The TAM-TB Assay—A Promising TB Immune-Diagnostic Test With a Potential for Treatment Monitoring

Tuberculosis (TB) epidemiology is changing in Western and Central Europe due to the rise in immigration and refugees fleeing high-TB-burden areas of war and devastation. The change in local demography and the lack of sensitive and specific TB diagnostic and monitoring tools, especially for cases of childhood TB, leads to either missed cases or over-treatment of this group. Here we present a promising new diagnostic approach, the T cell activation marker (TAM)-TB assay, and its performance in a case of extra-pulmonary TB occurring in a 16 year old refugee from Afghanistan. This assay is based on the characterization of 3 activation markers (CD38, HLA-DR, and Ki67) and one maturation marker (CD27) on M. tuberculosis-specific CD4 T cells. It was performed at time-points T0 (10 days), T1 (1 month), T2 (6 months), and T3 (12 months) post-treatment initiation. All markers were able to detect active tuberculosis (aTB) within this patient at T0 and reverted to a healthy/LTBI phenotype at the end of treatment. Tantalizingly, there was a clear trend toward the healthy/LTBI phenotype for the markers at T1 and T2, indicating a potential role in monitoring anti-TB treatment in the future. This assay may therefore contribute to improved TB diagnostic algorithms and TB treatment monitoring, potentially allowing for individualization of TB treatment duration in the future

The TAM-TB Assay—A Promising TB Immune-Diagnostic Test With a Potential for Treatment Monitoring

Tuberculosis (TB) epidemiology is changing in Western and Central Europe due to the rise in immigration and refugees fleeing high-TB-burden areas of war and devastation. The change in local demography and the lack of sensitive and specific TB diagnostic and monitoring tools, especially for cases of childhood TB, leads to either missed cases or over-treatment of this group. Here we present a promising new diagnostic approach, the T cell activation marker (TAM)-TB assay, and its performance in a case of extra-pulmonary TB occurring in a 16 year old refugee from Afghanistan. This assay is based on the characterization of 3 activation markers (CD38, HLA-DR, and Ki67) and one maturation marker (CD27) on M. tuberculosis-specific CD4 T cells. It was performed at time-points TO (10 days), T1 (1 month), T2 (6 months), and T3 (12 months) post-treatment initiation. All markers were able to detect active tuberculosis (aTB) within this patient at T0 and reverted to a healthy/LTBI phenotype at the end of treatment. Tantalizingly, there was a clear trend toward the healthy/LTBI phenotype for the markers at T1 and T2, indicating a potential role in monitoring anti-TB treatment in the future. This assay may therefore contribute to improved TB diagnostic algorithms and TB treatment monitoring, potentially allowing for individualization of TB treatment duration in the future

Phenotypic Changes on Mycobacterium Tuberculosis-Specific CD4 T Cells as Surrogate Markers for Tuberculosis Treatment Efficacy

Background: The analysis of phenotypic characteristics on Mycobacterium tuberculosis (MTB)-specific T cells is a promising approach for the diagnosis of active tuberculosis (aTB) and for monitoring treatment success. We therefore studied phenotypic changes on MTB-specific CD4 T cells upon anti-tuberculosis treatment initiation in relation to the treatment response as determined by sputum culture.Methods: Peripheral blood mononuclear cells from subjects with latent MTB infection (n = 16) and aTB (n = 39) at baseline, weeks 9, 12, and 26 (end of treatment) were analyzed after intracellular interferon gamma staining and overnight stimulation with tuberculin. Liquid sputum cultures were performed weekly until week 12 and during 4 visits until week 26.Results: T cell activation marker expression on MTB-specific CD4 T cells differed significantly between subjects with aTB and latent MTB infection with no overlap for the frequencies of CD38pos and Ki67pos cells (both p < 0.0001). At 9 weeks after anti-TB treatment initiation the frequencies of activation marker (CD38, HLA-DR, Ki67) positive MTB-specific, but not total CD4 T cells, were significantly reduced (p < 0.0001). Treatment induced phenotypic changes from baseline until week 9 and until week 12 differed substantially between individual aTB patients and correlated with an individual's time to stable sputum culture conversion for expression of CD38 and HLA-DR (both p < 0.05). In contrast, the frequencies of maturation marker CD27 positive MTB-specific CD4 T cells remained largely unchanged until week 26 and significantly differed between subjects with treated TB disease and latent MTB infection (p = 0.0003).Discussion: Phenotypic changes of MTB-specific T cells are potential surrogate markers for tuberculosis treatment efficacy and can help to discriminate between aTB (profile: CD38pos, CD27low), treated TB (CD38neg, CD27low), and latent MTB infection (CD38neg, CD27high)

Shock Index in the early assessment of febrile children at the emergency department : a prospective multicentre study

