Antenatal Determinants of Bronchopulmonary Dysplasia and Late Respiratory Disease in Preterm Infants

RATIONALE: Mechanisms contributing to chronic lung disease after preterm birth are incompletely understood. OBJECTIVES: To identify antenatal risk factors associated with increased risk for bronchopulmonary dysplasia (BPD) and respiratory disease during early childhood after preterm birth, we performed a prospective, longitudinal study of 587 preterm infants with gestational age less than 34 weeks and birth weights between 500 and 1,250 g. METHODS: Data collected included perinatal information and assessments during the neonatal intensive care unit admission and longitudinal follow-up by questionnaire until 2 years of age. MEASUREMENTS AND MAIN RESULTS: After adjusting for covariates, we found that maternal smoking prior to preterm birth increased the odds of having an infant with BPD by twofold (P = 0.02). Maternal smoking was associated with prolonged mechanical ventilation and respiratory support during the neonatal intensive care unit admission. Preexisting hypertension was associated with a twofold (P = 0.04) increase in odds for BPD. Lower gestational age and birth weight z-scores were associated with BPD. Preterm infants who were exposed to maternal smoking had higher rates of late respiratory disease during childhood. Twenty-two percent of infants diagnosed with BPD and 34% of preterm infants without BPD had no clinical signs of late respiratory disease during early childhood. CONCLUSIONS: We conclude that maternal smoking and hypertension increase the odds for developing BPD after preterm birth, and that maternal smoking is strongly associated with increased odds for late respiratory morbidities during early childhood. These findings suggest that in addition to the BPD diagnosis at 36 weeks, other factors modulate late respiratory outcomes during childhood. We speculate that measures to reduce maternal smoking not only will lower the risk for preterm birth but also will improve late respiratory morbidities after preterm birth

The Upper Respiratory Tract as a Microbial Source for Pulmonary Infections in Cystic Fibrosis. Parallels from Island Biogeography

A continuously mixed series of microbial communities inhabits various points of the respiratory tract, with community composition determined by distance from colonization sources, colonization rates, and extinction rates. Ecology and evolution theory developed in the context of biogeography is relevant to clinical microbiology and could reframe the interpretation of recent studies comparing communities from lung explant samples, sputum samples, and oropharyngeal swabs. We propose an island biogeography model of the microbial communities inhabiting different niches in human airways. Island biogeography as applied to communities separated by time and space is a useful parallel for exploring microbial colonization of healthy and diseased lungs, with the potential to inform our understanding of microbial community dynamics and the relevance of microbes detected in different sample types. In this perspective, we focus on the intermixed microbial communities inhabiting different regions of the airways of patients with cystic fibrosis

Oral antibiotic prescribing patterns for treatment of pulmonary exacerbations in two large pediatric CF centers

Introduction: Oral antibiotics are frequently prescribed for outpatient pulmonary exacerbations (PEx) in children with cystic fibrosis (CF). This study aimed to characterize oral antibiotic use for PEx and treatment outcomes at two large US CF centers. Methods: Retrospective, descriptive study of oral antibiotic prescribing practices among children with CF ages 6–17 years over 1 year. The care setting for antibiotic initiation (clinic or phone encounter) was determined and outcomes were compared. Results: A total of 763 oral antibiotic courses were prescribed to 312 patients aged 6–17 years (77% of 403 eligible patients) with a median of two courses per year (range: 1–10). Fifty‐eight percent of prescriptions were provided over the phone. Penicillin was the most commonly prescribed antibiotic class (36% of prescriptions) but differences in antibiotic class prescriptions were noted between the two centers. Hospitalizations occurred within 3 months following 19% of oral antibiotic courses. Forced expiratory volume in 1 s (FEV1) recovered to within 90% of prior baseline within 6 months in 87% of encounters; the mean (SD) % recovery was 99.6% (12.1%) of baseline. Outcomes did not differ between phone and clinic prescriptions. Conclusions: Phone prescriptions, commonly excluded in studies of PEx, made up more than half of all oral antibiotic courses. Heterogeneity in prescribing patterns was observed between the two centers. Most patients had improvement in FEV1 returning to near their prior baseline, but hospitalizations occurred in one‐fifth following oral antibiotic treatment. Efforts to optimize PEx treatment must consider care that occurs over the phone; this is particularly important as the use of telemedicine increases.This work was supported by the Cystic Fibrosis Foundation (SANDERS18A1, HOPPE16A0)

Application of Two-Part Statistics for Comparison of Sequence Variant Counts

Investigation of microbial communities, particularly human associated communities, is significantly enhanced by the vast amounts of sequence data produced by high throughput sequencing technologies. However, these data create high-dimensional complex data sets that consist of a large proportion of zeros, non-negative skewed counts, and frequently, limited number of samples. These features distinguish sequence data from other forms of high-dimensional data, and are not adequately addressed by statistical approaches in common use. Ultimately, medical studies may identify targeted interventions or treatments, but lack of analytic tools for feature selection and identification of taxa responsible for differences between groups, is hindering advancement. The objective of this paper is to examine the application of a two-part statistic to identify taxa that differ between two groups. The advantages of the two-part statistic over common statistical tests applied to sequence count datasets are discussed. Results from the t-test, the Wilcoxon test, and the two-part test are compared using sequence counts from microbial ecology studies in cystic fibrosis and from cenote samples. We show superior performance of the two-part statistic for analysis of sequence data. The improved performance in microbial ecology studies was independent of study type and sequence technology used

