A host-protein based assay to differentiate between bacterial and viral infections in preschool children (OPPORTUNITY): a double-blind, multicentre, validation study

Using blood TNF-related apoptosis-inducing ligand levels to discriminate between viral and bacterial infections: A prospective observational study

OBJECTIVE

The aim of the investigation was to evaluate variations in blood TNF-related apoptosis-inducing ligand (TRAIL) levels between patients with viral and bacterial infections and the diagnostic performance of TRAIL for identifying viral and bacterial infections.

METHODS

The investigation included 169 adult (>18 years) patients presenting with medical signs of acute infections (inclusion criteria included a body temperature over 37.5 °C, an onset of symptoms no more than 12 days). Reference standard was based on a rigorous expert panel and the majority of the panel determined the infectious etiology. Finally, 104 patients with 78 bacterial and 26 viral reference standard outcomes were enrolled in this investigation (24 were eliminated depending on the exclusion criteria; 41 had indeterminate reference standard diagnosis). ELISA was employed to measure TRAIL levels in the group of 78 subjects with bacterial infections and 26 individuals with viral infections, and the diagnostic performance of TRAIL was identified by receiver operating characteristic (ROC) analysis.

RESULTS

The TRAIL level in individuals with bacterial infections was significantly lower than that in subjects with viral infections (16.59 (2.61-32.6) pg/mL vs. 97.39 (36.18-127.74) pg/mL, P < 0.05). The area under the ROC curve (AUC) of TRAIL was 0.86 (95 %CI:0.79 to 0.94) for identifying bacterial and viral infections. Combining TRAIL with C-reactive protein (CRP), the AUC was 0.94 (95 %CI:0.89 to 1.00).

CONCLUSIONS

TRAIL is diagnostic for discriminating between viral and bacterial infections. Combining TRAIL with CRP increases the AUC.

Differentiating viral and bacterial infections: A machine learning model based on routine blood test values

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.

Natural variability of TRAIL, IP-10, and CRP in healthy adults - The "HERACLES" study

A novel host-protein score (called MMBV) helps to distinguish bacterial from viral infection by combining the blood concentrations of three biomarkers: tumour necrosis factor related apoptosis inducing ligand (TRAIL), interferon gamma induced protein 10 (IP-10), and C-reactive protein (CRP). These host biomarkers are differentially expressed in response to bacterial versus viral acute infection. We conducted a prospective study, with a time series design, in healthy adult volunteers in the Netherlands. The aim was to determine the variability of TRAIL, IP-10, and CRP and the MMBV score in healthy adults across time. Up to six blood samples were taken from each healthy volunteer over a period of up to four weeks. In 77 healthy participants without recent or current symptoms, MMBV scores (maximal) were bacterial in 1.3 % and viral (or other non-infectious etiology) in 93.5 % of participants. There was little variation in the mean concentrations of TRAIL (74.5 pg/ml), IP-10 (113.6 pg/ml), and CRP (1.90 mg/L) as well as the MMBV score. The variability of biomarker measurement was comparable to the precision of the measurement platform for TRAIL, IP-10, and CRP. Our findings establish the mean values of these biomarkers and MMBV in healthy individuals and indicate little variability between and within individuals over time, supporting the potential utility of this novel diagnostic to detect infection-induced changes.

Clinical usefulness of a host signature based on TRAIL, IP10, and CRP (MeMed BV) as infection biomarkers in intensive care units' patients. A retrospective observational study

INTRODUCTION

Infection complications are common in intensive care unit patients, and early detection remains a diagnostic challenge. Procalcitonin (PCT) and C-reactive protein (CRP) are commonly used biomarkers. A novel diagnostic approach focuses on the host immune response. One of the approaches, the MMBV index, is based on measuring in a blood sample three parameters: (i) tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), (ii) interferon-γ-induced protein-10 (IP10), and (iii) CRP. This study aimed to evaluate the usefulness of MMBV as an infection biomarker in an ICU cohort.

PATIENTS AND METHODS

Forty-six patients treated in the University Clinical Center in Gdansk ICU were enrolled in the study, and their clinical data were retrospectively analyzed. In total, 91 MMBV results were analyzed.

RESULTS

Most of the patients had high MMBV values, suggesting bacterial etiology. A weak correlation between PCT and MMBV was observed, and no correlation between parameter changes was noted. There was a correlation between CRP/MMBV and between changes in CRP / changes in MMBV.

CONCLUSION

It seems that MMBV is not valuable for ICU patients neither in diagnosing nor monitoring infection. Higher MMBV values may predict unfavorable treatment outcomes.

Distinguishing community-acquired bacterial and viral meningitis: Microbes and biomarkers

Diagnostic tools to differentiate between community-acquired bacterial and viral meningitis are essential to target the potentially lifesaving antibiotic treatment to those at greatest risk and concurrently spare patients with viral meningitis from the disadvantages of antibiotics. In addition, excluding bacterial meningitis and thus decreasing antibiotic consumption would be important to help reduce antimicrobial resistance and healthcare expenses. The available diagnostic laboratory tests for differentiating bacterial and viral meningitis can be divided microbiological pathogen-focussed methods and biomarkers of the host response. Bacterial culture-independent microbiological methods, such as highly multiplexed nucleic acid amplification tests, are rapidly making their way into the clinical practice. At the same time, more conventional host protein biomarkers, such as procalcitonin and C-reactive protein, are supplemented by newer proteomic and transcriptomic signatures. This review aims to summarise the current state and the recent advances in diagnostic methods to differentiate bacterial from viral meningitis.

Validation of a whole blood machine learning strategy for distinguishing between bacterial and viral infection in a pediatric hospital setting

Similar symptoms between viral and bacterial diseases often make diagnosis difficult. This study assessed the clinical performance of the newly cleared whole-blood Bacterial versus Viral Score assay in our pediatric cohort to the previously validated serum assay and emergency department physician diagnosis. This assay shows excellent agreement (R = 0.997) with the serum assay and has great diagnostic accuracy when compared to physician diagnosis.

Advances in sepsis biomarkers

Sepsis, a dysregulated host immune response to an infectious agent, significantly increases morbidity and mortality for hospitalized patients worldwide. This chapter reviews (1) the basic principles of infectious diseases, pathophysiology and current definition of sepsis, (2) established sepsis biomarkers such lactate, procalcitonin and C-reactive protein, (3) novel, newly regulatory-cleared/approved biomarkers, such as assays that evaluate white blood cell properties and immune response molecules, and (4) emerging biomarkers and biomarker panels to highlight future directions and opportunities in the diagnosis and management of sepsis.

A decade of clinical microbiology: top 10 advances in 10 years: what every infection preventionist and antimicrobial steward should know

The past 10 years have brought paradigm-shifting changes to clinical microbiology. This paper explores the top 10 transformative innovations across the diagnostic spectrum, including not only state of the art technologies but also preanalytic and post-analytic advances. Clinical decision support tools have reshaped testing practices, curbing unnecessary tests. Innovations like broad-range polymerase chain reaction and metagenomic sequencing, whole genome sequencing, multiplex molecular panels, rapid phenotypic susceptibility testing, and matrix-assisted laser desorption ionization time-of-flight mass spectrometry have all expanded our diagnostic armamentarium. Rapid home-based testing has made diagnostic testing more accessible than ever. Enhancements to clinician-laboratory interfaces allow for automated stewardship interventions and education. Laboratory restructuring and consolidation efforts are reshaping the field of microbiology, presenting both opportunities and challenges for the future of clinical microbiology laboratories. Here, we review key innovations of the last decade.

Biomarkers for Serious Bacterial Infections in Febrile Children

Febrile infections in children are a common cause of presentation to the emergency department (ED). While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; nowadays, a better understanding of immunochemical pathways has led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models, combining them for serious bacterial infection (SBI) diagnosis in febrile children. Literature analysis shows that inflammatory response is a complex mechanism in which many biochemical and immunological factors contribute to the host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined, and for this reason, it is reasonable to take them both into consideration in the evaluation of febrile children. The potential of machine learning tools, which represent a real novelty in medical practice, in conjunction with routine clinical and biological information, may improve the accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pediatric precision medicine still uncertain. More data from further research are needed to improve clinical practice and decision making using these new technologies.

