Responses to Bacteria, Virus, and Malaria Distinguish the Etiology of Pediatric Clinical Pneumonia

Antibiotics for Paediatric Community-Acquired Pneumonia: What is the Optimal Course Duration?

Despite significant global reductions in cases of pneumonia during the last 3 decades, pneumonia remains the leading cause of post-neonatal mortality in children aged <5 years. Beyond the immediate disease burden it imposes, pneumonia contributes to long-term morbidity, including lung function deficits and bronchiectasis. Viruses are the most common cause of childhood pneumonia, but bacteria also play a crucial role. However, the optimal duration of antibiotic therapy for bacterial pneumonia remains uncertain in both low- and middle-income countries and in high-income countries. Knowing the optimal duration of antibiotic therapy for pneumonia is crucial for effective antimicrobial stewardship. This is especially important as concerns mount over rising antibiotic resistance in respiratory bacterial pathogens, which increases the risk of treatment failure. Numerous studies have focused on the duration of oral antibiotics and short-term outcomes, such as clinical cure and mortality. In contrast, only one study has examined both intravenous and oral antibiotics and their impact on long-term respiratory outcomes following pneumonia hospitalisation. However, study findings may be influenced by their inclusion criteria when children unlikely to have bacterial pneumonia are included. Efforts to differentiate between bacterial and non-bacterial pneumonia continue, but a validated, accurate, and simple point-of-care diagnostic test remains elusive. Without certainty that a child has bacterial pneumonia, determining the optimal duration of antibiotic treatment is challenging. This review examines the evidence for the recommended duration of antibiotics for treating uncomplicated pneumonia in otherwise healthy children and concludes that the question of duration is unresolved.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Circulating markers of neutrophil activation and lung injury in pediatric pneumonia in low-resource settings

Diagnostic biomarkers for childhood pneumonia could guide management and improve antibiotic stewardship in low-resource settings where chest x-ray (CXR) is not always available. In this cross-sectional study, we measured chitinase 3-like protein 1 (CHI3L1), surfactant protein D (SP-D), lipocalin-2 (LCN2), and tissue inhibitor of metalloproteinases-1 (TIMP-1) in Ugandan children under the age of five hospitalized with acute lower respiratory tract infection. We determined the association between biomarker levels and primary end-point pneumonia, indicated by CXR consolidation. We included 89 children (median age 11 months, 39% female). Primary endpoint pneumonia was present in 22 (25%). Clinical signs were similar in children with and without CXR consolidation. Broad-spectrum antibiotics (ceftriaxone) were administered in 83 (93%). Levels of CHI3L1, SP-D, LCN2 and TIMP-1 were higher in patients with primary end-point pneumonia compared to patients with normal CXR or other infiltrates. All markers were moderately accurate predictors of primary end-point pneumonia, with area under receiver operator characteristic curves of 0.66-0.70 (p<0.05 for all markers). The probability of CXR consolidation increased monotonically with the number of markers above cut-off. Among 28 patients (31%) in whom all four markers were below the cut-off, the likelihood ratio of CXR consolidation was 0.11 (95%CI 0.015 to 0.73). CHI3L1, SP-D, LCN2 and TIMP-1 were associated with CXR consolidation in children with clinical pneumonia in a low-resource setting. Combinations of quantitative biomarkers may be useful to safely withhold antibiotics in children with a low probability of bacterial infection.

A high α1-antitrypsin/interleukin-10 ratio predicts bacterial pneumonia in adults with community-acquired pneumonia: a prospective cohort study

BACKGROUND

Current microbiological tests fail to identify the causative microorganism in more than half of all pneumonia cases. We explored biomarkers that could be used for differentiating between bacterial and viral pneumonia in patients with community-acquired pneumonia (CAP).

METHODS

In this prospective cohort study conducted in Japan, data obtained from adult patients with bacterial pneumonia, including bacterial and viral coinfections (bacterial pneumonia [BP] group), and purely viral pneumonia (VP group) at diagnosis were analyzed using multivariate logistic regression analysis to identify predictors of bacterial pneumonia. Furthermore, a decision tree was developed using the predictors.

RESULTS

A total of 210 patients were analyzed. The BP and VP groups comprised 108 and 18 patients, respectively. The other 84 patients had no identified causative microorganism. The two groups shared similar characteristics, including disease severity; however, a significant difference (p < 0.05) was observed between the two groups regarding sputum type; sputum volume score; neutrophil counts; and serum levels of interleukin (IL)-8, IL-10, and α1-antitrypsin (AAT). Sputum volume score (p < 0.001), IL-10 (p < 0.001), and AAT (p = 0.008) were ultimately identified as predictors of BP. The area under the curve for these three variables on the receiver operating characteristic (ROC) curve was 0.927 (95% confidence interval [CI]: 0.881-0.974). The ROC curve for sputum volume score and an AAT/IL-10 ratio showed a diagnostic cutoff of 1 + and 65, respectively. Logistic regression analysis using dichotomized variables at the cutoff values showed that the odds ratios for the diagnosis of BP were 10.4 (95% CI: 2.2-50.2) for sputum volume score (absence vs. presence) and 19.8 (95% CI: 4.7-83.2) for AAT/IL-10 ratio (< 65 vs. ≥ 65).

