We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and AfricanAmericans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped to 3272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications, and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.

Background 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. Methods and findings 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. Conclusions 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.

Using an electronic health record with information entered at the point of care, we found that early immunization status is a strong predictor of immunization delay for young children that can be identified as early as 3 months of age. Electronic health records may prove useful to clinicians and health systems in identifying children at high risk for immunization delay.

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This retrospective cohort study aimed to describe antipyretic use among healthy patients in a pediatric emergency department (ED) with nonurgent fever defined as: triage level 4 or 5, chief complaint fever or temperature 38°C to 39°C, and otherwise normal vital signs, and determine if antipyretic administration is associated with increased ED length of stay (LOS). We compared continuous variables using Kruskal-Wallis and Wilcoxon rank sum testing. We adjusted confounding variables using logistic regression modeling. A total of 22 169 patients were included. Of these, 52% received antipyretic: acetaminophen (38%), ibuprofen (19%), or both antipyretics (5%). ED LOS (median hours) varied by number of antipyretic types given (none, 2.2; ibuprofen, 2.7; acetaminophen, 2.7; and both 3.4, P < .001) and number of doses (0 doses, 2.2, 1 dose, 2.7; 2 doses, 3.4, P < .001). Patients who received antipyretic were more likely to have ED LOS greater than 2 hours (adjusted odds ratio 1.99, 95% CI 1.88-2.11) compared with those with no antipyretic, controlling for age, imaging studies, laboratory studies, antibiotic administration, and disposition.