easy to perform and provide fast results.

METHODS

In this study, 48,010 paired results of Ag-testing and RT-PCR, performed on HCWs between January 2021 and April 2022, were evaluated to determine the diagnostic accuracy of SARS-CoV-2 Ag-tests in diagnosing potentially infectious individuals. This analysis was performed with cycling threshold values (Ct-values) ≤30 and ≤25 as cut-offs.

RESULTS

Respectively 3.1% (n = 1507) and 0.3% (n = 140) of Ag-tests were positive or indeterminate, and thus indicative for SARS-CoV-2 infection. In total, 2479 (5.2%) RT-PCRs were positive, of which 1529 (61.7%) had a Ct-value ≤25 and 402 (16.2%) a Ct-value between 26 and 30. At Ct-value ≤30 as a cut-off, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Ag-tests were 79.0%, 99.8%, 93.8% and 99.1%, respectively. At Ct-value ≤25, sensitivity further improved to 92.0%, by which the NPV increased to 99.7%.

CONCLUSIONS

To prevent transmission from HCWs to patients and co-workers, while maintaining workforce capacity, Ag-tests are a valuable addition to RT-PCR tests, as they have a quick turnaround time and excellent sensitivity for identifying individuals with high potential for transmission.

Spatiotemporal prediction of vancomycin-resistant Enterococcus colonisation

BACKGROUND

Vancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected. This study investigates how patient movements between hospital wards and their antibiotic use can explain the colonisation of patients with VRE.

METHODS

Intrahospital patient movements, antibiotic use and PCR screening data were used from a hospital in the Netherlands. The PageRank algorithm was used to calculate two daily centrality measures based on the spatiotemporal graph to summarise the flow of patients and antibiotics at the ward level. A decision tree model was used to determine a simple set of rules to estimate the daily probability of patient VRE colonisation for each hospital ward. The model performance was improved using a random forest model and compared using 30% test sample.

RESULTS

Centrality covariates summarising the flow of patients and their antibiotic use between hospital wards can be used to predict the daily colonisation of VRE at the hospital ward level. The decision tree model produced a simple set of rules that can be used to determine the daily probability of patient VRE colonisation for each hospital ward. An acceptable area under the ROC curve (AUC) of 0.755 was achieved using the decision tree model and an excellent AUC of 0.883 by the random forest model on the test set. These results confirms that the random forest model performs better than a single decision tree for all levels of model sensitivity and specificity on data not used to estimate the models.

CONCLUSION

This study showed how the movements of patients inside hospitals and their use of antibiotics could predict the colonisation of patients with VRE at the ward level. Two daily centrality measures were proposed to summarise the flow of patients and antibiotics at the ward level. An early warning system for VRE can be developed to test and further develop infection prevention plans and outbreak strategies using these results.

A spatiotemporal simulation study on the transmission of harmful microorganisms through connected healthcare workers in a hospital ward setting

Background

Hand transmission of harmful microorganisms may lead to infections and poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against these transmissions is the adherence to spatiotemporal hand hygiene policies, but adherence rates are relatively low and vary over space and time. The spatiotemporal effects on hand transmission and spread of these microorganisms for varying hand hygiene compliance levels are unknown. This study aims to (1) identify a healthcare worker occupancy group of potential super-spreaders and (2) quantify spatiotemporal effects on the hand transmission and spread of harmful microorganisms for varying levels of hand hygiene compliance caused by this group.

Methods

Spatiotemporal data were collected in a hospital ward of an academic hospital using radio frequency identification technology for 7 days. A potential super-spreader healthcare worker occupation group was identified using the frequency identification sensors’ contact data. The effects of five probability distributions of hand hygiene compliance and three harmful microorganism transmission rates were simulated using a dynamic agent-based simulation model. The effects of initial simulation assumptions on the simulation results were quantified using five risk outcomes.

Results

Nurses, doctors and patients are together responsible for 81.13% of all contacts. Nurses made up 70.68% of all contacts, which is more than five times that of doctors (10.44%). This identifies nurses as the potential super-spreader healthcare worker occupation group. For initial simulation conditions of extreme lack of hand hygiene compliance (5%) and high transmission rates (5% per contact moment), a colonised nurse can transfer microbes to three of the 17 healthcare worker or patients encountered during the 98.4 min of visiting 23 rooms while colonised. The harmful microorganism transmission potential for nurses is higher during weeknights (5 pm – 7 am) and weekends as compared to weekdays (7 am – 5 pm).

Conclusion

Spatiotemporal behaviour and social mixing patterns of healthcare can change the expected number of hand transmissions and spread of harmful microorganisms by super-spreaders in a closed healthcare setting. These insights can be used to evaluate spatiotemporal safety behaviours and develop infection prevention and control strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05954-7.

Elucidating vancomycin-resistant Enterococcus faecium outbreaks: the role of clonal spread and movement of mobile genetic elements

Background

Vancomycin-resistant Enterococcus faecium (VREfm) has emerged as a nosocomial pathogen worldwide. The dissemination of VREfm is due to both clonal spread and spread of mobile genetic elements (MGEs) such as transposons.

Objectives

We aimed to combine vanB-carrying transposon data with core-genome MLST (cgMLST) typing and epidemiological data to understand the pathways of transmission in nosocomial outbreaks.

Methods

Retrospectively, 36 VREfm isolates obtained from 34 patients from seven VREfm outbreak investigations in 2014 were analysed. Isolates were sequenced on a MiSeq and a MinION instrument. De novo assembly was performed in CLC Genomics Workbench and the hybrid assemblies were obtained through Unicycler v0.4.1. Ridom SeqSphere+ was used to extract MLST and cgMLST data. Detailed analysis of each transposon and their integration points was performed using the Artemis Comparison Tool (ACT) and multiple blast analyses.

Results

Four different vanB transposons were found among the isolates. cgMLST divided ST80 isolates into three cluster types (CTs); CT16, CT104 and CT106. ST117 isolates were divided into CT24, CT103 and CT105. Within VREfm isolates belonging to CT103, two different vanB transposons were found. In contrast, VREfm isolates belonging to CT104 and CT106 harboured an identical vanB transposon.

Conclusions

cgMLST provides a high discriminatory power for the epidemiological analysis of VREfm. However, additional transposon analysis is needed to detect horizontal gene transfer. Combining these two methods allows investigation of both clonal spread as well as the spread of MGEs. This leads to new insights and thereby better understanding of the complex transmission routes in VREfm outbreaks.