Reliability of Quantitative Real-Time PCR for Bacterial Detection in Cystic Fibrosis Airway Specimens

The cystic fibrosis (CF) airway microbiome is complex; polymicrobial infections are common, and the presence of fastidious bacteria including anaerobes make culture-based diagnosis challenging. Quantitative real-time PCR (qPCR) offers a culture-independent method for bacterial quantification that may improve diagnosis of CF airway infections; however, the reliability of qPCR applied to CF airway specimens is unknown. We sought to determine the reliability of nine specific bacterial qPCR assays (total bacteria, three typical CF pathogens, and five anaerobes) applied to CF airway specimens. Airway and salivary specimens from clinically stable pediatric CF subjects were collected. Quantitative PCR assay repeatability was determined using triplicate reactions. Split-sample measurements were performed to measure variability introduced by DNA extraction. Results from qPCR were compared to standard microbial culture for Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, common pathogens in CF. We obtained 84 sputa, 47 oropharyngeal and 27 salivary specimens from 16 pediatric subjects with CF. Quantitative PCR detected bacterial DNA in over 97% of specimens. All qPCR assays were highly reproducible at quantities ≥102 rRNA gene copies/reaction with coefficient of variation less than 20% for over 99% of samples. There was also excellent agreement between samples processed in duplicate. Anaerobic bacteria were highly prevalent and were detected in mean quantities similar to that of typical CF pathogens. Compared to a composite gold standard, qPCR and culture had variable sensitivities for detection of P. aeruginosa, S. aureus and H. influenzae from CF airway samples. By reliably quantifying fastidious airway bacteria, qPCR may improve our understanding of polymicrobial CF lung infections, progression of lung disease and ultimately improve antimicrobial treatments

Bacterial Signatures of Paediatric Respiratory Disease : An Individual Participant Data Meta-Analysis

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.Peer reviewe

Evaluation of estimation quality of a general paradigm for indexing animal abundance when observations are counts

Relative abundance indices are widely applied to monitor wildlife populations. A general indexing paradigm was developed for structuring data collection and validly conducting analyses. This approach is applicable for many observation metrics, with observations made at stations through the area of interest and repeated over several days. The variance formula for the general index was derived using a linear mixed model, with statistical tests and confidence intervals constructed assuming Gaussian-distributed observations. However, many observation methods, like intrusions to track plots or camera traps, involve counts with many zeroes, producing Poisson-like observations. To fill this inferential gap between Gaussian analytical assumptions and Poisson-distributed data we evaluated, via a broad Monte Carlo simulation study, variance estimation and confidence interval coverage when Gaussian statistical inference is applied to data generated from a Poisson distribution. The mixed effects linear model assuming Gaussian observations performed well in estimating variances and confidence intervals when simulated Poisson data were in the range found in field studies (88–96% confidence interval coverage). Estimation improved by increasing the number of observation days. Confidence interval coverage rates performed very well (even with few observation days) when day-to-day variability was small, while effective estimation resulted for a great range in station-to-station variability. These results provide a foundational basis for applying the general indexing paradigm to count data, strengthen the generality of the approach, provide valuable information for study design, and should reassure practitioners about the validity of their analytical inferences when using count data

Permutation-based methods for mediation analysis in studies with small sample sizes

Background Mediation analysis can be used to evaluate the effect of an exposure on an outcome acting through an intermediate variable or mediator. For studies with small sample sizes, permutation testing may be useful in evaluating the indirect effect (i.e., the effect of exposure on the outcome through the mediator) while maintaining the appropriate type I error rate. For mediation analysis in studies with small sample sizes, existing permutation testing methods permute the residuals under the full or alternative model, but have not been evaluated under situations where covariates are included. In this article, we consider and evaluate two additional permutation approaches for testing the indirect effect in mediation analysis based on permutating the residuals under the reduced or null model which allows for the inclusion of covariates. Methods Simulation studies were used to empirically evaluate the behavior of these two additional approaches: (1) the permutation test of the Indirect Effect under Reduced Models (IERM) and (2) the Permutation Supremum test under Reduced Models (PSRM). The performance of these methods was compared to the standard permutation approach for mediation analysis, the permutation test of the Indirect Effect under Full Models (IEFM). We evaluated the type 1 error rates and power of these methods in the presence of covariates since mediation analysis assumes no unmeasured confounders of the exposure–mediator–outcome relationships. Results The proposed PSRM approach maintained type I error rates below nominal levels under all conditions, while the proposed IERM approach exhibited grossly inflated type I rates in many conditions and the standard IEFM exhibited inflated type I error rates under a small number of conditions. Power did not differ substantially between the proposed PSRM approach and the standard IEFM approach. Conclusions The proposed PSRM approach is recommended over the existing IEFM approach for mediation analysis in studies with small sample sizes