Laboratory diagnosis of CNS infections in children due to emerging and re-emerging neurotropic viruses

Recent decades have witnessed the emergence and re-emergence of numerous medically important viruses that cause central nervous system (CNS) infections in children, e.g., Zika, West Nile, and enterovirus/parechovirus. Children with immature immune defenses and blood-brain barrier are more vulnerable to viral CNS infections and meningitis than adults. Viral invasion into the CNS causes meningitis, encephalitis, brain imaging abnormalities, and long-term neurodevelopmental sequelae. Rapid and accurate detection of neurotropic viral infections is essential for diagnosing CNS diseases and setting up an appropriate patient management plan. The addition of new molecular assays and next-generation sequencing has broadened diagnostic capabilities for identifying infectious meningitis/encephalitis. However, the expansion of test menu has led to new challenges in selecting appropriate tests and making accurate interpretation of test results. There are unmet gaps in development of rapid, sensitive and specific molecular assays for a growing list of emerging and re-emerging neurotropic viruses. Herein we will discuss the advances and challenges in the laboratory diagnosis of viral CNS infections in children. This review not only sheds light on selection and interpretation of a suitable diagnostic test for emerging/re-emerging neurotropic viruses, but also calls for more research on development and clinical utility study of novel molecular assays. IMPACT: Children with immature immune defenses and blood-brain barrier, especially neonates and infants, are more vulnerable to viral central nervous system infections and meningitis than adults. The addition of new molecular assays and next-generation sequencing has broadened diagnostic capabilities for identifying infectious meningitis and encephalitis. There are unmet gaps in the development of rapid, sensitive and specific molecular assays for a growing list of emerging and re-emerging neurotropic viruses.

Point-of-care biomarker assay for rapid multiplexed detection of CRP and IP-10

Rapid and accurate measurements of immune protein markers are essential for diagnosis and treatment in all clinical settings. The recent pandemic has revealed a stark need for developing new tools and assays that could be rapidly used in diverse settings and provide useful information to clinicians. Here, we describe the development and test application of a novel one-step CRP/IP-10 duplex assay for the LightDeck platform capable of delivering reproducible and accurate measurements in under eight minutes. We used the optimized assay to measure CRP and IP-10 levels in human blood and serum samples from healthy, SARS-CoV-2 (COVID-19) positive, and influenza-like illness (ILI) presenting patients. Our results agreed with previously published analyte levels and enabled us to make statistically significant comparisons relevant to multiple clinical parameters. Our duplex assay is a simple and powerful tool for aiding prognostic decision-making in diverse settings.

Diagnostic Accuracy of a Real-Time Host-Protein Test for Infection

OBJECTIVE

Determining infection etiology can be difficult because viral and bacterial diseases often manifest similarly. A host protein test that computationally integrates the circulating levels of TNF-related apoptosis-induced ligand, interferon γ-induced protein-10, and C-reactive protein to differentiate between bacterial and viral infection (called MMBV) demonstrated high performance in multiple prospective clinical validation studies. Here, MMBV's diagnostic accuracy is evaluated in febrile children for whom physicians were uncertain about etiology when applied at the physician's discretion.

METHODS

Patients aged 3 months to 18 years were retrospectively recruited (NCT03075111; SPIRIT study; 2014-2017). Emergency department physician's etiological suspicion and certainty level were recorded in a questionnaire at blood-draw. MMBV results are based on predefined score thresholds: viral/non-bacterial etiology (0 ≤ score <35), equivocal (35 ≤ score ≤65), and bacterial or coinfection (65 < score ≤100). Reference standard etiology (bacterial/viral/indeterminate) was adjudicated by 3 independent experts based on all available patient data. Experts were blinded to MMBV. MMBV and physician's etiological suspicion were assessed against the reference standard.

RESULTS

Of 3003 potentially eligible patients, the physicians were uncertain about infection etiology for 736 of the cases assigned a reference standard (128 bacterial, 608 viral). MMBV performed with sensitivity 89.7% (96/107; 95% confidence interval 82.4-94.3) and specificity 92.6% (498/538; 95% confidence interval 90.0-94.5), significantly outperforming physician's etiological suspicion (sensitivity 49/74 = 66.2%, specificity 265/368 = 72.0%; P < .0001). MMBV equivocal rate was 12.4% (91/736).

CONCLUSIONS

MMBV was more accurate in determining etiology compared with physician's suspicion and had high sensitivity and specificity according to the reference standard.

Evaluation of the diagnostic performance of EpiTuub® Fecal Rotavirus Antigen Rapid Test Kit in Amhara National Regional State, Ethiopia: A multi-center cross-sectional study

Rotavirus is the leading cause of morbidity and mortality due to acute gastroenteritis among children under five years globally. Early diagnosis of rotavirus infection minimizes its spread and helps to determine the appropriate management of diarrhea. The aim of this study was to evaluate the performance of EpiTuub® Fecal Rotavirus Antigen Rapid Test Kit for the diagnosis of rotavirus infection among diarrheic children under five years in Ethiopian healthcare settings. A total of 537 children with diarrhea were enrolled from three referral hospitals in Amhara National Regional State, Ethiopia. The samples were tested using one-step RT-PCR and EpiTuub® Fecal Rotavirus Antigen Rapid Test Kit (KTR-917, Epitope Diagnostics, San Diego USA) in parallel. Diagnostic performance of the rapid test kit was evaluated using the one-step RT-PCR as a gold standard. The sensitivity, specificity, and predictive values of the rapid test kit were determined. Moreover, the agreement of the rapid test kit with one step RT-PCR was determined by kappa statistics and receiver operators' curve (ROC) analysis was done to assess the overall diagnostic accuracy of the rapid test kit. Fecal Rotavirus Antigen Rapid Test Kit has shown a sensitivity of 75.5% and specificity of 98.2%. The kit was also found to have 89.9% and 95.0% positive and negative predictive values, respectively. The Fecal Rotavirus Antigen Rapid Test Kit has shown a substantial agreement (78.7%, p = 0.0001) with one-step RT-PCR. The overall accuracy of the Fecal Rotavirus Antigen Rapid Test Kit was excellent with the area under the ROC curve of 86.9% (95% CI = 81.6, 92.1%) (p = .0001). Thus, Fecal Rotavirus Antigen Rapid Test is a sensitive, specific, user-friendly, rapid, and equipment-free option to be used at points of care in Ethiopian health care settings where resource is limited precluding the use of one step RT-PCR. Furthermore, the kit could be used in the evaluation and monitoring of rotavirus vaccine effectiveness in the aforementioned settings.

Host biomarkers and combinatorial scores for the detection of serious and invasive bacterial infection in pediatric patients with fever without source

BACKGROUND

Improved tools are required to detect bacterial infection in children with fever without source (FWS), especially when younger than 3 years old. The aim of the present study was to investigate the diagnostic accuracy of a host signature combining for the first time two viral-induced biomarkers, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and interferon γ-induced protein-10 (IP-10), with a bacterial-induced one, C-reactive protein (CRP), to reliably predict bacterial infection in children with fever without source (FWS) and to compare its performance to routine individual biomarkers (CRP, procalcitonin (PCT), white blood cell and absolute neutrophil counts, TRAIL, and IP-10) and to the Labscore.

METHODS

This was a prospective diagnostic accuracy study conducted in a single tertiary center in children aged less than 3 years old presenting with FWS. Reference standard etiology (bacterial or viral) was assigned by a panel of three independent experts. Diagnostic accuracy (AUC, sensitivity, specificity) of host individual biomarkers and combinatorial scores was evaluated in comparison to reference standard outcomes (expert panel adjudication and microbiological diagnosis).

