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.

Antibiotic misuse in respiratory tract infections in children and adults-a prospective, multicentre study (TAILORED Treatment).

Respiratory tract infections (RTI) are more commonly caused by viral pathogens in children than in adults. Surprisingly, little is known about antibiotic use in children as compared to adults with RTI. This prospective study aimed to determine antibiotic misuse in children and adults with RTI, using an expert panel reference standard, in order to prioritise the target age population for antibiotic stewardship interventions. We recruited children and adults who presented at the emergency department or were hospitalised with clinical presentation of RTI in The Netherlands and Israel. A panel of three experienced physicians adjudicated a reference standard diagnosis (i.e. bacterial or viral infection) for all the patients using all available clinical and laboratory information, including a 28-day follow-up assessment. The cohort included 284 children and 232 adults with RTI (median age, 1.3 years and 64.5 years, respectively). The proportion of viral infections was larger in children than in adults (209(74%) versus 89(38%), p < 0.001). In case of viral RTI, antibiotics were prescribed (i.e. overuse) less frequently in children than in adults (77/209 (37%) versus 74/89 (83%), p < 0.001). One (1%) child and three (2%) adults with bacterial infection were not treated with antibiotics (i.e. underuse); all were mild cases. This international, prospective study confirms major antibiotic overuse in patients with RTI. Viral infection is more common in children, but antibiotic overuse is more frequent in adults with viral RTI. Together, these findings support the need for effective interventions to decrease antibiotic overuse in RTI patients of all ages.

A host-protein signature is superior to other biomarkers for differentiating between bacterial and viral disease in patients with respiratory infection and fever without source: a prospective observational study.

Bacterial and viral infections often present with similar symptoms. Etiologic misdiagnosis can alter the trajectory of patient care, including antibiotic overuse. A host-protein signature comprising tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein-10 (IP-10), and C-reactive protein (CRP) was validated recently for differentiating bacterial from viral disease. However, a focused head-to-head comparison of its diagnostic performance against other biomarker candidates for this indication was lacking in patients with respiratory infection and fever without source. We compared the signature to other biomarkers and prediction rules using specimens collected prospectively at two secondary medical centers from children and adults. Inclusion criteria included fever > 37.5 °C, symptom duration ≤ 12 days, and presentation with respiratory infection or fever without source. Comparator method was based on expert panel adjudication. Signature and biomarker cutoffs and prediction rules were predefined. Of 493 potentially eligible patients, 314 were assigned unanimous expert panel diagnosis and also had sufficient specimen volume. The resulting cohort comprised 175 (56%) viral and 139 (44%) bacterial infections. Signature sensitivity 93.5% (95% CI 89.1-97.9%), specificity 94.3% (95% CI 90.7-98.0%), or both were significantly higher (all p values < 0.01) than for CRP, procalcitonin, interleukin-6, human neutrophil lipocalin, white blood cell count, absolute neutrophil count, and prediction rules. Signature identified as viral 50/57 viral patients prescribed antibiotics, suggesting potential to reduce antibiotic overuse by 88%. The host-protein signature demonstrated superior diagnostic performance in differentiating viral from bacterial respiratory infections and fever without source. Future utility studies are warranted to validate potential to reduce antibiotic overuse.

ENViz: a Cytoscape App for integrated statistical analysis and visualization of sample-matched data with multiple data types.

ENViz (Enrichment Analysis and Visualization) is a Cytoscape app that performs joint enrichment analysis of two types of sample matched datasets in the context of systematic annotations. Such datasets may be gene expression or any other high-throughput data collected in the same set of samples. The enrichment analysis is done in the context of pathway information, gene ontology or any custom annotation of the data. The results of the analysis consist of significant associations between profiled elements of one of the datasets to the annotation terms (e.g. miR-19 was associated to the cell-cycle process in breast cancer samples). The results of the enrichment analysis are visualized as an interactive Cytoscape network.

miRNA target enrichment analysis reveals directly active miRNAs in health and disease.

microRNAs (miRNAs) are short non-coding regulatory RNA molecules. The activity of a miRNA in a biological process can often be reflected in the expression program that characterizes the outcome of the activity. We introduce a computational approach that infers such activity from high-throughput data using a novel statistical methodology, called minimum-mHG (mmHG), that examines mutual enrichment in two ranked lists. Based on this methodology, we provide a user-friendly web application that supports the statistical assessment of miRNA target enrichment analysis (miTEA) in the top of a ranked list of genes or proteins. Using miTEA, we analyze several target prediction tools by examining performance on public miRNA constitutive expression data. We also apply miTEA to analyze several integrative biology data sets, including a novel matched miRNA/mRNA data set covering nine human tissue types. Our novel findings include proposed direct activity of miR-519 in placenta, a direct activity of the oncogenic miR-15 in different healthy tissue types and a direct activity of the poorly characterized miR-768 in both healthy tissue types and cancer cell lines. The miTEA web application is available at http://cbl-gorilla.cs.technion.ac.il/miTEA/.

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.

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. 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.

