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We previously found that antibiotic use at24 months of age was associated with slightly higher body weight at 5 years of age. In this study, we examine associations of early life antibiotic prescriptions with weight outcomes at 108 to 132 months of age ("10 years").We used electronic health record data from 2009 through 2016 from 10 health systems in PCORnet, a national distributed clinical research network. We examined associations of any (vs no) antibiotics at24 months of age with body mass index z-score (BMI-z) at 10 years adjusted for confounders selected a priori. We further examined dose response (number of antibiotic episodes) and antibiotic spectrum (narrow and broad).Among 56,727 included children, 57% received any antibiotics at24 months; at 10 years, mean (standard deviation) BMI-z was 0.54 (1.14), and 36% had overweight or obesity. Any versus no antibiotic use at24 months was associated with a slightly higher BMI-z at 10 years among children without a complex chronic condition (β 0.03; 95% confidence interval [CI] 0.01, 0.05) or with a complex chronic condition (β 0.09; 95% CI 0.03, 0.15). Any versus no antibiotic use was not associated with odds of overweight or obesity at 10 years among children without (odds ratio 1.02; 95% CI 0.97, 1.07) or with a complex chronic condition (odds ratio 1.07; 95% CI 0.96, 1.19).The small and likely clinically insignificant associations in this study are consistent with our previous 5-year follow-up results, suggesting that, if this relationship is indeed causal, early increases in weight are small but maintained over time.

Background The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients. Methods The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798). Results The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months. Conclusions The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.

BACKGROUND Privacy-protecting analytic approaches without centralized pooling of individual-level data, such as distributed regression, are particularly important for vulnerable populations, such as children, but these methods have not yet been tested in multi-center pediatric studies. METHODS Using the electronic health data from 34 healthcare institutions in the National Patient-Centered Clinical Research Network (PCORnet), we fit 12 multivariable-adjusted linear regression models to assess the associations of antibiotic use

We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.

Objectives To 1) probabilistically link two important pediatric data sources, Virtual Pediatric Systems and PEDSnet, 2) evaluate linkage accuracy overall and in patients with severe sepsis or septic shock, and 3) identify variables important to linkage accuracy. Design Retrospective linkage of prospectively collected datasets from Virtual Pediatrics Systems, Inc (Los Angeles, CA) and the PEDSnet consortium. Setting Single-center academic PICU. Patients All PICU encounters between January 1, 2012, and December 31, 2017, that were deterministically matched between the two datasets. Interventions None. Measurements and main results We abstracted records from Virtual Pediatric Systems and PEDSnet corresponding to PICU encounters and probabilistically linked using 44 features shared by the two datasets. We generated a gold standard deterministic linkage using protected health information elements, which were then removed from datasets. We then calculated candidate pair log-likelihood ratios for all pairs of subjects and selected optimal pairs in a two-stage algorithm. A total of 22,051 gold standard PICU encounter pairs were identified over the study period. The optimal linkage model demonstrated excellent discrimination (area under the receiver operating characteristic curve > 0.99); 19,801 cases (89.9%) were matched with 13 false positives. The addition of two protected health information dates (admission month, birth day-of-year) increased to 20,189 (91.6%) the cases matched, with three false positives. Restricting to patients with Virtual Pediatric Systems diagnosis of severe sepsis or septic shock (n = 1,340 [6.1%]) matched 1,250 cases (93.2%) with zero false positives. Increased number of laboratory values present in the first 12 hours of admission significantly increased log-likelihood ratios, suggesting stronger candidate pair matching. Conclusions We demonstrated the use of probabilistic linkage to accurately join two complementary pediatric critical care datasets at a single academic PICU in the absence of protected health information. Combining datasets with curated diagnoses and granular measurements can validate patient acuity metrics and facilitate multicenter machine learning algorithms. We anticipate these methods will generalize to other common PICU diagnoses.

The benefits of antibiotic treatment during pregnancy are immediate, but there may be long-term risks to the developing child. Prior studies show an association between early life antibiotics and obesity, but few have examined this risk during pregnancy. To evaluate the association of maternal antibiotic exposure during pregnancy on childhood BMI-z at 5 years, we conducted a retrospective cohort analysis. Using electronic health record data from seven health systems in PCORnet, a national distributed clinical research network, we included children with same-day height and weight measures who could be linked to mothers with vital measurements during pregnancy. The primary independent variable was maternal outpatient antibiotic prescriptions during pregnancy (any versus none). We examined dose response (number of antibiotic episodes), spectrum and class of antibiotics, and antibiotic episodes by trimester. The primary outcome was child age- and sex-specific BMI-z at age 5 years. The final sample was 53,320 mother–child pairs. During pregnancy, 29.9% of mothers received antibiotics. In adjusted models, maternal outpatient antibiotic prescriptions during pregnancy were not associated with child BMI-z at age 5 years (β = 0.00, 95% CI −0.03, 0.02). When evaluating timing during pregnancy, dose-response, spectrum and class of antibiotics, there were no associations of maternal antibiotics with child BMI-z at age 5 years. In this large observational cohort, provision of antibiotics during pregnancy was not associated with childhood BMI-z at 5 years.