RESULTS

241 patients were included. 68 of them (28%) were diagnosed with a bacterial infection and 5 (2%) with invasive bacterial infection (IBI). Labscore, ImmunoXpert, and CRP attained the highest AUC values for the detection of bacterial infection, respectively 0.854 (0.804-0.905), 0.827 (0.764-0.890), and 0.807 (0.744-0.869). Labscore and ImmunoXpert outperformed the other single biomarkers with higher sensitivity and/or specificity and showed comparable performance to one another although slightly reduced sensitivity in children < 90 days of age.

CONCLUSION

Labscore and ImmunoXpert demonstrate high diagnostic accuracy for safely discriminating bacterial infection in children with FWS aged under and over 90 days, supporting their adoption in the assessment of febrile patients.

Host test based on tumor necrosis factor-related apoptosis-inducing ligand, interferon gamma-induced protein-10 and C-reactive protein for differentiating bacterial and viral respiratory tract infections in adults: diagnostic accuracy study

OBJECTIVES

To assess the performance of a test (called BV), integrating the blood levels of three immune proteins into a score, to differentiate bacterial from viral infection among adults with suspected lower respiratory tract infection (LRTI).

METHODS

Prospective diagnostic accuracy study, enrolling febrile adults >18 years with LRTI signs or symptoms for less than 7 days presenting to several hospitals' emergency departments in Israel. The main exclusion criterion was immunodeficiency. Reference standard diagnosis (bacterial/viral/indeterminate) was based on three experts independently reviewing comprehensive patient data including follow-up data. BV generated three results: viral infection or other nonbacterial condition (0 ≤ score < 35), equivocal (35 ≤ score ≤ 65) and bacterial infection including co-infection (65 < score ≤ 100). BV performance was assessed against the reference standard with indeterminate reference standard and equivocal BV cases removed.

RESULTS

Of 490 enrolled patients, 415 met eligibility criteria (median age 56 years, interquartile range 35). The reference standard classified 104 patients as bacterial, 210 as viral and 101 as indeterminate. BV was equivocal in 9.6% (30/314). Excluding indeterminate reference standard diagnoses and equivocal BV results, BV's sensitivity for bacterial infection was 98.1% (101/103; 95% confidence interval 95.4-100), specificity 88.4% (160/181; 83.7-93.1) and negative predictive value 98.8% (160/162; 97.1-100).

DISCUSSION

BV exhibited high diagnostic performance for febrile adults with suspected LRTI among patients with reference standard diagnoses of bacterial or viral LRTI.

An immune-protein score combining TRAIL, IP-10 and CRP for predicting severe COVID-19 disease

COVID-19 patients are oftentimes over- or under-treated due to a deficit in predictive management tools. This study reports derivation of an algorithm that integrates the host levels of TRAIL, IP-10, and CRP into a single numeric score that is an early indicator of severe outcome for COVID-19 patients and can identify patients at-risk to deteriorate. 394 COVID-19 patients were eligible; 29% meeting a severe outcome (intensive care unit admission/non-invasive or invasive ventilation/death). The score's area under the receiver operating characteristic curve (AUC) was 0.86, superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The score differentiated severe patients who further deteriorated from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001). The score accurately predicted COVID-19 patients at-risk for severe outcome, and therefore has potential to facilitate timely care escalation and de-escalation and appropriate resource allocation.

A Novel Combination of Host Protein Biomarkers to Distinguish Bacterial From Viral Infections in Febrile Children in Emergency Care

BACKGROUND

Distinguishing bacterial and viral infections based on clinical symptoms in febrile children attending the emergency department (ED) is challenging. The aim of this study is to determine a novel combination of host protein biomarkers and to assess its performance in distinguishing between bacterial and viral infection in febrile children attending EDs.

METHODS

A literature search was performed to identify blood protein biomarkers able to distinguish bacterial and viral infections (May 2015-May 2019). We selected 7 protein biomarkers: Procalcitonin, TNF-related apoptosis-inducing ligand (TRAIL), interleukin (IL)-4, IL-6, Interferon gamma-induced protein-10 (CXCL-10), interferon-gamma and lipocalin 2 (LCN2). These were measured in blood plasma using a bead-based immunoassay in children with a confirmed bacterial or viral infection attending EDs in the Netherlands. We used generalized linear modeling to classify bacterial and viral infections and applied a previously developed feature selection algorithm to select the optimal combination of proteins. We performed a subgroup analysis of this protein signature in patients with C-reactive protein <60 mg/L, representing a clinically challenging diagnostic group.

RESULTS

In total 102 children were included (N = 67 bacterial; N = 35 viral). Individual performance of the 7 biomarkers in classifying bacterial versus viral infections ranged from 60.8%-74.5% area under the receiver operator curve (AUC). TRAIL, LCN2 and IL-6 were identified as the best 3-protein signature with an AUC of 86% (95% CI: 71.3%-100%). In 57 patients with C-reactive protein levels <60 mg/L, the 3-protein signature had an AUC of 85.1% (95% CI: 75.3%-94.9%).

CONCLUSION

We demonstrate a promising novel combination of 3 host protein biomarkers; TRAIL, LCN2 and IL-6, which performs well in classifying bacterial and viral infections in febrile children in emergency care.

Validation of a Multi-Analyte Immunoassay for Distinguishing Bacterial vs. Viral Infections in a Pediatric Cohort

BACKGROUND

Clinical presentation of viral and bacterial infections or co-infections overlaps significantly. Pathogen identification is the gold standard for appropriate treatment. Recently, FDA cleared a multivariate index test called MeMed-BV that distinguishes viral and bacterial infections based on the differential expression of 3 host proteins. Here, we sought to validate MeMed-BV immunoassay on MedKey analyzer in our pediatric hospital following guidelines from the Clinical and Laboratory Standards Institute.

METHODS

The analytical performance of the MeMed-BV test was evaluated with precision (intra- and inter-assay), method comparison and interference studies. The clinical performance (diagnostic sensitivity and specificity) of the MeMed-BV test was assessed by conducting a retrospective cohort study (n=60) using plasma samples from pediatric patients with acute febrile illness who visited the emergency department of our hospital.

RESULTS

MeMed-BV showed acceptable intra- and inter-assay precision with a range of <3 score units in both the high-score bacterial as well as the low-score viral controls. Diagnostic accuracy studies revealed a sensitivity of 94% and specificity of 88% for identifying bacterial infections or co-infections. Our MeMed-BV results showed an excellent agreement (R=0.998) with manufacturer's laboratory data and compared well with ELISA studies. Gross hemolysis and icterus did not affect the assay, but gross lipemia showed a considerable bias in samples with moderate likelihood of viral infection. Importantly, the MeMed-BV test performed better than routinely measured infection-related biomarkers like white blood cell counts, procalcitonin and C-reactive protein in classifying bacterial infections.

CONCLUSION

MeMed-BV immunoassay demonstrated acceptable analytical performance and is reliable for distinguishing viral and bacterial infections or co-infections in pediatric patients. Future studies are warranted to examine the clinical utility, especially with respect to reducing the need for blood cultures and time to treatment for the patient.

Host Response Biomarkers for Sepsis in the Emergency Room

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Cost-Impact Analysis of a Novel Diagnostic Test to Assess Community-Acquired Pneumonia Etiology in the Emergency Department Setting: A Multi-Country European Study

BACKGROUND

We aimed to estimate the economic and clinical impacts of a novel diagnostic test called LIAISON^® MeMed BV^® (LMMBV), which can differentiate bacterial from viral infections, in patients with community-acquired pneumonia (CAP) in emergency departments.

METHODS

A cost-impact simulation model was developed to investigate the financial consequences of the introduction of LMMBV into the standard of care (SOC) diagnostic process in Italy, Germany, and Spain. Clinical outcomes were expressed as antibiotic patients and days saved, reduced hospital admissions, and shortened hospital length of stay (LOS). Cost savings were evaluated from the perspectives of third-party payers and hospitals. A deterministic sensitivity analysis (DSA) was carried out.

