ABSTRACT
BACKGROUND: Diagnosis of bacterial meningitis (BM) is challenging in newborn infants. Presently, biomarkers of BM have limited diagnostic accuracy. Analysis of cerebrospinal fluid (CSF) metabolites may be a useful diagnostic tool in BM. METHODS: In a nested case-control study, we examined >400 metabolites in CSF of uninfected infants and infants with culture-confirmed BM using gas and liquid chromatography mass spectrometry. Preterm and full-term infants in a Level III or IV Neonatal Intensive Care Unit were prospectively enrolled when evaluated for serious bacterial infection. RESULTS: Over 200 CSF metabolites significantly differed in uninfected infants and infants with BM. Using machine learning, we found that as few as 6 metabolites distinguished infants with BM from uninfected infants in this pilot cohort. Further analysis demonstrated three metabolites associated with Group B Streptococcal meningitis. CONCLUSIONS: We report the first comprehensive metabolic analysis of CSF in infants with BM. In our pilot cohort, we derived a metabolic signature that predicted the presence or absence of BM, irrespective of gestational age, postnatal age, sex, race and ethnicity, presence of neurosurgical hardware, white blood cell count in CSF, and red blood cell contamination in CSF. Metabolic analysis may aid diagnosis of BM and facilitate clinical decision-making in infants. IMPACT: In a pilot cohort, metabolites in cerebrospinal fluid distinguished infants with bacterial meningitis from uninfected infants.We report the first comprehensive metabolic analysis of cerebrospinal fluid in infants with bacterial meningitis.Our findings may be used to improve diagnosis of bacterial meningitis and to offer mechanistic insights into the pathophysiology of bacterial meningitis in infants.
Subject(s)
Brain Injuries/microbiology , Meningitis, Bacterial/metabolism , Algorithms , Anti-Bacterial Agents/therapeutic use , Biomarkers/metabolism , Brain Injuries/complications , Case-Control Studies , Cerebrospinal Fluid/metabolism , Chromatography, Liquid , Decision Support Systems, Clinical , Erythrocyte Count , False Positive Reactions , Female , Gas Chromatography-Mass Spectrometry , Humans , Infant , Infant, Newborn , Infant, Premature , Intensive Care Units, Neonatal , Leukocyte Count , Machine Learning , Male , Meningitis, Bacterial/complications , Neurosurgery/methods , Prospective Studies , Sensitivity and Specificity , Streptococcal Infections/drug therapy , Streptococcus agalactiaeABSTRACT
OBJECTIVE: To determine if time to antibiotic administration is associated with mortality and in-hospital outcomes in a neonatal intensive care unit (NICU) population. STUDY DESIGN: We conducted a prospective evaluation of infants with suspected sepsis between September 2014 and February 2018; sepsis was defined as clinical concern prompting blood culture collection and antibiotic administration. Time to antibiotic administration was calculated from time of sepsis identification, defined as the order time of either blood culture or an antibiotic, to time of first antibiotic administration. We used linear models with generalized estimating equations to determine the association between time to antibiotic administration and mortality, ventilator-free and inotrope-free days, and NICU length of stay in patients with culture-proven sepsis. RESULTS: Among 1946 sepsis evaluations, we identified 128 episodes of culture-proven sepsis in 113 infants. Among them, prolonged time to antibiotic administration was associated with significantly increased risk of mortality at 14 days (OR, 1.47; 95% CI, 1.15-1.87) and 30 days (OR, 1.47; 95% CI, 1.11-1.94) as well as fewer inotrope-free days (incidence rate ratio, 0.91; 95% CI, 0.84-0.98). No significant associations with ventilator-free days or NICU length of stay were demonstrated. CONCLUSIONS: Among infants with sepsis, delayed time to antibiotic administration was an independent risk factor for death and prolonged cardiovascular dysfunction. Further study is needed to define optimal timing of antimicrobial administration in high-risk NICU populations.
Subject(s)
Anti-Bacterial Agents/administration & dosage , Sepsis/drug therapy , Sepsis/mortality , Comorbidity , Electronic Health Records , Female , Humans , Infant , Infant, Newborn , Intensive Care Units, Neonatal , Intensive Care, Neonatal , Length of Stay , Linear Models , Male , Multivariate Analysis , Probability , Prospective Studies , Risk Factors , Sepsis/microbiology , Time-to-Treatment , Treatment OutcomeABSTRACT
This article describes the process of extracting electronic health record (EHR) data into a format that supports analyses related to the timeliness of antibiotic administration. The de-identified data that accompanies this article were collected from a cohort of infants who were evaluated for possible sepsis in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia (CHOP). The interpretation of findings from these data are reported in a separate manuscript [1]. For purposes of illustration for interested readers, scripts written in the R programming language related to the creation and use of the dataset have also been provided. Interested researchers are encouraged to contact the research team to discuss opportunities for collaboration.