Phenotyping, ie, identification of patients possessing a characteristic of interest, is a fundamental task for research conducted using electronic health records. However, challenges to this task include imperfect sensitivity and specificity of clinical codes and inconsistent availability of more detailed data such as laboratory test results. Despite these challenges, most existing electronic health records-derived phenotypes are rule-based, consisting of a series of Boolean arguments informed by expert knowledge of the disease of interest and its coding. The objective of this paper is to introduce a Bayesian latent phenotyping approach that accounts for imperfect data elements and missing not at random missingness patterns that can be used when no gold-standard data are available. We conducted simulation studies to compare alternative phenotyping methods under different patterns of missingness and applied these approaches to a cohort of 68 265 children at elevated risk for type 2 diabetes mellitus (T2DM). In simulation studies, the latent class approach had similar sensitivity to a rule-based approach (95.9% vs 91.9%) while substantially improving specificity (99.7% vs 90.8%). In the PEDSnet cohort, we found that biomarkers and clinical codes were strongly associated with latent T2DM status. The latent T2DM class was also strongly predictive of missingness in biomarkers. Glucose was missing in 83.4% of patients (odds ratio for latent T2DM status = 0.52) while hemoglobin A1c was missing in 91.2% (odds ratio for latent T2DM status = 0.03 ), suggesting missing not at random missingness. The latent phenotype approach may substantially improve on rule-based phenotyping.

OBJECTIVES: The National Patient-Centered Clinical Research Network (PCORnet) supports observational and clinical research using healthcare data. The PCORnet Antibiotics and Childhood Growth Study is one of PCORnet’s inaugural observational studies. The objectives of this manuscript are to describe (1) the processes used to integrate and analyze data from children across 36 participating institutions and (2) the cohort characteristics and prevalence of antibiotic use. METHODS: We included children in the cohort if they had at least one same-day height and weight measured in each of three age periods: 1) before 12 months, 2) 12 to 30 months, and 3) after 24 months. We distributed statistical queries that each institution ran on its local version of the PCORnet Common Data Model, with aggregate data returned for analysis. We defined overweight or obesity as age-sex-specific body mass index ≥ 85(th), obesity ≥95(th) percentile, and severe obesity ≥120% of the 95(th) percentile. RESULTS: 681,739 children met the cohort inclusion criteria and were racially/ethnically diverse (24.9% black, 17.5% Hispanic). Before 24 months, 55.2% of children received at least one antibiotic prescription; 21.3% received a single antibiotic prescription, 14.3% received four or more, and 33.3% received a broad spectrum antibiotic. Overweight and obesity prevalence was 27.6% at 4 to

Kaposiform hemangioendothelioma (KHE) is a rare vascular tumor characterized by aggressive local invasion and a syndrome of platelet trapping known as Kasabach-Merritt phenomenon that, through deposition of platelet derived growth factors, may perpetuate the growth of the tumor. Although many cases of KHE are successfully treated with local control or low-intensity chemotherapy, some cases are often refractory even to aggressive treatment. Herein, we describe a patient with a refractory, recurrent KHE despite multiple attempts at local control and intensive chemotherapy, that ultimately was successfully treated with rationally designed and low-intensity combination therapy of sirolimus and aspirin.

Objective PEDSnet is a clinical data research network (CDRN) that aggregates electronic health record data from multiple children’s hospitals to enable large-scale research. Assessing data quality to ensure suitability for conducting research is a key requirement in PEDSnet. This study presents a range of data quality issues identified over a period of 18 months and interprets them to evaluate the research capacity of PEDSnet. Materials and Methods Results were generated by a semiautomated data quality assessment workflow. Two investigators reviewed programmatic data quality issues and conducted discussions with the data partners’ extract-transform-load analysts to determine the cause for each issue. Results The results include a longitudinal summary of 2182 data quality issues identified across 9 data submission cycles. The metadata from the most recent cycle includes annotations for 850 issues: most frequent types, including missing data (>300) and outliers (>100); most complex domains, including medications (>160) and lab measurements (>140); and primary causes, including source data characteristics (83%) and extract-transform-load errors (9%). Discussion The longitudinal findings demonstrate the network’s evolution from identifying difficulties with aligning the data to a common data model to learning norms in clinical pediatrics and determining research capability. Conclusion While data quality is recognized as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished. This paper presents a real-world account of studying and interpreting data quality findings in a pediatric CDRN, and the lessons learned could be used by other CDRNs.