RESULTS

LMMBV was associated with a reduction in antibiotic prescriptions, treatment duration, and LOS. Furthermore, the adoption of LMMBV would allow savings per patient up to EUR 364 and EUR 328 for hospitals and EUR 91 and EUR 59 for payers in Italy and Germany, respectively. In Spain, average savings per patient could reach up to EUR 165 for both payers and hospitals. Savings were most sensitive to test accuracy, with DSA confirming the robustness of the results.

CONCLUSIONS

Combining LMMBV with the current SOC diagnostic process is expected to provide clinical and economic benefits in Italy, Germany, and Spain.

Bacterial vs viral etiology of fever: A prospective study of a host score for supporting etiologic accuracy of emergency department physicians

BACKGROUND

A host-protein score (BV score) that combines the circulating levels of TNF-related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein 10 (IP-10) and C-reactive protein (CRP) was developed for distinguishing bacterial from viral infection. This study assessed the potential of the BV score to impact decision making and antibiotic stewardship at the emergency department (ED), by comparing BV score's performance to physician's etiological suspicion at patient presentation.

METHODS

Rosetta study participants, aged 3 months to 18 years with febrile respiratory tract infection or fever without source, were prospectively recruited in a tertiary care pediatric ED. 465 patients were recruited, 298 met eligibility criteria and 287 were enrolled. ED physician's etiological suspicion was recorded in a questionnaire. BV score was measured retrospectively with results interpreted as viral, bacterial or equivocal and compared to reference standard etiology, which was adjudicated by three independent experts based on all available data. Experts were blinded to BV scores.

RESULTS

Median age was 1.3 years (interquartile range 1.7), 39.7% females. 196 cases were reference standard viral and 18 cases were reference standard bacterial. BV score attained sensitivity of 88.9% (95% confidence interval: 74.4-100), specificity 92.1% (88.1-96.0), positive predictive value 53.3% (35.5-71.2) and negative predictive value 98.8% (97.1-100). Positive likelihood ratio was 11.18 (6.59-18.97) and negative likelihood ratio was 0.12 (0.03-0.45). The rate of BV equivocal scores was 9.4%. Comparing physician's suspicion to BV score and to the reference standard, and assuming full adoption, BV score could potentially correct the physician's diagnosis and reduce error ~2-fold, from 15.9% to 8.2%.

CONCLUSIONS

BV score has potential to aid the diagnostic process. Future studies are warranted to assess the impact of real-time BV results on ED practice.

Association of viral load with TRAIL, IP-10, CRP biomarker signature and disease severity in children with respiratory tract infection or fever without source: A prospective, multicentre cohort study

BACKGROUND

To investigate the association of viral load (VL) with (i) tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein-10, C-reactive protein, and a combinatorial score (BV score), and (ii) clinical severity.

STUDY DESIGN

In this prospective, multicentre cohort substudy, children with respiratory tract infection or fever without source were enrolled. VL for influenza virus, rhinovirus, respiratory syncytial virus, and adenovirus was measured from nasopharyngeal swabs. The reference standard diagnosis was established based on expert panel adjudication.

RESULTS

Of 1140 recruited patients, 333 had a virus monodetection. VL for the aggregated data set correlated with TRAIL and IP-10 levels, with the length of oxygen therapy, and inversely with the BV score. At a single viral level, only the influenza VL yielded a correlation with TRAIL, IP-10 levels, and the BV score. Children with a viral reference standard diagnosis had significantly higher VL than those with bacterial infection (p = 0.0005). Low TRAIL (incidence rate ratio [IRR] 0.6, 95% confidence interval [CI] 0.39-0.91) and young age (IRR 0.62, 95% CI 0.49-0.79) were associated with a longer hospital stay, while young age (IRR 0.33, 95% CI 0.18-0.61), low TRAIL (IRR 0.25, 95% CI 0.08-0.76), and high VL (IRR 1.16, 95% CI 1.00-1.33) were predictive of longer oxygen therapy.

CONCLUSION

These findings indicate that VL correlates with biomarkers and may serve as a complementary tool pertaining to disease severity.

Performance Evaluation of Host Biomarker Combinations for the Diagnosis of Serious Bacterial Infection in Young Febrile Children: A Double-Blind, Multicentre, Observational Study

The diagnosis of serious bacterial infection (SBI) in young febrile children remains challenging. This prospective, multicentre, observational study aimed to identify new protein marker combinations that can differentiate a bacterial infection from a viral infection in 983 children, aged 7 days–36 months, presenting with a suspected SBI at three French paediatric emergency departments. The blood levels of seven protein markers (CRP, PCT, IL-6, NGAL, MxA, TRAIL, IP-10) were measured at enrolment. The patients received the standard of care, blinded to the biomarker results. An independent adjudication committee assigned a bacterial vs. viral infection diagnosis based on clinical data, blinded to the biomarker results. Computational modelling was applied to the blood levels of the biomarkers using independent training and validation cohorts. Model performances (area under the curve (AUC), positive and negative likelihood ratios (LR+ and LR–)) were calculated and compared to those of the routine biomarkers CRP and PCT. The targeted performance for added value over CRP or PCT was LR+ ≥ 5.67 and LR− ≤ 0.5. Out of 652 analysed patients, several marker combinations outperformed CRP and PCT, although none achieved the targeted performance criteria in the 7 days–36 months population. The models seemed to perform better in younger (7–91 day-old) patients, with the CRP/MxA/TRAIL combination performing best (AUC 0.895, LR+ 10.46, LR− 0.16). Although computational modelling using combinations of bacterial- and viral-induced host-protein markers is promising, further optimisation is necessary to improve SBI diagnosis in young febrile children.

Forty Years of Molecular Diagnostics for Infectious Diseases

Nearly 40 years have elapsed since the invention of the PCR, with its extremely sensitive and specific ability to detect nucleic acids via in vitro enzyme-mediated amplification. In turn, more than 2 years have passed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, during which time molecular diagnostics for infectious diseases have assumed a larger global role than ever before. In this context, we review broadly the progression of molecular techniques in clinical microbiology, to their current prominence. Notably, these methods now entail both the detection and quantification of microbial nucleic acids, along with their sequence-based characterization. Overall, we seek to provide a combined perspective on the techniques themselves, as well as how they have come to shape health care at the intersection of technologic innovation, pathophysiologic knowledge, clinical/laboratory logistics, and even financial/regulatory factors.

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department

BACKGROUND AND IMPORTANCE

mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis.

OBJECTIVE

Validation of the host-mRNA infection severity classifier IMX-SEV-2.

DESIGN, SETTINGS AND PARTICIPANTS

Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections.

OUTCOME MEASURES AND ANALYSIS

Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure.

MAIN RESULTS

Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P  = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86).

CONCLUSION

The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.

Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future

Clinical microbiology has possessed a marvellous past, an important present and a bright future. Western medicine modernization started with the discovery of bacterial pathogens, and from then, clinical bacteriology became a cornerstone of diagnostics. Today, clinical microbiology uses standard techniques including Gram stain morphology, in vitro culture, antigen and antibody assays, and molecular biology both to establish a diagnosis and monitor the progression of microbial infections. Clinical microbiology has played a critical role in pathogen detection and characterization for emerging infectious diseases as evidenced by the ongoing COVID-19 pandemic. Revolutionary changes are on the way in clinical microbiology with the application of “-omic” techniques, including transcriptomics and metabolomics, and optimization of clinical practice configurations to improve outcomes of patients with infectious diseases.

Application of Biomarkers in the Diagnostic Distinction of Bacterial and Viral Infections

Infectious diseases, which pose a great threat worldwide, have a significant impact on public health and the world economy. It contributes to increased healthcare costs, unnecessary drug-related side effects, and increased antimicrobial resistance. It is not always easy to distinguish the etiological differentiation of diseases that can develop with bacteria and viruses. Therefore, one of the biggest challenges in medicine is how to correctly distinguish between the different causes of these infections and how to manage the patient. Because bacterial and viral infections often present similar symptoms. The real decision is whether the infection is caused by bacteria or viruses and whether to treat the patient with antibiotics. There are many different methodological approaches to diagnosing infections. Biomarkers have been used in the diagnosis of diseases and other conditions for many years. Biomarkers are molecules found in blood and body fluids in measurable amounts, which can evaluate biological and pathological processes. These key indicators can provide vital information in determining disease prognosis, predicting response to treatments, adverse events and drug interactions, and identifying key risks. An effective biomarker is extremely important for the early diagnosis of various diseases. The explosion of interest in biomarker research is driving the development of new predictive, diagnostic, and prognostic products in modern medical practice. The purpose of this review is to demonstrate the use and diagnostic potential of current and investigational biomarkers in the distinction between bacterial and viral infections.

Childhood Pneumonia Diagnostics in Sub-Saharan Africa: A Systematic Review

BACKGROUND

The prompt and accurate aetiological diagnosis of childhood pneumonia remains a challenge, especially in sub-Saharan Africa (SSA) because of limited resources for disease management.

OBJECTIVE

To review existing diagnostics for childhood pneumonia and potential modalities available to differentiate between bacterial and viral aetiologies in SSA.

METHODS

Online databases were searched for relevant articles published between January 2010 and December 2020 regarding childhood pneumonia diagnosis, conducted in SSA in children less than 18 years of age. The 2020 PRISMA checklist was utilized in appraising the selected studies and the QUADAS-2 tool was employed to assess the risk of bias in each of the studies selected.

RESULTS

A total of 1542 study titles and abstracts were screened following which 45 studies (39 on childhood pneumonia diagnostics and 6 on discriminating between bacterial and viral childhood pneumonia) were selected for review. Microbiological investigations (79.7%) constituted the most utilized index tests with blood-related specimen (32.8%) being the most utilized specimen. The most performed index diagnostic modality was polymerase chain reaction (PCR) (53.1%). The commonest reference gold standard technique was based on clinical diagnosis of the disease (46.2%). Only six studies in SSA attempted at using serum biomarkers, either singly or in combination to distinguish between aetiologies with use of combined biomarkers showing promise.

CONCLUSION

Microbiological investigations are the most employed diagnostic methods for childhood pneumonia in SSA. More studies are required to evaluate the potential use of serum biomarkers; either singly or in combination with the goal of discriminating bacterial and viral childhood pneumonia.

Myxovirus resistance protein A for discriminating between viral and bacterial lower respiratory tract infections in children - The TREND study

OBJECTIVE

Discriminating between viral and bacterial lower respiratory tract infection (LRTI) in children is challenging, leading to an excessive use of antibiotics. Myxovirus resistance protein A (MxA) is a promising biomarker for viral infections. The primary aim of the study was to assess differences in blood MxA levels between children with viral and bacterial LRTI. Secondary aims were to assess differences in blood MxA levels between children with viral LRTI and asymptomatic controls and to assess MxA levels in relation to different respiratory viruses.

METHODS

Children with LRTI were enrolled as cases at Sachs' Children and Youth Hospital, Stockholm, Sweden. Nasopharyngeal aspirates and blood samples for analysis of viral PCR, MxA and CRP were systematically collected from all study subjects in addition to standard laboratory/radiology assessment. Aetiology was defined according to an algorithm based on laboratory and radiological findings. Asymptomatic children with minor surgical disease were enrolled as controls.

RESULTS

MxA levels were higher in children with viral LRTI (n=242) as compared to both bacterial (n=5) LRTI (p<0.01, area under the curve (AUC) 0.90, 95% confidence interval (CI):0.81-0.99) and controls (AUC 0.92, 95% CI:0.88-0.95). In the subgroup of children with pneumonia diagnosis, a cut-off of MxA 430μg/l discriminated between viral (n=29) and bacterial (n=4) aetiology with 93% (95% CI: 78%-99%) sensitivity and 100% (95% CI: 51%-100%) specificity (AUC 0.98, 95% CI: 0.94-1.00). The highest MxA levels were seen in cases PCR positive for influenza (median MxA 1699μg/l, interquartile range (IQR): 732-2996) and respiratory syncytial virus (median MxA 1115μg/l, IQR: 679-2489).

CONCLUSION

MxA accurately discriminated between viral and bacterial aetiology in children with LRTI, particularly in the group of children with pneumonia diagnosis, but the number of children with bacterial LRTI was low.

A point-of-need platform for rapid measurement of a host-protein score that differentiates bacterial from viral infection: analytical evaluation

The objective was to evaluate the analytical performance of a new point-of-need platform for rapid and accurate measurement of a host-protein score that differentiates between bacterial and viral infection. The system comprises a dedicated test cartridge (MeMed BV®) and an analyzer (MeMed Key®). In each run, three host proteins (TRAIL, IP-10 and CRP) are measured quantitatively and a combinational score (0-100) computed that indicates the likelihood of Bacterial versus Viral infection (BV score). Serum samples collected from patients with acute infection representing viral (0 ≤ score < 35), equivocal (35 ≤ score ≤ 65), or bacterial (65 < score ≤ 100) scores based on pre-defined score cutoffs were employed for the analytical evaluation studies as well as samples from healthy individuals. To assess reproducibility, triplicate runs were conducted at 3 different sites, on 2 analyzers per site over 5 non-consecutive days. Lower limit of quantitation (LLoQ) and analytical measurement range were established utilizing recombinant proteins. Sample stability was evaluated using patient samples representative of BV score range (0-100). MeMed Key® and MeMed BV® passed the acceptance criteria for each study. In the reproducibility study, TRAIL, IP-10 and CRP measurements ranged with coefficient of variation from 9.7 to 12.7%, 4.6 to 6.2% and 5.0 to 11.6%, respectively. LLoQ concentrations were established as 15 pg/mL, 100 pg/mL and 1 mg/L for TRAIL, IP-10 and CRP, respectively. To date, sample stability when frozen is 14 months. In summary, the analytical performance reported here, along with diagnostic accuracy established in the Apollo clinical validation study (NCT04690569), supports that MeMed BV® run on MeMed Key® can serve as a tool to assist clinicians in differentiating between bacterial and viral infection.

A Novel Algorithm With Paired Predictive Indexes to Stratify the Risk Levels of Neonates With Invasive Bacterial Infections: A Multicenter Cohort Study

BACKGROUND

Our aim was to develop a predictive model comprising clinical and laboratory parameters for early identification of full-term neonates with different risks of invasive bacterial infections (IBIs).

METHODS

We conducted a retrospective study including 1053 neonates presenting in 9 tertiary hospitals in China from January 2010 to August 2019. An algorithm with paired predictive indexes (PPIs) for risk stratification of neonatal IBIs was developed. Predictive performance was validated using k-fold cross-validation.

RESULTS

Overall, 166 neonates were diagnosed with IBIs (15.8%). White blood cell count, C-reactive protein level, procalcitonin level, neutrophil percentage, age at admission, neurologic signs, and ill-appearances showed independent associations with IBIs from stepwise regression analysis and combined into 23 PPIs. Using 10-fold cross-validation, a combination of 7 PPIs with the highest predictive performance was picked out to construct an algorithm. Finally, 58.1% (612/1053) patients were classified as low-risk cases. The sensitivity and negative predictive value of the algorithm were 95.3% (95% confidence interval: 91.7-98.3) and 98.7% (95% confidence interval: 97.8-99.6), respectively. An online calculator based on this algorithm was developed for clinical use.

CONCLUSIONS

The new algorithm constructed for this study was a valuable tool to screen neonates with suspected infection. It stratified risk levels of IBIs and had an excellent predictive performance.

BV score differentiates viral from bacterial-viral co-infection in adenovirus PCR positive children

BACKGROUND AND OBJECTIVES

Adenovirus causes acute respiratory illness that can mimic bacterial infection, making it challenging to differentiate adenoviral infection from adenoviral-bacterial co-infection. A host-protein score (BV score) for differentiating bacterial from viral infection that combines the expression levels of TNF-related apoptosis-induced ligand, interferon gamma-induced protein-10, and C-reactive protein exhibited a negative predictive value (NPV) of 98% in prior studies. Here we evaluate BV score's diagnostic accuracy in pediatrics with adenovirus PCR detection.

METHODS

This is a sub-analysis of children aged 3 months to 20 years with adenovirus PCR-positive infection recruited prospectively in two previous cohort studies. Reference standard diagnosis (bacterial, viral or indeterminate) was based on expert adjudication. BV score ranges from 0 to 100 and provides three results based on predefined cutoffs: viral or other non-bacterial etiology (0 ≤ score < 35), equivocal (35 ≤ score ≤ 65), and bacterial or co-infection (65 < score ≤ 100). Experts were blinded to BV results.

RESULTS

Out of 1,779 children, 142 had an adenovirus PCR-positive nasopharyngeal swab. Median age was 1.2 years (interquartile range 0.6-1.8), 50.7% were male and 52.8% were hospitalized. 12 cases were reference standard bacterial, 115 reference standard viral and 15 were indeterminate. BV score attained sensitivity of 100.0% (no false negatives), specificity of 89.5% (95% confidence interval: 83.2-95.8), and NPV of 100.0% (92.6-100.0). Equivocal rate was 19.7%.

CONCLUSIONS

BV score accurately differentiated between adenoviral and bacterial-adenoviral co-infection in this cohort of children with PCR-positive adenovirus detection. This performance supports a potential to improve appropriate antibiotic use.

Cost impact analysis of novel host-response diagnostic for patients with community-acquired pneumonia in the emergency department

BACKGROUND

There is significant over-prescription of antibiotics for suspected community-acquired pneumonia (CAP) patients as bacterial and viral pathogens are difficult to differentiate. To address this issue, a host response diagnostic called MeMed BV (MMBV) was developed that accurately differentiates bacterial from viral infection at the point of need by integrating measurements of multiple biomarkers. A literature-based cost-impact model was developed that compared the cost impact and clinical benefits between using the standard of care diagnostics combined with MMBV relative to standard of care diagnostics alone.

METHODS

The patient population was stratified according to the pneumonia severity index, and cost savings were considered from payer and provider perspectives. Four scenarios were considered. The main analysis considers the cost impact of differences in antibiotic stewardship and resulting adverse events. The first, second, and third scenarios combine the impacts on antibiotic stewardship with changes in hospital admission probability, length of hospital stay and diagnosis related group (DRG) reallocation, and hospital admission probability, length of stay, and DRG reallocation in combination, respectively.

RESULTS

The main analysis results show overall per-patient savings of $37 for payers and $223 for providers. Scenarios 1, 2, and 3 produced savings of $137, $189, and $293 for payers, and $339, $713, and $809 for providers, respectively.

LIMITATIONS

Models are simulations of real-world clinical processes, and are not sensitive to variations in clinical practice driven by differences in physician practice styles, differences in facility-level practice patterns, and patient comorbidities expected to exacerbate the clinical impact of CAP. Hospital models are limited to costs and do not consider differences in revenue associated with each approach.

CONCLUSIONS

Introducing MMBV to the current SOC diagnostic process is likely to be cost-saving to both hospitals and payers when considering impacts on antibiotic distribution, hospital admission rate, hospital LOS, and DRG reallocation.

A comparison of host response strategies to distinguish bacterial and viral infection

OBJECTIVES

Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI).

METHODS

In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests.

RESULTS

The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively.

CONCLUSIONS

A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI.

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

Differentiating viral from bacterial infections in febrile children is challenging and often leads to an unnecessary use of antibiotics. There is a great need for more accurate diagnostic tools. New molecular methods have improved the particular diagnostics of viral respiratory tract infections, but defining etiology can still be challenging, as certain viruses are frequently detected in asymptomatic children. For the detection of bacterial infections, time consuming cultures with limited sensitivity are still the gold standard. As a response to infection, the immune system elicits a cascade of events, which aims to eliminate the invading pathogen. Recent studies have focused on these host–pathogen interactions to identify pathogen-specific biomarkers (gene expression profiles), or “pathogen signatures”, as potential future diagnostic tools. Other studies have assessed combinations of traditional bacterial and viral biomarkers (C-reactive protein, interleukins, myxovirus resistance protein A, procalcitonin, tumor necrosis factor-related apoptosis-inducing ligand) to establish etiology. In this review we discuss the performance of such novel diagnostics and their potential role in clinical praxis. In conclusion, there are several promising novel biomarkers in the pipeline, but well-designed randomized controlled trials are needed to evaluate the safety of using these novel biomarkers to guide clinical decisions.

A host signature based on TRAIL, IP-10, and CRP for reducing antibiotic overuse in children by differentiating bacterial from viral infections: a prospective, multicentre cohort study

OBJECTIVES

Identifying infection aetiology is essential for appropriate antibiotic use. Previous studies have shown that a host-protein signature consisting of TNF-related apoptosis-induced ligand (TRAIL), interferon-γ-induced protein-10 (IP-10), and C-reactive protein (CRP) can accurately differentiate bacterial from viral infections.

METHODS

This prospective, multicentre cohort study, entitled AutoPilot-Dx, aimed to validate signature performance and to estimate its potential impact on antibiotic use across a broad paediatric population (>90 days to 18 years) with respiratory tract infections, or fever without source, at emergency departments and wards in Italy and Germany. Infection aetiology was adjudicated by experts based on clinical and laboratory investigations, including multiplex PCR and follow-up data.

RESULTS

In total, 1140 patients were recruited (February 2017-December 2018), of which 1008 met the eligibility criteria (mean age 3.5 years, 41.9% female). Viral and bacterial infections were adjudicated for 628 (85.8%) and 104 (14.2%) children, respectively; 276 patients were assigned an indeterminate reference standard outcome. For the 732 children with reference standard aetiology, the signature discriminated bacterial from viral infections with a sensitivity of 93.7% (95%CI 88.7-98.7), a specificity of 94.2% (92.2-96.1), positive predictive value of 73.0% (65.0-81.0), and negative predictive value of 98.9% (98.0-99.8); in 9.8% the test results were equivocal. The signature performed consistently across different patient subgroups and detected bacterial immune responses in viral PCR-positive patients.

CONCLUSIONS

The findings validate the high diagnostic performance of the TRAIL/IP-10/CRP signature in a broad paediatric cohort, and support its potential to reduce antibiotic overuse in children with viral infections.

A Novel 29-Messenger RNA Host-Response Assay From Whole Blood Accurately Identifies Bacterial and Viral Infections in Patients Presenting to the Emergency Department With Suspected Infections: A Prospective Observational Study

OBJECTIVES

The rapid diagnosis of acute infections and sepsis remains a serious challenge. As a result of limitations in current diagnostics, guidelines recommend early antimicrobials for suspected sepsis patients to improve outcomes at a cost to antimicrobial stewardship. We aimed to develop and prospectively validate a new, 29-messenger RNA blood-based host-response classifier Inflammatix Bacterial Viral Non-Infected version 2 (IMX-BVN-2) to determine the likelihood of bacterial and viral infections.

DESIGN

Prospective observational study.

SETTING

Emergency Department, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Germany.

PATIENTS

Three hundred twelve adult patients presenting to the emergency department with suspected acute infections or sepsis with at least one vital sign change.

INTERVENTIONS

None (observational study only).

MEASUREMENTS AND MAIN RESULTS

Gene expression levels from extracted whole blood RNA was quantified on a NanoString nCounter SPRINT (NanoString Technologies, Seattle, WA). Two predicted probability scores for the presence of bacterial and viral infection were calculated using the IMX-BVN-2 neural network classifier, which was trained on an independent development set. The IMX-BVN-2 bacterial score showed an area under the receiver operating curve for adjudicated bacterial versus ruled out bacterial infection of 0.90 (95% CI, 0.85-0.95) compared with 0.89 (95% CI, 0.84-0.94) for procalcitonin with procalcitonin being used in the adjudication. The IMX-BVN-2 viral score area under the receiver operating curve for adjudicated versus ruled out viral infection was 0.83 (95% CI, 0.77-0.89).

CONCLUSIONS

IMX-BVN-2 demonstrated accuracy for detecting both viral infections and bacterial infections. This shows the potential of host-response tests as a novel and practical approach for determining the causes of infections, which could improve patient outcomes while upholding antimicrobial stewardship.

Seeking diagnostic and prognostic biomarkers for childhood bacterial pneumonia in sub-Saharan Africa: study protocol for an observational study

INTRODUCTION

Clinically diagnosed pneumonia in children is a leading cause of paediatric hospitalisation and mortality. The aetiology is usually bacterial or viral, but malaria can cause a syndrome indistinguishable from clinical pneumonia. There is no method with high sensitivity to detect a bacterial infection in these patients and, as result, antibiotics are frequently overprescribed. Conversely, unrecognised concomitant bacterial infection in patients with malarial infections occur with omission of antibiotic therapy from patients with bacterial infections. Previously, we identified two combinations of blood proteins with 96% sensitivity and 86% specificity for detecting bacterial disease. The current project aimed to validate and improve these combinations by evaluating additional biomarkers in paediatric patients with clinical pneumonia. Our goal was to describe combinations of a limited number of proteins with high sensitivity and specificity for bacterial infection to be incorporated in future point-of-care tests. Furthermore, we seek to explore signatures to prognosticate clinical pneumonia.

METHODS AND ANALYSIS

Patients (n=900) aged 2-59 months presenting with clinical pneumonia at two Gambian hospitals will be enrolled and classified according to criteria for definitive bacterial aetiology (based on microbiological tests and chest radiographs). We will measure proteins at admission using Luminex-based immunoassays in 90 children with definitive and 160 with probable bacterial aetiology, and 160 children classified according to the prognosis of their disease. Previously identified diagnostic signatures will be assessed through accuracy measures. Moreover, we will seek new diagnostic and prognostic signatures through machine learning methods, including support vector machine, penalised regression and classification trees.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from the Gambia Government/Medical Research Council Unit The Gambia Joint Ethics Committee (protocol 1616) and the institutional review board of Boston University Medical Centre (STUDY00000958). Study results will be disseminated to the staff of the study hospitals, in scientific seminars and meetings, and in publications.

TRIAL REGISTRATION NUMBER

H-38462.

FAM89A and IFI44L for distinguishing between viral and bacterial infections in children with febrile illness

IMPORTANCE

The current lack of reliable rapid tests for distinguishing between bacterial and viral infections has contributed to antibiotic misuse.

OBJECTIVE

This study aimed to develop a novel biomarker assay that integrates FAM89A and IFI44L measurements to assist in differentiating between bacterial and viral infections.

METHODS

This prospective study recruited children with febrile illness from two hospitals between July 1, 2018, and June 30, 2019. A panel of three experienced pediatricians performed reference standard diagnoses of all patients (i.e., bacterial or viral infection) using available clinical and laboratory data, including a 28-day follow-up assessment. Assay operators were blinded to the reference standard diagnoses. The expression levels of FAM89A and IFI44L were determined by quantitative real-time polymerase chain reaction assessment.

RESULTS

Of 133 potentially eligible patients with suspected bacterial or viral infection, 35 were excluded after the application of exclusion criteria. The resulting cohort included 98 patients: 59 with viral diagnoses and 39 with bacterial diagnoses. The areas under the curve (AUCs) of diagnoses using FAM89A and IFI44L were 0.694 [95% confidence interval (CI): 0.583-0.804] and 0.751 (95% CI: 0.651-0.851), respectively. The disease risk score (DRS) [log2(FAM89A expression) - log2(IFI44L expression)] signature achieved an improved area under the receiver operating characteristic curve (AUC, 0.825; 95% CI: 0.735-0.915), compared with the AUC generated from individual host RNA. A combination of the DRS and the C-reactive protein (CRP) level achieved an AUC of 0.896 (95% CI: 0.825-0.966). Optimal cutoffs for the DRS and CRP level were -3.18 and 19.80 mg/L, respectively.

INTERPRETATION

The DRS was significantly more accurate than the CRP level in distinguishing between bacterial and viral infections; the combination of these two parameters exhibited greater sensitivity and specificity. This study provides information that could be useful for the clinical application of FAM89A and IFI44L in terms of distinguishing between viral and bacterial infections.

A two-transcript biomarker of host classifier genes for discrimination of bacterial from viral infection in acute febrile illness: a multicentre discovery and validation study

BACKGROUND

Acute febrile illness is one of the main reasons for outpatient hospital visits worldwide. However, differential diagnosis between bacterial and viral causes is challenging and misdiagnosis can result in antimicrobial overuse and hinder prompt treatment. We aimed to build and validate a diagnostic model to discriminate bacterial from viral infection in acute febrile illness by evaluating the expression of potential classifier host genes.

METHODS

In this multicentre discovery and validation study, we included patients aged 14-85 years with acute febrile illness (fever for ≤14 days, axillary temperature of ≥38°C, and confirmed bacterial infection, viral infection, or non-infectious inflammatory disease), and healthy control participants (no significant medical history and no fever within the past 90 days) from four hospitals in Shandong province, China. Patients from the first hospital were divided into the screening, discovery, and internal validation groups, and patients from the three other hospitals comprised the external validation group. We measured expression of candidate genes in peripheral blood by RT-PCR, and patients for whom a successful RT-PCT result was recorded were included in the next-step analysis. For patients from the first hospital, those enrolled during the early phase of the study were assigned to the screening group, which was used to identify the optimal transcripts (IFI44L and PI3) for discrimination between bacterial and viral infections by screening four candidate genes (FAM89A, IFI44L, PI3, and ITGB2) by RT-PCR. The remaining patients were then randomly assigned (1:1) to discovery and internal validation groups by time of admission and blood drawing via the equidistant random sampling method. A logistic regression model integrating the mRNA levels of IFI44L and PI3 was built by use of the discovery group, and the diagnostic performance of the model was evaluated in the internal and external validation groups using area under the receiver operating curve (AUC), sensitivity, and specificity.

FINDINGS

Between March 1, 2018, and Aug 31, 2019, we assessed 1658 individuals for inclusion in the study. After exclusion of ineligible participants, 458 participants were enrolled (178 patients with acute febrile illness caused by bacterial infection, 212 with acute febrile illness caused by viral infection, 38 with non-infectious inflammatory diseases, and 30 healthy controls). The 390 patients with bacterial or viral infections were assigned to one of four groups: screening (n=64, 33 with bacterial infections and 31 with viral infections), discovery (n=124, 55 with bacterial infections and 69 with viral infections), internal validation (n=124, 55 with bacterial infections and 69 with viral infections), and external validation (n=78, 35 with bacterial infections and 43 with viral infections). Of the four candidate host genes (FAM89A, IFI44L, PI3, and ITGB2), IFI44L and PI3 showed the most discriminative expression pattern and were used to build the logistic regression model. We established the optimal cutoff of the bacterial infection likelihood score to be 0·547598. With the diagnostic result from the gold standard tests (culture and PCR) as the reference, the two-transcript classifier model had an AUC of 0·969 (95% CI 0·937-1·000), sensitivity of 0·891 (0·782-0·949), and specificity of 0·971 (0·900-0·992) to discriminate bacterial and viral infections in the internal validation group. The model showed similar results in the external validation group (AUC 0·986, 95% CI 0·968-1·000; sensitivity 0·857, 0·706-0·937; and specificity 0·954, 0·845-0·987).