Silencing of a large microRNA cluster on human chromosome 14q32 in melanoma: biological effects of mir-376a and mir-376c on insulin growth factor 1 receptor.

BACKGROUND: Metastatic melanoma is a devastating disease with limited therapeutic options. MicroRNAs (miRNAs) are small non coding RNA molecules with important roles in post-transcriptional gene expression regulation, whose aberrant expression has been implicated in cancer. RESULTS: We show that the expression of miRNAs from a large cluster on human chromosome 14q32 is significantly down-regulated in melanoma cell lines, benign nevi and melanoma samples relative to normal melanocytes. This miRNA cluster resides within a parentally imprinted chromosomal region known to be important in development and differentiation. In some melanoma cell lines, a chromosomal deletion or loss-of-heterozygosity was observed in the cis-acting regulatory region of this cluster. In several cell lines we were able to re-express two maternally-induced genes and several miRNAs from the cluster with a combination of de-methylating agents and histone de-acetylase inhibitors, suggesting that epigenetic modifications take part in their silencing. Stable over-expression of mir-376a and mir-376c, two miRNAs from this cluster that could be re-expressed following epigenetic manipulation, led to modest growth retardation and to a significant decrease in migration in-vitro. Bioinformatic analysis predicted that both miRNAs could potentially target the 3'UTR of IGF1R. Indeed, stable expression of mir-376a and mir-376c in melanoma cells led to a decrease in IGF1R mRNA and protein, and a luciferase reporter assay indicated that the 3'UTR of IGF1R is a target of both mir-376a and mir-376c. CONCLUSIONS: Our work is the first to show that the large miRNA cluster on chromosome 14q32 is silenced in melanoma. Our results suggest that down-regulation of mir-376a and mir-376c may contribute to IGF1R over-expression and to aberrant negative regulation of this signaling pathway in melanoma, thus promoting tumorigenesis and metastasis.

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.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections.

BACKGROUND: The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI. RESULTS: Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the 'bacterial' patients and 82% of the 'viral' patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus). CONCLUSIONS: We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

Differential Serum and Urine CRP, IP-10, and TRAIL Levels in Pediatric Urinary Tract Infection.

Background: It is estimated that clinical evaluation and urinalysis are unable to diagnose >10% of urinary tract infections (UTI) in young children. TNF-related apoptosis induced ligand (TRAIL), interferon gamma induced protein-10 (IP-10), and C-reactive protein (CRP) exhibit differential expression in the blood in response to bacterial vs. viral infection. We assessed if the urinary and serum levels of these host biomarkers discriminate UTI, nephronia, and response to antibiotic treatment. Methods: Hospitalized febrile children aged <18 years with suspected UTI based on abnormal urinalysis were recruited prospectively between 2016 and 2018; also, non-febrile controls were recruited. Following urine culture results and hospitalization course, participants were divided into three groups based on AAP criteria and expert adjudication: UTI, viral infection, and indeterminate. Results: Seventy-three children were enrolled, 61 with suspected UTI and 12 non-febrile controls. Of the 61 with suspected UTI, 40 were adjudicated as UTI, 10 viral infection, and 11 as indeterminate. Urinary CRP and IP-10 levels were significantly higher in the UTI group (p ≤ 0.05). Urinary CRP differentiated UTI from non-bacterial etiology in children under and over 3 months of age, with AUCs 0.98 (95% CI: 0.93-1.00) and 0.82 (0.68-0.95), respectively. Similarly, urinary IP-10 discriminated with AUCs of 0.80 (0.59-1.00) and 0.90 (0.80-1.00), respectively. Serum CRP and IP-10 levels were significantly higher in UTI cases with nephronia (p ≤ 0.03). UTI-induced changes in the levels of urinary and serum biomarkers resolved during recovery. Conclusions: CRP, IP-10, and TRAIL represent biomarkers with potential to aid the clinician in diagnosis and management of UTI.

Observational cohort study of IP-10's potential as a biomarker to aid in inflammation regulation within a clinical decision support protocol for patients with severe COVID-19.

BACKGROUND: Treatment of severely ill COVID-19 patients requires simultaneous management of oxygenation and inflammation without compromising viral clearance. While multiple tools are available to aid oxygenation, data supporting immune biomarkers for monitoring the host-pathogen interaction across disease stages and for titrating immunomodulatory therapy is lacking. METHODS: In this single-center cohort study, we used an immunoassay platform that enables rapid and quantitative measurement of interferon γ-induced protein 10 (IP-10), a host protein involved in lung injury from virus-induced hyperinflammation. A dynamic clinical decision support protocol was followed to manage patients infected with severe acute respiratory syndrome coronavirus 2 and examine the potential utility of timely and serial measurements of IP-10 as tool in regulating inflammation. RESULTS: Overall, 502 IP-10 measurements were performed on 52 patients between 7 April and 10 May 2020, with 12 patients admitted to the intensive care unit. IP-10 levels correlated with COVID-19 severity scores and admission to the intensive care unit. Among patients in the intensive care unit, the number of days with IP-10 levels exceeding 1,000 pg/mL was associated with mortality. Administration of corticosteroid immunomodulatory therapy decreased IP-10 levels significantly. Only two patients presented with subsequent IP-10 flare-ups exceeding 1,000 pg/mL and died of COVID-19-related complications. CONCLUSIONS: Serial and readily available IP-10 measurements potentially represent an actionable aid in managing inflammation in COVID-19 patients and therapeutic decision-making. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04389645, retrospectively registered on May 15, 2020.