CONCLUSIONS

Considering that obtaining a definitive etiologic diagnosis with the current testing methods is difficult and time consuming, a decision tree with two predictors, namely sputum volume and the AAT/IL-10 ratio, can be useful in predicting BP among patients diagnosed with CAP and facilitating the appropriate use of antibiotics.

TRIAL REGISTRATION

UMIN000034673 registered on November 29, 2018.

A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information

Purpose

This study aimed to develop a deep learning model based on chest radiography (CXR) images and clinical data to accurately classify gram-positive and gram-negative bacterial pneumonia in children to guide the use of antibiotics.

Methods

We retrospectively collected CXR images along with clinical information for gram-positive (n=447) and gram-negative (n=395) bacterial pneumonia in children from January 1, 2016, to June 30, 2021. Four types of machine learning models based on clinical data and six types of deep learning algorithm models based on image data were constructed, and multi-modal decision fusion was performed.

Results

In the machine learning models, CatBoost, which only used clinical data, had the best performance; its area under the receiver operating characteristic curve (AUC) was significantly higher than that of the other models (P<0.05). The incorporation of clinical information improved the performance of deep learning models that relied solely on image-based classification. Consequently, AUC and F1 increased by 5.6% and 10.2% on average, respectively. The best quality was achieved with ResNet101 (model accuracy: 0.75, recall rate: 0.84, AUC: 0.803, F1: 0.782).

Conclusion

Our study established a pediatric bacterial pneumonia model that utilizes CXR and clinical data to accurately classify cases of gram-negative and gram-positive bacterial pneumonia. The results confirmed that the addition of image data to the convolutional neural network model significantly improved its performance. While the CatBoost-based classifier had greater advantages owing to a smaller dataset, the quality of the Resnet101 model trained using multi-modal data was comparable to that of the CatBoost model, even with a limited number of samples.

Shine: A novel strategy to extract specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets

BACKGROUND

Concentrations of the pathogenic microorganisms' DNA in biological samples are typically low. Therefore, DNA diagnostics of common infections are costly, rarely accurate, and challenging. Limited by failing to cover updated epidemic testing samples, computational services are difficult to implement in clinical applications without complex customized settings. Furthermore, the combined biomarkers used to maintain high conservation may not be cost effective and could cause several experimental errors in many clinical settings. Given the limitations of recent developed technology, 16S rRNA is too conserved to distinguish closely related species, and mosaic plasmids are not effective as well because of their uneven distribution across prokaryotic taxa.

RESULTS

Here, we provide a computational strategy, Shine, that allows extraction of specific, sensitive and well-conserved biomarkers from massive microbial genomic datasets. Distinguished with simple concatenations with blast-based filtering, our method involves a de novo genome alignment-based pipeline to explore the original and specific repetitive biomarkers in the defined population. It can cover all members to detect newly discovered multicopy conserved species-specific or even subspecies-specific target probes and primer sets. The method has been successfully applied to a number of clinical projects and has the overwhelming advantages of automated detection of all pathogenic microorganisms without the limitations of genome annotation and incompletely assembled motifs. Using on our pipeline, users may select different configuration parameters depending on the purpose of the project for routine clinical detection practices on the website https://bioinfo.liferiver.com.cn with easy registration.

CONCLUSIONS

The proposed strategy is suitable for identifying shared phylogenetic markers while featuring low rates of false positive or false negative. This technology is suitable for the automatic design of minimal and efficient PCR primers and other types of detection probes.

Prognostic accuracy of biomarkers of immune and endothelial activation in Mozambican children hospitalized with pneumonia

Pneumonia is a leading cause of child mortality. However, currently we lack simple, objective, and accurate risk-stratification tools for pediatric pneumonia. Here we test the hypothesis that measuring biomarkers of immune and endothelial activation in children with pneumonia may facilitate the identification of those at risk of death. We recruited children <10 years old fulfilling WHO criteria for pneumonia and admitted to the Manhiça District Hospital (Mozambique) from 2010 to 2014. We measured plasma levels of IL-6, IL-8, Angpt-2, sTREM-1, sFlt-1, sTNFR1, PCT, and CRP at admission, and assessed their prognostic accuracy for in-hospital, 28-day, and 90-day mortality. Healthy community controls, within same age strata and location, were also assessed. All biomarkers were significantly elevated in 472 pneumonia cases versus 80 controls (p<0.001). IL-8, sFlt-1, and sTREM-1 were associated with in-hospital mortality (p<0.001) and showed the best discrimination with AUROCs of 0.877 (95% CI: 0.782 to 0.972), 0.832 (95% CI: 0.729 to 0.935) and 0.822 (95% CI: 0.735 to 0.908), respectively. Their performance was superior to CRP, PCT, oxygen saturation, and clinical severity scores. IL-8, sFlt-1, and sTREM-1 remained good predictors of 28-day and 90-day mortality. These findings suggest that measuring IL-8, sFlt-1, or sTREM-1 at hospital presentation can guide risk-stratification of children with pneumonia, which could enable prioritized care to improve survival and resource allocation.

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19

PURPOSE

This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages.

METHODS

Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve.

RESULTS

The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages.

CONCLUSION

MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.

Immune and endothelial activation markers and risk stratification of childhood pneumonia in Uganda: A secondary analysis of a prospective cohort study

BACKGROUND

Despite the global burden of pneumonia, reliable triage tools to identify children in low-resource settings at risk of severe and fatal respiratory tract infection are lacking. This study assessed the ability of circulating host markers of immune and endothelial activation quantified at presentation, relative to currently used clinical measures of disease severity, to identify children with pneumonia who are at risk of death.

METHODS AND FINDINGS

We conducted a secondary analysis of a prospective cohort study of children aged 2 to 59 months presenting to the Jinja Regional Hospital in Jinja, Uganda between February 2012 and August 2013, who met the Integrated Management of Childhood Illness (IMCI) diagnostic criteria for pneumonia. Circulating plasma markers of immune (IL-6, IL-8, CXCL-10/IP-10, CHI3L1, sTNFR1, and sTREM-1) and endothelial (sVCAM-1, sICAM-1, Angpt-1, Angpt-2, and sFlt-1) activation measured at hospital presentation were compared to lactate, respiratory rate, oxygen saturation, procalcitonin (PCT), and C-reactive protein (CRP) with a primary outcome of predicting 48-hour mortality. Of 805 children with IMCI pneumonia, 616 had severe pneumonia. Compared to 10 other immune and endothelial activation markers, sTREM-1 levels at presentation had the best predictive accuracy in identifying 48-hour mortality for children with pneumonia (AUROC 0.885, 95% CI 0.841 to 0.928; p = 0.03 to p < 0.001) and severe pneumonia (AUROC 0.870, 95% CI 0.824 to 0.916; p = 0.04 to p < 0.001). sTREM-1 was more strongly associated with 48-hour mortality than lactate (AUROC 0.745, 95% CI 0.664 to 0.826; p < 0.001), respiratory rate (AUROC 0.615, 95% CI 0.528 to 0.702; p < 0.001), oxygen saturation (AUROC 0.685, 95% CI 0.594 to 0.776; p = 0.002), PCT (AUROC 0.650, 95% CI 0.566 to 0.734; p < 0.001), and CRP (AUROC 0.562, 95% CI 0.472 to 0.653; p < 0.001) in cases of pneumonia and severe pneumonia. The main limitation of this study was the unavailability of radiographic imaging.

CONCLUSIONS

In this cohort of Ugandan children, sTREM-1 measured at hospital presentation was a significantly better indicator of 48-hour mortality risk than other common approaches to risk stratify children with pneumonia. Measuring sTREM-1 at clinical presentation may improve the early triage, management, and outcome of children with pneumonia at risk of death.

TRIAL REGISTRATION

The trial was registered at clinicaltrial.gov (NCT04726826).

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.

Serial evaluation of thoracic radiographs and acute phase proteins in dogs with pneumonia

Background

Acute phase proteins (APP) may guide treatment of pneumonia in dogs but correlations with radiographic abnormalities are poorly characterized.

Objectives

Develop a thoracic radiographic severity scoring system (TRSS), assess correlation of radiographic changes with APP concentrations, and compare time to APP and radiograph normalization with duration of antimicrobials treatment.

Animals

Sixteen client‐owned dogs, 12 with aspiration pneumonia, and 4 with community‐acquired pneumonia.

Methods

Concentrations of C‐reactive protein (CRP), serum amyloid A (SAA), and haptoglobin were measured on days 1, 3, 7, 14, 28, and 60 and orthogonal 2‐view thoracic radiographs were obtained on days 1, 7, 14, 28, and 60. Treatment was clinician‐guided and blinded to APP concentrations. Radiographic severity scores were assigned by blinded, randomized retrospective review by 2 board‐certified radiologists with arbitration by a third radiologist.

Results

Median (interquartile range [IQR]) time to normalization of CRP (7 days [7‐14]) and SAA concentrations (7 days [7‐14]) were shorter than antimicrobial treatment duration (17.5 days [14.5‐33.5]; P = .001 and .002, respectively) and TRSS normalization (14 days [8.8‐52], P = .02 and .02, respectively). The CRP and SAA concentrations were positively correlated with TRSS (CRP r_s, 0.643; SAA r_s, 0.634; both P < .0001). Both CRP and SAA identified normal thoracic radiographs area under the curve (AUC) 0.873 and 0.817, respectively, both P < .0001. Interobserver agreement for TRSS assignment was moderate (κ, .499; P < .0001).

Conclusion and Clinical Importance

Concentrations of CRP and SAA normalized before radiographic resolution and before clinicians discontinued antimicrobial treatment. The CRP and SAA concentrations may guide duration of antimicrobial treatment for dogs with pneumonia.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

Importance

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.

Objective

To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.

Design, Setting, and Participants

This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.

Interventions

The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.

Main Outcomes and Measures

Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement.

Results

Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.

Conclusions and Relevance

In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Association of Clinical Signs, Host Biomarkers and Etiology With Radiological Pneumonia in Bhutanese Children

Diagnosing pneumonia and identifying those requiring antibiotherapy remain challenging. Chest radiographs (CXR) are often used as the reference standard. We aimed to describe clinical characteristics, host-response biomarkers and etiology, and assess their relationship to CXR findings in children with pneumonia in Thimphu, Bhutan. Children between 2 and 59 months hospitalized with WHO-defined pneumonia were prospectively enrolled and classified into radiological endpoint and non-endpoint pneumonia. Blood and nasopharyngeal washing were collected for microbiological analyses and plasma levels of 11 host-response biomarkers were measured. Among 149 children with readable CXR, 39 (26.2%) presented with endpoint pneumonia. Identification of respiratory viruses was common, with no significant differences by radiological outcomes. No clinical sign was suggestive of radiological pneumonia, but children with radiological pneumonia presented higher erythrocyte sedimentation rate, C-reactive protein and procalcitonin. Markers of endothelial and immune activation had little accuracy for the reliable identification of radiological pneumonia.

Risk-stratification of febrile African children at risk of sepsis using sTREM-1 as basis for a rapid triage test

Identifying febrile children at risk of sepsis in low-resource settings can improve survival, but recognition triage tools are lacking. Here we test the hypothesis that measuring circulating markers of immune and endothelial activation may identify children with sepsis at risk of all-cause mortality. In a prospective cohort study of 2,502 children in Uganda, we show that Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1) measured at first clinical presentation, had high predictive accuracy for subsequent in-hospital mortality. sTREM-1 had the best performance, versus 10 other markers, with an AUROC for discriminating children at risk of death of 0.893 in derivation (95% CI 0.843-0.944) and 0.901 in validation (95% CI 0.856-0.947) cohort. sTREM-1 cutoffs corresponding to a negative likelihood ratio (LR) of 0.10 and a positive LR of 10 classified children into low (1,306 children, 53.1%), intermediate (942, 38.3%) and high (212, 8.6%) risk zones. The estimated incidence of death was 0.5%, 3.9%, and 31.8%, respectively, suggesting sTREM-1 could be used to risk-stratify febrile children. These findings do not attempt to derive a risk prediction model, but rather define sTREM-1 cutoffs as the basis for rapid triage test for all cause fever syndromes in children in low-resource settings.

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.

Novel point-of-care biomarker combination tests to differentiate acute bacterial from viral respiratory tract infections to guide antibiotic prescribing: a systematic review

BACKGROUND

Acute respiratory tract infections (RTIs) are the most common reason to seek medical care, with many patients receiving inappropriate antibiotics. Novel testing approaches to identify aetiology at the point-of-care are required to accurately guide antibiotic treatment.

OBJECTIVE

To assess the diagnostic accuracy of biomarker combinations to rapidly differentiate between acute bacterial or viral RTI aetiology.

DATA SOURCES

MEDLINE, Embase and Web of Science databases were searched to February 2021.

STUDY ELIGIBILITY CRITERIA

Diagnostic accuracy studies comparing accuracy of point-of-care and rapid diagnostic tests in primary or secondary care, consisting of biomarker combinations, to identify bacterial or viral aetiology of RTI.

METHODS

Risk of bias was assessed using the QUADAS-2 tool. Sensitivity and specificity of tests reported by more than one study were meta-analysed using a random effects model.

RESULTS

Twenty observational studies (3514 patients) were identified. Eighteen were judged at high risk of bias. For bacterial aetiologies, sensitivity ranged from 61% to 100% and specificity from 18% to 96%. For viral aetiologies, sensitivity ranged from 59% to 97% and specificity from 74% to 100%. Studies evaluating two commercial tests were meta-analysed. For ImmunoXpert, the summary sensitivity and specificity were 85% (95% CI 75%-91%, k = 4) and 86% (95% CI 73%-93%, k = 4) for bacterial infections, and 90% (95% CI 79%-96%, k = 3) and 92% (95% CI 83%-96%, k = 3) for viral infections, respectively. FebriDx had pooled sensitivity and specificity of 84% (95% CI 75%-90%, k = 4) and 93% (95% CI 90%-95%, k = 4) for bacterial infections, and 87% (95% CI 72%-95%; k = 4) and 82% (95% CI 66%-86%, k = 4) for viral infections, respectively.

CONCLUSION

Combinations of biomarkers show potential clinical utility in discriminating the aetiology of RTIs. However, the limitations in the evidence base, due to a high proportion of studies with high risk of bias, preclude firm conclusions. Future research should be in primary care and evaluate patient outcomes and cost-effectiveness with experimental study designs.

CLINICAL TRIAL

PROSPERO registration number: CRD42020178973.

Distinguishing bacterial versus non-bacterial causes of febrile illness - A systematic review of host biomarkers

BACKGROUND

Acute febrile illnesses (AFIs) represent a major disease burden globally; however, the paucity of reliable, rapid point-of-care testing makes their diagnosis difficult. A simple tool for distinguishing bacterial versus non-bacterial infections would radically improve patient management and reduce indiscriminate antibiotic use. Diagnostic tests based on host biomarkers can play an important role here, and a target product profile (TPP) was developed to guide development.

OBJECTIVES

To qualitatively evaluate host biomarkers that can distinguish bacterial from non-bacterial causes of AFI.

DATA SOURCES

The PubMed database was systematically searched for relevant studies published between 2015 and 2019.

STUDY ELIGIBILITY CRITERIA

Studies comparing diagnostic performances of host biomarkers in patients with bacterial versus non-bacterial infections were included.

PARTICIPANTS

Studies involving human participants and/or human samples were included.

METHODS

We collected information following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A risk of bias assessment was performed, based on a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Score 2).

RESULTS

We identified 1107 publications. Following screening, 55 publications were included, with 265 biomarker entries. Entries mostly comprised protein biomarkers (58.9%), followed by haematological, RNA, and metabolite biomarkers (15.5%, 8.7%, 12.5%). Sensitivity/specificity was reported for 45.7% of biomarker entries. We assessed a high overall risk of bias for most entries (75.8%). In studies with low/medium risk of bias, four biomarker entries tested in blood samples had sensitivity/specificity of more than 0.90/0.80. Only 12 additional biomarker entries were identified with sensitivity/specificity of more than 0.65/0.65.

CONCLUSIONS

Most recently assessed biomarkers represent well-known biomarkers, e.g. C-reactive protein and procalcitonin. Some protein biomarkers with the highest reported performances include a combined biomarker signature (CRP, IP-10, and TRAIL) and human neutrophil lipocalin (HNL). Few new biomarkers are in the pipeline; however, some RNA signatures show promise. Further high-quality studies are needed to confirm these findings.

Biomarkers to distinguish bacterial from viral pediatric clinical pneumonia in a malaria endemic setting

Background

Differential etiologies of pediatric acute febrile respiratory illness pose challenges for all populations globally, but especially in malaria-endemic settings because the pathogens responsible overlap in clinical presentation and frequently occur together. Rapid identification of bacterial pneumonia with high-quality diagnostic tools would enable appropriate, point-of-care antibiotic treatment. Current diagnostics are insufficient, and the discovery and development of new tools is needed. We report a unique biomarker signature identified in blood samples to accomplish this.

Methods

Blood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based, high-dynamic-range, quantitative assay (~1200 proteins). We identified new biomarkers using a training set of samples from patients with established bacterial, viral, or malarial pneumonia. Proteins with significantly variable abundance across etiologies (false discovery rate <0.01) formed the basis for predictive diagnostic models derived from machine learning techniques (Random Forest, Elastic Net). Validation on a dedicated test set of samples was performed.

Results

Significantly different abundances between bacterial and viral infections (219 proteins) and bacterial infections and mixed (viral and malaria) infections (151 proteins) were found. Predictive models achieved >90% sensitivity and >80% specificity, regardless of number of pathogen classes. Bacterial pneumonia was strongly associated with neutrophil markers—in particular, degranulation including HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI.

Conclusions

Blood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.

Cytokine Profile Distinguishes Children With Plasmodium falciparum Malaria From Those With Bacterial Blood Stream Infections

Background

Malaria presents with unspecific clinical symptoms that frequently overlap with other infectious diseases and is also a risk factor for coinfections, such as non-Typhi Salmonella. Malaria rapid diagnostic tests are sensitive but unable to distinguish between an acute infection requiring treatment and asymptomatic malaria with a concomitant infection. We set out to test whether cytokine profiles could predict disease status and allow the differentiation between malaria and a bacterial bloodstream infection.

Methods

We created a classification model based on cytokine concentration levels of pediatric inpatients with either Plasmodium falciparum malaria or a bacterial bloodstream infection using the Luminex platform. Candidate markers were preselected using classification and regression trees, and the predictive strength was calculated through random forest modeling.

Results

Analyses revealed that a combination of 7–15 cytokines exhibited a median disease prediction accuracy of 88% (95th percentile interval, 73%–100%). Haptoglobin, soluble Fas-Ligand, and complement component C2 were the strongest single markers with median prediction accuracies of 82% (with 95th percentile intervals of 71%–94%, 62%–94%, and 62%–94%, respectively).

Conclusions

Cytokine profiles possess good median disease prediction accuracy and offer new possibilities for the development of innovative point-of-care tests to guide treatment decisions in malaria-endemic regions.

Is Malaria an Important Cause of Death among Adults?

A long-held assumption has been that nearly all malaria deaths in high-transmission areas are of children younger than 5 years and pregnant women. Most global malaria mortality estimates incorporate this assumption in their calculations. In 2010, the Indian Million Death Study, which assigns cause of death by verbal autopsy (VA), challenged the reigning perception, producing a U-shaped mortality age curve, with rates rising after age 45 years in areas of India with substantial malaria transmission. Similar patterns are seen in Africa in the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) network, also relying on VA. Whether these results are accurate or are misidentified deaths can be resolved by improving the evidence for assigning causes for adult acute infectious deaths in high malaria transmission areas. The options for doing so include improving the accuracy of VA and adding postmortem biological evidence, steps we believe should be initiated without delay.

Blood biomarkers differentiating viral versus bacterial pneumonia aetiology: a literature review

Background and objectives

The goal of this literature review is to compare current studies regarding the accuracy of different serum markers in differentiating viral from bacterial pneumonia in the pediatric population with what is employed in the medical settings at present. Currently there is still a lack of significant research, that would give us evaluation on biomarkers benefits towards getting a definite diagnosis of pneumonia. Finding out the potential of biomarkers to differentiate between viral and bacterial pneumonia is also important because knowing the exact pathogen would prevent irrational use of antibiotics. At present, irrational, broad-spectrum antibiotic use and increasing antibiotic resistance in microorganisms are still one of the greatest challenges in clinical settings. The use of biomarkers in clinical practice would not only facilitate accurate diagnosis, but would also help to reduce the amount of antibiotics overuse.

Materials and methods

Literature search conducted on Medline and Google Scholar using a combination of terms. Articles that were in English and within ten years of the search date were manually sorted according to inclusion and exclusion criteria. Results: Initial search returned n = 13,408. After activating filters, n = 140 were identified of which n = 12 included for literature review.

Conclusions

Rise or drop in the concentration of a single marker is not accurate enough for predicting viral/bacterial community acquired pneumonia. This is because there is overlapping to a varying extent depending on the marker cut-off values, detection methods, analyses, the desired specificity, and sensitivity. Furthermore, the presence of mixed infection makes almost all markers suboptimal to be used universally. New markers such as MxA1 and HMGB1 gave promising results. However, to replicate a similar testing condition in a clinical environment may not be practical. Another approach is to make use of more than one marker and combine with clinical signs and symptoms. This may not be cost-effective in many clinical settings; nevertheless, in many studies, marker combination greatly improved the predictive power.

Challenges in the diagnosis of paediatric pneumonia in intervention field trials: recommendations from a pneumonia field trial working group

Pneumonia is a leading killer of children younger than 5 years despite high vaccination coverage, improved nutrition, and widespread implementation of the Integrated Management of Childhood Illnesses algorithm. Assessing the effect of interventions on childhood pneumonia is challenging because the choice of case definition and surveillance approach can affect the identification of pneumonia substantially. In anticipation of an intervention trial aimed to reduce childhood pneumonia by lowering household air pollution, we created a working group to provide recommendations regarding study design and implementation. We suggest to, first, select a standard case definition that combines acute (≤14 days) respiratory symptoms and signs and general danger signs with ancillary tests (such as chest imaging and pulse oximetry) to improve pneumonia identification; second, to prioritise active hospital-based pneumonia surveillance over passive case finding or home-based surveillance to reduce the risk of non-differential misclassification of pneumonia and, as a result, a reduced effect size in a randomised trial; and, lastly, to consider longitudinal follow-up of children younger than 1 year, as this age group has the highest incidence of severe pneumonia.

Biomarkers of Community-Acquired Pneumonia: A Key to Disease Diagnosis and Management

Community-acquired pneumonia (CAP) is a dangerous disease caused by a spectrum of bacterial and viral pathogens. The choice of specific therapy and the need for hospitalization or transfer to the intensive care unit are determined by the causative agent and disease severity. The microbiological analysis of sputum largely depends on the quality of the material obtained. The prediction of severity and the duration of therapy are determined individually, and existing prognostic scales are used generally. This review examines the possibilities of using specific serological biomarkers to detect the bacterial or viral aetiology of CAP and to assess disease severity. Particular emphasis is placed on the use of biomarker signatures and the discovery of biomarker candidates for a single multiplex analysis.

A biomarker approach to syndrome-based treatment of severe childhood illness in malaria-endemic areas

This opinion article deals with the diagnostic clinical challenges faced by clinicians or health care workers in malaria-endemic areas when a severely sick child presents to the clinic with fever, coma or respiratory distress. Indeed, the coexistence of malaria with other severe infections like meningitis, invasive bacterial infection or pneumonia makes appropriate treatment allocation a matter of life and death. The use of biomarkers has been proposed as a potential solution to this problem. The arrival of high-throughput technologies allowed thousands of molecules (transcripts, proteins and metabolites) to be been screened in clinical samples from large cohorts of well/characterised patients. The major aim of these studies was to identify biomarkers that inform important decisions: should this child be referred to hospital? Should antibiotics, anti-malarials, or both, be administered? There is a large discrepancy between the number of biomarker discovery studies published and the number of biomarkers that have been clinically validated, let alone implemented. This article reflects on the many opportunities and obstacles encountered in biomarker research in malaria-endemic areas.

Integrated fever management: disease severity markers to triage children with malaria and non-malarial febrile illness

Febrile symptoms in children are a leading cause of health-care seeking behaviour worldwide. The majority of febrile illnesses are uncomplicated and self-limited, without the need for referral or hospital admission. However, current diagnostic tools are unable to identify which febrile children have self-limited infection and which children are at risk of progressing to life-threatening infections, such as severe malaria. This paper describes the need for a simple community-based tool that can improve the early recognition and triage of febrile children, with either malarial or non-malarial illness, at risk of critical illness. The integration of a disease severity marker into existing malaria rapid diagnostic tests (RDT) could enable detection of children at risk of severe infection in the hospital and community, irrespective of aetiology. Incorporation of a disease severity marker could inform individualized management and early triage of children at risk of life-threatening infection. A child positive for both malaria and a disease severity marker could be prioritized for urgent referral/admission and parenteral therapy. A child positive for malaria and negative for a disease severity marker could be managed conservatively, as an out-patient, with oral anti-malarial therapy. An RDT with a disease severity marker could facilitate an integrated community-based approach to fever syndromes and improve early recognition, risk stratification, and prompt treatment of severe malaria and other life-threatening infections.

Survey of plasma proteins in children with progeria pre-therapy and on-therapy with lonafarnib

BackgroundHutchinson-Gilford progeria syndrome (HGPS) is an ultra-rare, fatal, segmental premature aging syndrome caused by the aberrant lamin A protein, progerin. The protein farnesyltransferase inhibitor, lonafarnib, ameliorates some aspects of cardiovascular and bone disease.MethodsWe performed a prospective longitudinal survey of plasma proteins in 24 children with HGPS (an estimated 10% of the world's population at the time) at baseline and on lonafarnib therapy, compared with age- and gender-matched controls using a multi-analyte, microsphere-based immunofluorescent assay.ResultsThe mean levels for 23/66 (34.8%) proteins were significantly lower and 7/66 (10.6%) were significantly higher in HGPS samples compared with those in controls (P≤0.05). Six proteins whose concentrations were initially lower normalized with lonafarnib therapy: interleukins 1α, 7, and 13, beta-2 microglobulin, C-reactive protein, and myoglobin. Alpha-2 macroglobulin, a protease inhibitor associated with stroke, was elevated at baseline and subsequently normalized with lonafarnib therapy.ConclusionThis is the first study to employ a multi-analyte array platform in HGPS. Novel potential biomarkers identified in this study should be further validated by correlations with clinical disease status, especially proteins associated with cardiovascular disease and those that normalized with lonafarnib therapy.

Interleukin-10 and soluble tumor necrosis factor receptor II are potential biomarkers of Plasmodium falciparum infections in pregnant women: a case-control study from Nanoro, Burkina Faso

Background

Diagnosis of malaria in pregnancy is problematic due to the low sensitivity of conventional diagnostic tests (rapid diagnostic test and microscopy), which is exacerbated due to low peripheral parasite densities, and lack of clinical symptoms. In this study, six potential biomarkers to support malaria diagnosis in pregnancy were evaluated.

Methods

Blood samples were collected from pregnant women at antenatal clinic visits and at delivery. Microscopy and real-time PCR were performed for malaria diagnosis and biomarker analyses were performed by ELISA (interleukin 10, IL-10; tumor necrosis factor-α, TNF-α; soluble tumor necrosis factor receptor II, sTNF-RII; soluble fms-like tyrosine kinase 1, sFlt-1; leptin and apolipoprotein B, Apo-B). A placental biopsy was collected at delivery to determine placental malaria.

Results

IL-10 and sTNF-RII were significantly higher at all time-points in malaria-infected women (p < 0.001). Both markers were also positively associated with parasite density (p < 0.001 and p = 0.003 for IL-10 and sTNF-RII respectively). IL-10 levels at delivery, but not during pregnancy, were negatively associated with birth weight. A prediction model was created using IL-10 and sTNF-RII cut-off points. For primigravidae the model had a sensitivity of 88.9% (95%CI 45.7–98.7%) and specificity of 83.3% (95% CI 57.1–94.9%) for diagnosing malaria during pregnancy. For secundi- and multigravidae the sensitivity (81.8% and 56.5% respectively) was lower, while specificity (100.0% and 94.3% respectively) was relatively high. Sub-microscopic infections were detected in 2 out of 3 secundi- and 5 out of 12 multigravidae.

Conclusions

The combination of biomarkers IL-10 and sTNF-RII have the potential to support malaria diagnosis in pregnancy. Additional markers may be needed to increase sensitivity and specificity, this is of particular importance in populations with sub-microscopic infections or in whom other inflammatory diseases are prevalent.

Electronic supplementary material

The online version of this article (10.1186/s40364-017-0114-7) contains supplementary material, which is available to authorized users.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

drLumi: An open-source package to manage data, calibrate, and conduct quality control of multiplex bead-based immunoassays data analysis

Multiplex bead-based immunoassays are used to measure concentrations of several analytes simultaneously. These assays include control standard curves (SC) to reduce between-plate variability and normalize quantitation of analytes of biological samples. Suboptimal calibration might result in large random error and decreased number of samples with analyte concentrations within the limits of quantification. Suboptimal calibration may be a consequence of poor fitness of the functions used for the SC, the treatment of the background noise and the method used to estimate the limits of quantification. Currently assessment of fitness of curves is largely dependent on operator and that may add additional error. Moreover, there is no software to automate data managing and quality control. In this article we present a R package, drLumi, with functions for managing data, calibrating assays and performing quality control. To optimize the assay the package implements: i) three dose-response functions, ii) four approaches for treating background noise and iii) three methods for estimating limits of quantifications. Other implemented functions are focused on the quality control of the fitted standard curve: detection of outliers, estimation of the confidence or prediction interval, and estimation of summary statistics. With demonstration purpose, we apply the software to 30 cytokines, chemokines and growth factors measured in a multiplex bead-based immunoassay in a study aiming to measure correlates of risk or protection from malaria of the RTS,S malaria vaccine nested in the Phase 3 randomized controlled trial of this vaccine.

New Biomarkers and Diagnostic Tools for the Management of Fever in Low- and Middle-Income Countries: An Overview of the Challenges

A lack of simple, inexpensive, and rapid diagnostic tests for febrile illnesses other than malaria leads to overtreatment with antibiotics for those who test negative for malaria, and contributes to the global rise in antimicrobial resistance. New tests for the detection of host biomarkers provide promising tools to differentiate bacterial from non-bacterial infections in febrile patients. However, most available biomarker tests are not currently used in resource-limited settings, and very few evaluations have been performed in low- and middle-income country populations with non-severe febrile illness. As a result, our knowledge of the performance of these tests in settings with high prevalence of infectious and poverty-related diseases such as malaria, HIV, malnutrition and intestinal parasites is poor. This paper describes challenges faced during the process of getting to an approved test, including difficulties in selecting the most appropriate fever biomarkers; suitable study designs and sites for test evaluations; lack of available reference tests to evaluate the performance of new tests; and lack of clear regulatory pathways to introduce such tests. As many new biomarker assays are in development, understanding these challenges will better enable those working in this area to address them during product development.

Transcriptional Categorization of the Etiology of Pneumonia Syndrome in Pediatric Patients in Malaria-Endemic Areas

Background

Pediatric acute respiratory distress in tropical settings is very common. Bacterial pneumonia is a major contributor to morbidity and mortality rates and requires adequate diagnosis for correct treatment. A rapid test that could identify bacterial (vs other) infections would have great clinical utility.

Methods and Results

We performed RNA (RNA-seq) sequencing and analyzed the transcriptomes of 68 pediatric patients with well-characterized clinical phenotype to identify transcriptional features associated with each disease class. We refined the features to predictive models (support vector machine, elastic net) and validated those models in an independent test set of 37 patients (80%-85% accuracy).

Conclusions

We have identified sets of genes that are differentially expressed in pediatric patients with pneumonia syndrome attributable to different infections and requiring different therapeutic interventions. Findings of this study demonstrate that human transcription signatures in infected patients recapitulate the underlying biology and provide models for predicting a bacterial diagnosis to inform treatment.

Tying malaria and microRNAs: from the biology to future diagnostic perspectives

Symptoms caused by bacterial, viral and malarial infections usually overlap and aetiologic diagnosis is difficult. Patient management in low-resource countries with limited laboratory services has been based predominantly on clinical evaluation and syndromic approaches. However, such clinical assessment has limited accuracy both for identifying the likely aetiological cause and for the early recognition of patients who will progress to serious or fatal disease. Plasma-detectable biomarkers that rapidly and accurately diagnose severe infectious diseases could reduce morbidity and decrease the unnecessary use of usually scarce therapeutic drugs. The discovery of microRNAs (miRNAs) has opened exciting new avenues to identify blood biomarkers of organ-specific injury. This review assesses current knowledge on the relationship between malaria disease and miRNAs, and evaluates how future research might lead to the use of these small molecules for identifying patients with severe malaria disease and facilitate treatment decisions.