<p><strong>OBJECTIVES: </strong>To determine incidence and severity of acute kidney injury (AKI) within 7 days of sepsis evaluation and to assess AKI duration and the association between AKI and 30-day mortality.</p>

<p><strong>STUDY DESIGN: </strong>Retrospective, matched cohort study in a single-center level IV NICU. Eligible infants underwent sepsis evaluations at ≥72 hours of age during calendar years 2013-2018. Exposed infants ("cases") were those with culture-proven sepsis and antimicrobial duration ≥5 days. Non-exposed infants ("controls") were matched 1:1 to exposed infants based on gestational and corrected gestational age, and had negative sepsis evaluations with antibiotic durations &lt;48 hours. AKI was defined by modified neonatal Kidney Disease Improving Global Outcomes criteria. Statistical analysis included Mann-Whitney and Chi-square tests, multivariable logistic regression, and Kaplan-Meier time-to-event analysis.</p>

<p><strong>RESULTS: </strong>Among 203 episodes of late-onset sepsis, 40 (20%) developed AKI within 7 days following evaluation, and among 193 episodes with negative cultures, 16 (8%) resulted in AKI (p=0.001). Episodes of sepsis also led to greater AKI severity, compared with non-septic episodes (P = .007). The timing of AKI onset and AKI duration did not differ between groups. Sepsis was associated with increased odds of developing AKI (aOR 3.0, 95% CI 1.5-6.2, p=0.002). AKI was associated with increased 30-day mortality (aOR 4.5, 95% CI 1.3-15.6, p=0.017).</p>

<p><strong>CONCLUSIONS: </strong>Infants with late-onset sepsis had increased odds of AKI and greater AKI severity within 7 days of sepsis evaluation, compared with age-matched infants without sepsis. AKI was independently associated with increased 30-day mortality. Strategies to mitigate AKI in critically ill neonates with sepsis may improve outcomes.</p>

<p><strong>BACKGROUND: </strong>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.</p>

<p><strong>METHODS: </strong>In a nested case-control study, we examined &gt;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.</p>

<p><strong>RESULTS: </strong>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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>IMPACT: </strong>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.</p>

<p>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.</p>

<p><strong>OBJECTIVE: </strong>To determine if time to antibiotic administration is associated with mortality and in-hospital outcomes in a neonatal intensive care unit (NICU) population.</p>

<p><strong>STUDY DESIGN: </strong>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.</p>

<p><strong>RESULTS: </strong>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&nbsp;days (OR, 1.47; 95% CI, 1.15-1.87) and 30&nbsp;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.</p>

<p><strong>CONCLUSIONS: </strong>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.</p>

<p><strong>BACKGROUND: </strong>Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition.</p>

<p><strong>METHODS AND FINDINGS: </strong>We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive-positive blood culture for a known pathogen (110 evaluations); and clinically positive-negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80-0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85-0.87, again with no significant differences.</p>

<p><strong>CONCLUSIONS: </strong>Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial.</p>