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists.

BACKGROUND: Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results. RESULTS: GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression). GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms. CONCLUSION: GOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. GOrilla is publicly available at: http://cbl-gorilla.cs.technion.ac.il

Clinically driven semi-supervised class discovery in gene expression data.

MOTIVATION: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries. RESULTS: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors. AVAILABILITY: MonoClaD (Monotone Class Discovery). See http:// bioinfo.cs.technion.ac.il/people/zohar/MonoClad/. SUPPLEMENTARY INFORMATION: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software. html

Automating a new host-protein assay for differentiating bacterial from viral infection to reduce operator hands-on time.

Distinguishing bacterial from viral infections is often challenging, leading to antibiotic misuse, and detrimental ramifications for the patient, the healthcare system and society. A novel ELISA-based assay that integrates the circulating levels of three host-response proteins (TRAIL, IP-10 and CRP) was developed to assist in differentiation between bacterial and viral etiologies. We developed a new protocol for measuring the host-based assay biomarkers using an automated ELISA workstation. The automated protocol was validated and was able to reduce technician hands-on time by 76%, while maintaining high analytical performance. Following automation, the assay has been incorporated into the routine workflow at a pediatric department, and is performed daily on admitted and emergency department patients. The automation protocol reduces the overall burden on the hospital laboratory performing the assay. This benefit has potential to promote adoption of the host-based assay, facilitating timely triage of febrile patients and prudent use of antibiotics.

A novel host-protein assay outperforms routine parameters for distinguishing between bacterial and viral lower respiratory tract infections.

Bacterial and viral lower respiratory tract infections (LRTIs) are often clinically indistinguishable, leading to antibiotic overuse. We compared the diagnostic accuracy of a new assay that combines 3 host-biomarkers (TRAIL, IP-10, CRP) with parameters in routine use to distinguish bacterial from viral LRTIs. Study cohort included 184 potentially eligible pediatric and adult patients. Reference standard diagnosis was based on adjudication by an expert panel following comprehensive clinical and laboratory investigation (including respiratory PCRs). Experts were blinded to assay results and assay performers were blinded to reference standard outcomes. Evaluated cohort included 88 bacterial and 36 viral patients (23 did not fulfill inclusion criteria; 37 had indeterminate reference standard outcome). Assay distinguished bacterial from viral LRTI patients with sensitivity of 0.93±0.06 and specificity of 0.91±0.09, outperforming routine parameters, including WBC, CRP and chest x-ray signs. These findings support the assay's potential to help clinicians avoid missing bacterial LRTIs or overusing antibiotics.

Validation of a Novel Assay to Distinguish Bacterial and Viral Infections.

BACKGROUND: Reliably distinguishing bacterial from viral infections is often challenging, leading to antibiotic misuse. A novel assay that integrates measurements of blood-borne host-proteins (tumor necrosis factor-related apoptosis-inducing ligand, interferon γ-induced protein-10, and C-reactive protein [CRP]) was developed to assist in differentiation between bacterial and viral disease. METHODS: We performed double-blind, multicenter assay evaluation using serum remnants collected at 5 pediatric emergency departments and 2 wards from children ≥3 months to ≤18 years without (n = 68) and with (n = 529) suspicion of acute infection. Infectious cohort inclusion criteria were fever ≥38°C and symptom duration ≤7 days. The reference standard diagnosis was based on predetermined criteria plus adjudication by experts blinded to assay results. Assay performers were blinded to the reference standard. Assay cutoffs were predefined. RESULTS: Of 529 potentially eligible patients with suspected acute infection, 100 did not fulfill infectious inclusion criteria and 68 had insufficient serum. The resulting cohort included 361 patients, with 239 viral, 68 bacterial, and 54 indeterminate reference standard diagnoses. The assay distinguished between bacterial and viral patients with 93.8% sensitivity (95% confidence interval: 87.8%-99.8%) and 89.8% specificity (85.6%-94.0%); 11.7% had an equivocal assay outcome. The assay outperformed CRP (cutoff 40 mg/L; sensitivity 88.2% [80.4%-96.1%], specificity 73.2% [67.6%-78.9%]) and procalcitonin testing (cutoff 0.5 ng/mL; sensitivity 63.1% [51.0%-75.1%], specificity 82.3% [77.1%-87.5%]). CONCLUSIONS: Double-blinded evaluation confirmed high assay performance in febrile children. Assay was significantly more accurate than CRP, procalcitonin, and routine laboratory parameters. Additional studies are warranted to support its potential to improve antimicrobial treatment decisions.

A novel host-proteome signature for distinguishing between acute bacterial and viral infections.

Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P<0.001), as well as routinely used clinical parameters and their combinations (P<0.001). It remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse.