Association of disease severity and genetic variation during primary Respiratory Syncytial Virus infections.

BACKGROUND: Respiratory Syncytial Virus (RSV) disease in young children ranges from mild cold symptoms to severe symptoms that require hospitalization and sometimes result in death. Studies have shown a statistical association between RSV subtype or phylogenic lineage and RSV disease severity, although these results have been inconsistent. Associations between variation within RSV gene coding regions or residues and RSV disease severity has been largely unexplored. METHODS: Nasal swabs from children (< 8 months-old) infected with RSV in Rochester, NY between 1977-1998 clinically presenting with either mild or severe disease during their first cold-season were used. Whole-genome RSV sequences were obtained using overlapping PCR and next-generation sequencing. Both whole-genome phylogenetic and non-phylogenetic statistical approaches were performed to associate RSV genotype with disease severity. RESULTS: The RSVB subtype was statistically associated with disease severity. A significant association between phylogenetic clustering of mild/severe traits and disease severity was also found. GA1 clade sequences were associated with severe disease while GB1 was significantly associated with mild disease. Both G and M2-2 gene variation was significantly associated with disease severity. We identified 16 residues in the G gene and 3 in the M2-2 RSV gene associated with disease severity. CONCLUSION: These results suggest that phylogenetic lineage and the genetic variability in G or M2-2 genes of RSV may contribute to disease severity in young children undergoing their first infection.

Palivizumab Prophylaxis in Infants and Young Children at Increased Risk of Hospitalization for Respiratory Syncytial Virus Infection.

Guidance from the American Academy of Pediatrics (AAP) for the use of palivizumab prophylaxis against respiratory syncytial virus (RSV) was first published in a policy statement in 1998. AAP recommendations have been updated periodically to reflect the most recent literature regarding children at greatest risk of severe RSV disease. Since the last update in 2014, which refined prophylaxis guidance to focus on those children at greatest risk, data have become available regarding the seasonality of RSV circulation, the incidence and risk factors associated with bronchiolitis hospitalizations, and the potential effects of the implementation of prophylaxis recommendations on hospitalization rates of children with RSV infection. This technical report summarizes the literature review by the Committee on Infectious Diseases, supporting the reaffirmation of the 2014 AAP policy statement on palivizumab prophylaxis among infants and young children at increased risk of hospitalization for RSV infection. Review of publications since 2014 did not support a change in recommendations for palivizumab prophylaxis and continues to endorse the guidance provided in the 2021 Red Book.

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

BACKGROUND: Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings. OBJECTIVE: The aim of this study was to derive a clinical-based IBI predictive model for febrile infants. DESIGNS, SETTING, AND PARTICIPANTS: This is a cross-sectional study of infants brought to two pediatric emergency departments from January 2011 to December 2018. Inclusion criteria were age 0-90 days, temperature ≥38°C, and documented gestational age, fever duration, and illness duration. MAIN OUTCOME AND MEASURES: To detect IBIs, we used regression and ensemble machine learning models and evidence-based predictors (i.e., sex, age, chronic medical condition, gestational age, appearance, maximum temperature, fever duration, illness duration, cough status, and urinary tract inflammation). We up-weighted infants with IBIs 8-fold and used 10-fold cross-validation to avoid overfitting. We calculated the area under the receiver operating characteristic curve (AUC), prioritizing a high sensitivity to identify the optimal cut-point to estimate sensitivity and specificity. RESULTS: Of 2311 febrile infants, 39 had an IBI (1.7%); the median age was 54 days (interquartile range: 35-71). The AUC was 0.819 (95% confidence interval: 0.762, 0.868). The predictive model achieved a sensitivity of 0.974 (0.800, 1.00) and a specificity of 0.530 (0.484, 0.575). Findings suggest that a clinical-based model can detect IBIs in febrile infants, performing similarly to serum biomarker-based models. This model may improve health equity by enabling clinicians to estimate IBI risk in any setting. Future studies should prospectively validate findings across multiple sites and investigate performance by age.

Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants.

BACKGROUND AND OBJECTIVE: For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models. METHODS: This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities. RESULTS: Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%). CONCLUSIONS: Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

Aberrant newborn T cell and microbiota developmental trajectories predict respiratory compromise during infancy.

Neonatal immune-microbiota co-development is poorly understood, yet age-appropriate recognition of - and response to - pathogens and commensal microbiota is critical to health. In this longitudinal study of 148 preterm and 119 full-term infants from birth through one year of age, we found that postmenstrual age or weeks from conception is a central factor influencing T cell and mucosal microbiota development. Numerous features of the T cell and microbiota functional development remain unexplained; however, by either age metric and are instead shaped by discrete perinatal and postnatal events. Most strikingly, we establish that prenatal antibiotics or infection disrupt the normal T cell population developmental trajectory, influencing subsequent respiratory microbial colonization and predicting respiratory morbidity. In this way, early exposures predict the postnatal immune-microbiota axis trajectory, placing infants at later risk for respiratory morbidity in early childhood.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.

BACKGROUND: A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. METHOD: We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). RESULTS: NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. CONCLUSION: Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants.

OBJECTIVE: To develop a novel predictive model using primarily clinical history factors and compare performance to the widely used Rochester Low Risk (RLR) model. STUDY DESIGN: In this cross-sectional study, we identified infants brought to one pediatric emergency department from January 2014 to December 2016. We included infants age 0-90 days, with temperature ≥38°C, and documented gestational age and illness duration. The primary outcome was bacterial infection. We used 10 predictors to develop regression and ensemble machine learning models, which we trained and tested using 10-fold cross-validation. We compared areas under the curve (AUCs), sensitivities, and specificities of the RLR, regression, and ensemble models. RESULTS: Of 877 infants, 67 had a bacterial infection (7.6%). The AUCs of the RLR, regression, and ensemble models were 0.776 (95% CI 0.746, 0.807), 0.945 (0.913, 0.977), and 0.956 (0.935, 0.975), respectively. Using a bacterial infection risk threshold of .01, the sensitivity and specificity of the regression model was 94.6% (87.4%, 100%) and 74.5% (62.4%, 85.4%), compared with 95.5% (87.5%, 99.1%) and 59.6% (56.2%, 63.0%) using the RLR model. CONCLUSIONS: Compared with the RLR model, sensitivities of the novel predictive models were similar whereas AUCs and specificities were significantly greater. If externally validated, these models, by producing an individualized bacterial infection risk estimate, may offer a targeted approach to young febrile infants that is noninvasive and inexpensive.

A Multiplex Microsphere IgG Assay for SARS-CoV-2 Using ACE2-Mediated Inhibition as a Surrogate for Neutralization.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the challenges inherent to the serological detection of a novel pathogen such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Serological tests can be used diagnostically and for surveillance, but their usefulness depends on their throughput, sensitivity, and specificity. Here, we describe a multiplex fluorescent microsphere-based assay, 3Flex, that can detect antibodies to three major SARS-CoV-2 antigens-spike (S) protein, the spike ACE2 receptor-binding domain (RBD), and nucleocapsid (NP). Specificity was assessed using 213 prepandemic samples. Sensitivity was measured and compared to that of the Abbott Architect SARS-CoV-2 IgG assay using serum samples from 125 unique patients equally binned (n = 25) into 5 time intervals (≤5, 6 to 10, 11 to 15, 16 to 20, and ≥21 days from symptom onset). With samples obtained at ≤5 days from symptom onset, the 3Flex assay was more sensitive (48.0% versus 32.0%), but the two assays performed comparably using serum obtained ≥21 days from symptom onset. A larger collection (n = 534) of discarded sera was profiled from patients (n = 140) whose COVID-19 course was characterized through chart review. This revealed the relative rise, peak (S, 23.8; RBD, 23.6; NP, 16.7 [in days from symptom onset]), and decline of the antibody response. Considerable interperson variation was observed with a subset of extensively sampled intensive care unit (ICU) patients. Using soluble ACE2, inhibition of antibody binding was demonstrated for S and RBD, and not for NP. Taking the data together, this study described the performance of an assay built on a flexible and high-throughput serological platform that proved adaptable to the emergence of a novel infectious agent.

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown. METHODS: We carried out 16S rRNA microbiota profiling of infants in the first year of life from (1) a cross-sectional cohort of 89 RSV-infected infants sampled during illness and 102 matched healthy controls, and (2) a matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled before, during, and after infection. RESULTS: We identified 12 taxa significantly associated with RSV infection. All 12 taxa were differentially abundant during infection, with 8 associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs infected than of disease severity. CONCLUSIONS: Our findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and impact of RSV infection on microbiota composition.

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. METHODS: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. RESULTS: We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. CONCLUSIONS: Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

Environmental Lead Exposure and Influenza and Respiratory Syncytial Virus Diagnoses in Young Children: A Test-Negative Case-Control Study.

Experimental and epidemiological evidence suggests that environmental toxicants may influence susceptibility to influenza and respiratory syncytial virus (RSV). The objective of the present study was to estimate the association between blood lead concentrations and the odds of child influenza or RSV infection. A test-negative, case-control study was conducted among 617 children, <4 years of age, tested for influenza/RSV from 2012-2017 in Rochester, NY. There were 49 influenza cases (568 controls) and 123 RSV cases (494 controls). Blood lead concentrations reported in children's medical records were linked with influenza/RSV lab test results. Covariables were collected from medical records, birth certificates, and U.S. census data. In this sample, evidence of an association between blood lead levels and RSV or influenza diagnosis was not observed. Children with a lead level ≥1 µg/dL vs. <1 µg/dL had an adjusted odds ratio (aOR) and 95% confidence limit of 0.95 (0.60, 1.49) for RSV and 1.34 (0.65, 2.75) for influenza. In sex-specific analyses, boys with lead concentrations ≥1 µg/dL vs. <1 µg/dL had an aOR = 1.89 (1.25, 2.86) for influenza diagnosis, while the estimates were inconsistent for girls. These results are suggestive of sex-specific associations between blood lead levels and the risk of influenza, although the sample size was small.

A Quality Improvement Initiative To Optimize Antibiotic Use in a Level 4 NICU.

BACKGROUND: Given the risks associated with antibiotics, efforts to reduce unnecessary antibiotic use in the NICU have become increasingly urgent. In 2016, a comprehensive 3-year quality improvement (QI) initiative was conducted in a level 4 NICU that sought to decrease the antibiotic use rate (AUR) by 20%. METHODS: This local QI initiative was conducted in the context of a multicenter learning collaborative focused on decreasing unnecessary antibiotic use. Improvement strategies focused on addressing gaps in the core elements of antibiotic stewardship programs. Outcome measures included the AUR and the percent of infants discharged without antibiotic exposure. Process measures included the percent of infants evaluated for early-onset sepsis (EOS) and duration of antibiotics used for various infections. Statistical process control charts were used to display and analyze data over time. RESULTS: The AUR decreased from 27.6% at baseline to 15.5%, a 43% reduction, and has been sustained for >18 months. Changes most attributable to this decrease include implementation of the sepsis risk calculator, adopting a 36-hour rule-out period for sepsis evaluations, a 36-hour antibiotic hard stop, and novel guideline for EOS evaluation among infants <35 weeks. The percent of infants discharged without antibiotic exposure increased from 15.8% to 35.1%. The percent of infants ≥36 weeks undergoing evaluation for EOS decreased by 42.3% and for those <35 weeks by 26%. CONCLUSIONS: Our efforts significantly reduced antibiotic use and exposure in our NICU. Our comprehensive, rigorous approach to QI is applicable to teams focused on improvement.

Unbiased analysis of peripheral blood mononuclear cells reveals CD4 T cell response to RSV matrix protein.

Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000-126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children. Flow cytometry analysis of cellular responses using "conventional" anti-viral markers (IL2, IFN-γ, TNF, IL10 and IL4) upon RSV-peptide stimulation detected an overall low RSV response in peripheral blood. Therefore we sought an unbiased approach to identify RSV-specific immune markers using RNA-sequencing upon stimulation of infant PBMCs with overlapping peptides representing RSV antigens. To understand the cellular response using transcriptional signatures, transcription factors and cell-type specific signatures were used to investigate breadth of response across peptides. Unexpected from the ICS data, M peptide induced a response equivalent to the F-peptide and was characterized by activation of GATA2, 3, STAT3 and IRF1. This along with upregulation of several unconventional T cell signatures was only observed upon M-peptide stimulation. Moreover, signatures of natural RSV infections were identified from the data available in the public domain to investigate similarities between transcriptional signatures from PBMCs and upon peptide stimulation. This analysis also suggested activation of T cell response upon M-peptide stimulation. Hence, based on transcriptional response, markers were chosen to validate the role of M-peptide in activation of T cells. Indeed, CD4+CXCL9+ cells were identified upon M-peptide stimulation by flow cytometry. Future work using additional markers identified in this study could reveal additional unconventional T cells responding to RSV infections in infants. In conclusion, T cell responses to RSV in infants may not follow the canonical Th1/Th2 patterns of effector responses but include additional functions that may be unique to the neonatal period and correlate with clinical outcomes.

Eight practices for data management to enable team data science.

INTRODUCTION: In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data. METHODS: We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management. RESULTS: In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects. CONCLUSIONS: We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Early childhood risk exposures and inflammation in early adolescence.

There is now reliable evidence that early psychosocial stress exposures are associated with behavioral health in children; the degree to which these same kinds of stress exposures predict physical health outcomes is not yet clear. We investigated the links between economic adversity, family and caregiving stress in early childhood and several markers of immune function in early adolescence. The sample is derived from the Family Life Project, a prospective longitudinal study of at-risk families. Socio-demographic and psychosocial risks have been assessed at regular intervals since the children were first assessed at 2 months of age. When the children were early adolescents, we conducted an in-depth health assessment of a subsample of families; blood samples were collected from venipuncture for interleukin(IL)-6, Tumor Necrosis Factor (TNF)-alpha, and C-reactive protein (CRP), as well as glucocorticoid resistance. Results indicated limited but reliable evidence of an association between early risk exposure and inflammation in adolescence. Specifically, caregiver depressive symptoms in early childhood predicted elevated CRP almost a decade later, and the prediction was significant after accounting for multiple covariates such as socio-economic adversity, health behaviors and body mass index. Our findings provide strong but limited evidence that early stress exposures may be associated with inflammation, suggesting one mechanism linking early stress exposure to compromised behavioral and somatic health.

Microbiome-Transcriptome Interactions Related to Severity of Respiratory Syncytial Virus Infection.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections and hospital visits during infancy and childhood. Although risk factors for RSV infection have been identified, the role of microbial species in the respiratory tract is only partially known. We aimed to understand the impact of interactions between the nasal microbiome and host transcriptome on the severity and clinical outcomes of RSV infection. We used 16 S rRNA sequencing to characterize the nasal microbiome of infants with RSV infection. We used RNA sequencing to interrogate the transcriptome of CD4+ T cells obtained from the same set of infants. After dimension reduction through principal component (PC) analysis, we performed an integrative analysis to identify significant co-variation between microbial clade and gene expression PCs. We then employed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) to estimate the clade-gene association patterns for each infant. Our network-based integrative analysis identified several clade-gene associations significantly related to the severity of RSV infection. The microbial taxa with the highest loadings in the implicated clade PCs included Moraxella, Corynebacterium, Streptococcus, Haemophilus influenzae, and Staphylococcus. Interestingly, many of the genes with the highest loadings in the implicated gene PCs are encoded in mitochondrial DNA, while others are involved in the host immune response. This study on microbiome-transcriptome interactions provides insights into how the host immune system mounts a response against RSV and specific infectious agents in nasal microbiota.

Measuring the Severity of Respiratory Illness in the First 2 Years of Life in Preterm and Term Infants.

OBJECTIVE: To develop a valid research tool to measure infant respiratory illness severity using parent-reported symptoms. STUDY DESIGN: Nose and throat swabs were collected monthly for 1 year and during respiratory illnesses for 2 years in a prospective study of term and preterm infants in the Prematurity, Respiratory Outcomes, Immune System and Microbiome study. Viral pathogens were detected using Taqman Array Cards. Parents recorded symptoms during respiratory illnesses using a Childhood Origins of Asthma (COAST) scorecard. The COAST score was validated using linear mixed effects regression modeling to evaluate associations with hospitalization and specific infections. A data-driven method was also used to compute symptom weights and derive a new score, the Infant Research Respiratory Infection Severity Score (IRRISS). Linear mixed effects regression modeling was repeated with the IRRISS illness data. RESULTS: From April 2013 to April 2017, 50 term, 40 late preterm, and 28 extremely low gestational age (<29 weeks of gestation) infants had 303 respiratory illness visits with viral testing and parent-reported symptoms. A range of illness severity was described with 39% of illness scores suggestive of severe disease. Both the COAST score and IRRISS were associated with respiratory syncytial virus infection and hospitalization. Gestational age and human rhinovirus infection were inversely associated with both scoring systems. The IRRISS and COAST scores were highly correlated (r = 0.93; P < .0001). CONCLUSIONS: Using parent-reported symptoms, we validated the COAST score as a measure of respiratory illness severity in infants. The new IRRISS score performed as well as the COAST score.