INTERPRETATION

IFI44L and PI3 transcripts, measured by RT-PCR, are robust classifiers to discriminate bacterial from viral infection in acute febrile illness. This two-transcript biomarker has the potential to be transformed into a commercial panel and applied universally.

FUNDING

None.

A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies

Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI. Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0- <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination. Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis ("definite bacterial" category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis ("definite viral" category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of "definite bacterial" vs. "definite viral" following the PERFORM algorithm than using the "SBI" vs. "non-SBI" classification; summary AUC for PCT was 0.77 (95% CI 0.72-0.82) vs. 0.70 (95% CI 0.65-0.75); for NGAL this was 0.80 (95% CI 0.69-0.91) vs. 0.70 (95% CI 0.58-0.81); for resistin this was 0.68 (95% CI 0.61-0.75) vs. 0.64 (0.58-0.69) The three biomarkers combined had summary AUC of 0.83 (0.77-0.89) for "definite bacterial" vs. "definite viral" infections and 0.71 (0.67-0.74) for "SBI" vs. "non-SBI." Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.

Development and validation of a prediction model for invasive bacterial infections in febrile children at European Emergency Departments: MOFICHE, a prospective observational study

OBJECTIVES

To develop and cross-validate a multivariable clinical prediction model to identify invasive bacterial infections (IBI) and to identify patient groups who might benefit from new biomarkers.

DESIGN

Prospective observational study.

SETTING

12 emergency departments (EDs) in 8 European countries.

PATIENTS

Febrile children aged 0-18 years.

MAIN OUTCOME MEASURES

IBI, defined as bacteraemia, meningitis and bone/joint infection. We derived and cross-validated a model for IBI using variables from the Feverkidstool (clinical symptoms, C reactive protein), neurological signs, non-blanching rash and comorbidity. We assessed discrimination (area under the receiver operating curve) and diagnostic performance at different risk thresholds for IBI: sensitivity, specificity, negative and positive likelihood ratios (LRs).

RESULTS

Of 16 268 patients, 135 (0.8%) had an IBI. The discriminative ability of the model was 0.84 (95% CI 0.81 to 0.88) and 0.78 (95% CI 0.74 to 0.82) in pooled cross-validations. The model performed well for the rule-out threshold of 0.1% (sensitivity 0.97 (95% CI 0.93 to 0.99), negative LR 0.1 (95% CI 0.0 to 0.2) and for the rule-in threshold of 2.0% (specificity 0.94 (95% CI 0.94 to 0.95), positive LR 8.4 (95% CI 6.9 to 10.0)). The intermediate thresholds of 0.1%-2.0% performed poorly (ranges: sensitivity 0.59-0.93, negative LR 0.14-0.57, specificity 0.52-0.88, positive LR 1.9-4.8) and comprised 9784 patients (60%).

CONCLUSIONS

The rule-out threshold of this model has potential to reduce antibiotic treatment while the rule-in threshold could be used to target treatment in febrile children at the ED. In more than half of patients at intermediate risk, sensitive biomarkers could improve identification of IBI and potentially reduce unnecessary antibiotic prescriptions.

Challenge of diagnosing acute infections in poor resource settings in Africa

Frequent disease outbreaks and acute infections occur in rural and low-income urban areas of Africa, with many health systems unprepared to diagnose and control diseases that are recurrent, endemic or have extended their geographic zone. In this review, we focus on acute infections that can be characterized by sudden onset, rapid progression, severe symptoms and poor prognosis. Consequently, these infections require early diagnosis and intervention. While effective vaccines have been developed against some of these diseases, lack of compliance and accessibility, and the need for repeated or multiple vaccinations mean large populations can remain vulnerable to infection. It follows that there is a need for enhancement of national surveillance and diagnostic capacity to avert morbidity and mortality from acute infections. We discuss the limitations of traditional diagnostic methods and explore the relative merits and applicability of protein-, carbohydrate- and nucleic acid-based rapid diagnostic tests that have been trialled for some infectious diseases. We also discuss the utility and limitations of antibody-based serological diagnostics and explore how systems biology approaches can better inform diagnosis. Lastly, given the complexity and high cost associated with after-service support of emerging technologies, we propose that, for resource-limited settings in Africa, multiplex point-of-care diagnostic tools be tailor-made to detect both recurrent acute infections and endemic infections.

Simultaneous Detection of Inflammatory Biomarkers by SERS Nanotag-Based Lateral Flow Assay with Portable Cloud Raman Spectrometer

Inflammatory biomarkers are closely related to infectious diseases. However, traditional clinical tests of laboratory inspection are unable to achieve rapid and accurate detection of these biomarkers on-site due to shortcomings such as complex experimental operation, expensive equipment, and long test time. Herein, we proposed a lateral flow assay (LFA) strip based on surface-enhanced Raman scattering (SERS) nanotags (SERS-LFA strips) for the simultaneous and quantitative detection of dual infection biomarkers, serum amyloid A (SAA) and C-reactive protein (CRP), respectively. In practice, mesoporous silica (mSiO2)-coated Au nanoparticles (Au NPs) were used as the SERS substrate. Mercaptobenzoic acid (MBA) was embedded in the internal gap between Au NPs and the mSiO2 shell to prepare AuMBA@mSiO2 NPs, onto which SAA and CRP antibodies were modified to prepare two AuMBA@mSiO2 SERS nanotags. The Raman intensities of the test and control lines were simultaneously identified for the qualitative detection of SAA and CRP, with limits of detection (LODs) as low as 0.1 and 0.05 ng/mL for SAA and CRP, respectively. Finally, aiming at point-of-care testing (POCT) applications, we used a smartphone-based portable Raman spectrometer to quantitatively analyze the SERS-LFA strips. The Raman signal could still be accurately detected when the concentration of SAA and CRP was 10 ng/mL, which is lower than the LOD required in clinical practice for most diseases. Therefore, taking into account its simple operation and short analysis time, by using a portable Raman spectrometer which can be equipped with a 5G cloud-based healthcare management system, the current strategy based on SERS-LFA provides the potential for the quick and on-site diagnosis of infectious diseases such as sepsis, which is of great significance for medical guidance on the treatment of widely spread infection-related diseases in remote areas that lack well-developed medical resources.

Future Biomarkers for Infection and Inflammation in Febrile Children

Febrile patients, suffering from an infection, inflammatory disease or autoimmunity may present with similar or overlapping clinical symptoms, which makes early diagnosis difficult. Therefore, biomarkers are needed to help physicians form a correct diagnosis and initiate the right treatment to improve patient outcomes following first presentation or admittance to hospital. Here, we review the landscape of novel biomarkers and approaches of biomarker discovery. We first discuss the use of current plasma parameters and whole blood biomarkers, including results obtained by RNA profiling and mass spectrometry, to discriminate between bacterial and viral infections. Next we expand upon the use of biomarkers to distinguish between infectious and non-infectious disease. Finally, we discuss the strengths as well as the potential pitfalls of current developments. We conclude that the use of combination tests, using either protein markers or transcriptomic analysis, have advanced considerably and should be further explored to improve current diagnostics regarding febrile infections and inflammation. If proven effective when combined, these biomarker signatures will greatly accelerate early and tailored treatment decisions.

A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study

Objective

To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full‐term neonate's risk level of having bacterial meningitis (BM).

Methods

A multicentered dataset was categorized into derivation (689 full‐term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed.

Results

In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C‐reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm’s sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%–98.9%) and 98.9% (95% CI 97.6%–99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%–100%) and 98.8% (95% CI 97.7%–100%).

Interpretation

The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP‐related decision‐making.

A host-based two-gene model for the identification of bacterial infection in general clinical settings

OBJECTIVES

In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings.

METHODS

We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals.

RESULTS

We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set.

CONCLUSIONS

We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.