Background Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies. Methods This study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters. Results The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99. Conclusion The EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.

Measures of health care quality are produced from a variety of data sources, but often, physicians do not believe these measures reflect the quality of provided care. The aim was to assess the value to health system leaders (HSLs) and parents of benchmarking on health care quality measures using data mined from the electronic health record (EHR). Using in-context interviews with HSLs and parents, the authors investigated what new decisions and actions benchmarking using data mined from the EHR may enable and how benchmarking information should be presented to be most informative. Results demonstrate that although parents may have little experience using data on health care quality for decision making, they affirmed its potential value. HSLs expressed the need for high-confidence, validated metrics. They also perceived barriers to achieving meaningful metrics but recognized that mining data directly from the EHR could overcome those barriers. Parents and HSLs need high-confidence health care quality data to support decision making.

BACKGROUND: Little is known about the specific information parents of children with cancer search for online. Understanding the content of parents’ searches over time could offer insight into what matters most to parents and identify knowledge gaps that could inform more comprehensive approaches to family education and support. METHODS: We describe parents’ health-related Google searches starting six months before cancer diagnosis and extending through the date of study enrollment which was at least one month after initiating cancer treatment. Searches were obtained retrospectively and grouped into health-related and non-health-related categories. The median time to parent enrollment from date of cancer diagnosis was 264 days. RESULTS: Parents searched for health-related topics more frequently than the general population (13% vs. 5%). Health-related searches increased in the months preceding the child’s cancer diagnosis and most commonly pertained to symptoms and logistics, “directions to hospital”. Health-related search volume peaked about a month after cancer diagnosis when general health-related searches were present in addition to cancer-specific searches. Eighteen percent of health-related searches were cancer-specific and of these cancer-specific searches, 54% pertained to support, for example “cancer quote for son.” CONCLUSIONS: Google search content offers insight into what matters to parents of cancer patients. Understanding search content could inform more comprehensive approaches to family education and support initiatives.

Importance There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and infection among pediatric patients across the United States. Objective To describe testing for SARS-CoV-2 and the epidemiology of infected patients. Design, Setting, and Participants A retrospective cohort study was conducted using electronic health record data from 135 794 patients younger than 25 years who were tested for SARS-CoV-2 from January 1 through September 8, 2020. Data were from PEDSnet, a network of 7 US pediatric health systems, comprising 6.5 million patients primarily from 11 states. Data analysis was performed from September 8 to 24, 2020. Exposure Testing for SARS-CoV-2. Main Outcomes and Measures SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) illness. Results A total of 135 794 pediatric patients (53% male; mean [SD] age, 8.8 [6.7] years; 3% Asian patients, 15% Black patients, 11% Hispanic patients, and 59% White patients; 290 per 10 000 population [range, 155-395 per 10 000 population across health systems]) were tested for SARS-CoV-2, and 5374 (4%) were infected with the virus (12 per 10 000 population [range, 7-16 per 10 000 population]). Compared with White patients, those of Black, Hispanic, and Asian race/ethnicity had lower rates of testing (Black: odds ratio [OR], 0.70 [95% CI, 0.68-0.72]; Hispanic: OR, 0.65 [95% CI, 0.63-0.67]; Asian: OR, 0.60 [95% CI, 0.57-0.63]); however, they were significantly more likely to have positive test results (Black: OR, 2.66 [95% CI, 2.43-2.90]; Hispanic: OR, 3.75 [95% CI, 3.39-4.15]; Asian: OR, 2.04 [95% CI, 1.69-2.48]). Older age (5-11 years: OR, 1.25 [95% CI, 1.13-1.38]; 12-17 years: OR, 1.92 [95% CI, 1.73-2.12]; 18-24 years: OR, 3.51 [95% CI, 3.11-3.97]), public payer (OR, 1.43 [95% CI, 1.31-1.57]), outpatient testing (OR, 2.13 [1.86-2.44]), and emergency department testing (OR, 3.16 [95% CI, 2.72-3.67]) were also associated with increased risk of infection. In univariate analyses, nonmalignant chronic disease was associated with lower likelihood of testing, and preexisting respiratory conditions were associated with lower risk of positive test results (standardized ratio [SR], 0.78 [95% CI, 0.73-0.84]). However, several other diagnosis groups were associated with a higher risk of positive test results: malignant disorders (SR, 1.54 [95% CI, 1.19-1.93]), cardiac disorders (SR, 1.18 [95% CI, 1.05-1.32]), endocrinologic disorders (SR, 1.52 [95% CI, 1.31-1.75]), gastrointestinal disorders (SR, 2.00 [95% CI, 1.04-1.38]), genetic disorders (SR, 1.19 [95% CI, 1.00-1.40]), hematologic disorders (SR, 1.26 [95% CI, 1.06-1.47]), musculoskeletal disorders (SR, 1.18 [95% CI, 1.07-1.30]), mental health disorders (SR, 1.20 [95% CI, 1.10-1.30]), and metabolic disorders (SR, 1.42 [95% CI, 1.24-1.61]). Among the 5374 patients with positive test results, 359 (7%) were hospitalized for respiratory, hypotensive, or COVID-19–specific illness. Of these, 99 (28%) required intensive care unit services, and 33 (9%) required mechanical ventilation. The case fatality rate was 0.2% (8 of 5374). The number of patients with a diagnosis of Kawasaki disease in early 2020 was 40% lower (259 vs 433 and 430) than in 2018 or 2019. Conclusions and Relevance In this large cohort study of US pediatric patients, SARS-CoV-2 infection rates were low, and clinical manifestations were typically mild. Black, Hispanic, and Asian race/ethnicity; adolescence and young adulthood; and nonrespiratory chronic medical conditions were associated with identified infection. Kawasaki disease diagnosis is not an effective proxy for multisystem inflammatory syndrome of childhood.