Funding Information: Funding This work was supported by the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 668303), by the National Institute for Health Research (NIHR) Biomedical Research Centres at Imperial College London, Newcastle Hospitals NHS Foundation Trust and Newcastle University, and by NIHR Academic Clinical Fellowship award (ACL-2018-21-00 to RN). Funding Information: This work was supported by the European Union's Horizon 2020 research and innovation programme (grant agreement no. 668303), by the National Institute for Health Research (NIHR) Biomedical Research Centres at Imperial College London, Newcastle Hospitals NHS Foundation Trust and Newcastle University, and by NIHR Academic Clinical Fellowship award (ACL-2018-21-00 to RN). Publisher Copyright: ©Objective: (1) To derive reference values for the Shock Index (heart rate/systolic blood pressure) based on a large emergency department (ED) population of febrile children and (2) to determine the diagnostic value of the Shock Index for serious illness in febrile children. Design/setting: Observational study in 11 European EDs (2017-2018). Patients: Febrile children with measured blood pressure. Main outcome measures: Serious bacterial infection (SBI), invasive bacterial infection (IBI), immediate life-saving interventions (ILSIs) and intensive care unit (ICU) admission. The association between high Shock Index (>95th centile) and each outcome was determined by logistic regression adjusted for age, sex, referral, comorbidity and temperature. Additionally, we calculated sensitivity, specificity and negative/positive likelihood ratios (LRs). Results: Of 5622 children, 461 (8.2%) had SBI, 46 (0.8%) had IBI, 203 (3.6%) were treated with ILSI and 69 (1.2%) were ICU admitted. High Shock Index was associated with SBI (adjusted OR (aOR) 1.6 (95% CI 1.3 to 1.9)), ILSI (aOR 2.5 (95% CI 2.0 to 2.9)), ICU admission (aOR 2.2 (95% CI 1.4 to 2.9)) but not with IBI (aOR: 1.5 (95% CI 0.6 to 2.4)). For the different outcomes, sensitivity for high Shock Index ranged from 0.10 to 0.15, specificity ranged from 0.95 to 0.95, negative LRs ranged from 0.90 to 0.95 and positive LRs ranged from 1.8 to 2.8. Conclusions: High Shock Index is associated with serious illness in febrile children. However, its rule-out value is insufficient which suggests that the Shock Index is not valuable as a screening tool for all febrile children at the ED.publishersversionPeer reviewe

A Visual Data Mining Tool that Facilitates Reconstruction of Transcription Regulatory Networks

Background: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of ‘‘guilt by association’’, where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. Methodology/Principal Findings: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed metaanalysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet

Guideline adherence in febrile children below 3 months visiting European Emergency Departments: an observational multicenter study

Febrile children below 3 months have a higher risk of serious bacterial infections, which often leads to extensive diagnostics and treatment. There is practice variation in management due to differences in guidelines and their usage and adherence. We aimed to assess whether management in febrile children below 3 months attending European Emergency Departments (EDs) was according to the guidelines for fever. This study is part of the MOFICHE study, which is an observational multicenter study including routine data of febrile children (0-18 years) attending twelve EDs in eight European countries. In febrile children below 3 months (excluding bronchiolitis), we analyzed actual management compared to the guidelines for fever. Ten EDs applied the (adapted) NICE guideline, and two EDs applied local guidelines. Management included diagnostic tests, antibiotic treatment, and admission. We included 913 children with a median age of 1.7 months (IQR 1.0-2.3). Management per ED varied as follows: use of diagnostic tests 14-83%, antibiotic treatment 23-54%, admission 34-86%. Adherence to the guideline was 43% (374/868) for blood cultures, 29% (144/491) for lumbar punctures, 55% (270/492) for antibiotic prescriptions, and 67% (573/859) for admission. Full adherence to these four management components occurred in 15% (132/868, range 0-38%), partial adherence occurred in 56% (484/868, range 35-77%). CONCLUSION: There is large practice variation in management. The guideline adherence was limited, but highest for admission which implies a cautious approach. Future studies should focus on guideline revision including new biomarkers in order to optimize management in young febrile children. WHAT IS KNOWN: • Febrile children below 3 months have a higher risk of serious bacterial infections, which often leads to extensive diagnostics and treatment. • There is practice variation in management of young febrile children due to differences in guidelines and their usage and adherence. WHAT IS NEW: • Full guideline adherence is limited, whereas partial guideline adherence is moderate in febrile children below 3 months across Europe. • Guideline revision including new biomarkers is needed to improve management in young febrile children

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING: European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation

AWaRe-ness of antimicrobial stewardship challenges in pediatric emergency care: results from the PERFORM study assessing consistency and appropriateness of antibiotic prescribing across Europe

Objectives Optimization of antimicrobial stewardship (AMS) is key to tackling antimicrobial resistance (AMR), which is exacerbated by over-prescription of antibiotics in pediatric Emergency Departments (EDs). We described patterns of empiric antibiotic use in European EDs, and characterized appropriateness and consistency of prescribing. Methods Between August 2016 and December 2019 febrile children attending the ED in nine European countries with suspected infection were recruited into the PERFORM (Personalised Risk assessment in Febrile illness to Optimise Real-life Management) study. Empiric systemic antibiotic use was determined in view of assigned final ‘bacterial’ or ‘viral’ phenotype. Antibiotics were classified according to WHO AWaRe. Results Of 2130 febrile episodes (excluding children with non-bacterial/non-viral phenotypes), 1549 (72.7%) were assigned a ‘bacterial’ and 581 (27.3%) a ‘viral’ phenotype. A total of 1318/1549 (85.1%) episodes with a ‘bacterial’ and 269/581 (46.3%) with a ‘viral’ phenotype received empiric systemic antibiotics (first two days of admission). Of those, the majority (87.8% in ‘bacterial’ and 87.0% in ‘viral’ group) received parenteral antibiotics. The top three antibiotics prescribed were third-generation cephalosporins, penicillins and penicillin/beta-lactamase inhibitor combinations. Of those treated with empiric systemic antibiotics in the ‘viral’ group 216/269 (80.3%) received ≥ one Watch antibiotic. Conclusions Differentiating bacterial from viral etiology in febrile illness on initial ED presentation remains challenging, resulting in a substantial over-prescription of antibiotics. A significant proportion of patients with a ‘viral’ phenotype received systemic antibiotics, predominantly classified as WHO Watch. Rapid and accurate point-of-care tests in the ED differentiating between bacterial and viral etiology, could significantly improve AMS

Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature

BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C