Importance Bacteremia causes considerable morbidity among children with acute leukemia and those undergoing hematopoietic stem cell transplantation (HSCT). There are limited data on the effect of antibiotic prophylaxis in children. Objective To determine the efficacy and risks of levofloxacin prophylaxis in children receiving intensive chemotherapy for acute leukemia or undergoing HSCT. Design, setting, and participants In this multicenter, open-label, randomized trial, patients (6 months-21 years) receiving intensive chemotherapy were enrolled (September 2011-April 2016) in 2 separate groups-acute leukemia, consisting of acute myeloid leukemia or relapsed acute lymphoblastic leukemia, and HSCT recipients-at 76 centers in the United States and Canada, with follow-up completed September 2017. Interventions Patients with acute leukemia were randomized to receive levofloxacin prophylaxis for 2 consecutive cycles of chemotherapy (n = 100) or no prophylaxis (n = 100). Those undergoing HSCT were randomized to receive levofloxacin prophylaxis during 1 HSCT procedure (n = 210) or no prophylaxis (n = 214). Main outcomes and measures The primary outcome was the occurrence of bacteremia during 2 chemotherapy cycles (acute leukemia) or 1 transplant procedure (HSCT). Secondary outcomes included fever and neutropenia, severe infection, invasive fungal disease, Clostridium difficile-associated diarrhea, and musculoskeletal toxic effects. Results A total of 624 patients, 200 with acute leukemia (median [interquartile range {IQR}] age, 11 years [6-15 years]; 46% female) and 424 undergoing HSCT (median [IQR] age, 7 years [3-14]; 38% female), were enrolled. Among 195 patients with acute leukemia, the likelihood of bacteremia was significantly lower in the levofloxacin prophylaxis group than in the control group (21.9% vs 43.4%; risk difference, 21.6%; 95% CI, 8.8%-34.4%, P = .001), whereas among 418 patients undergoing HSCT, the risk of bacteremia was not significantly lower in the levofloxacin prophylaxis group (11.0% vs 17.3%; risk difference, 6.3%; 95% CI, 0.3%-13.0%; P = .06). Fever and neutropenia were less common in the levofloxacin group (71.2% vs 82.1%; risk difference, 10.8%; 95% CI, 4.2%-17.5%; P = .002). There were no significant differences in severe infection (3.6% vs 5.9%; risk difference, 2.3%; 95% CI, -1.1% to 5.6%; P = .20), invasive fungal disease (2.9% vs 2.0%; risk difference, -1.0%; 95% CI, -3.4% to 1.5%, P = .41), C difficile-associated diarrhea (2.3% vs 5.2%; risk difference, 2.9%; 95% CI, -0.1% to 5.9%; P = .07), or musculoskeletal toxic effects at 2 months (11.4% vs 16.3%; risk difference, 4.8%; 95% CI, -1.6% to 11.2%; P = .15) or at 12 months (10.1% vs 14.4%; risk difference, 4.3%; 95% CI, -3.4% to 12.0%; P = .28) between the levofloxacin and control groups. Conclusions and relevance Among children with acute leukemia receiving intensive chemotherapy, receipt of levofloxacin prophylaxis compared with no prophylaxis resulted in a significant reduction in bacteremia. However, there was no significant reduction in bacteremia for levofloxacin prophylaxis among children undergoing HSCT.

OBJECTIVES: To determine the association of antibiotic use with weight outcomes in a large cohort of children. METHODS: Health care data were available from 2009 to 2016 for 35 institutions participating in the National Patient-Centered Clinical Research Network. Participant inclusion required same-day height and weight measurements at 0 to RESULTS: Among 1 792 849 children with a same-day height and weight measurement at CONCLUSIONS: Antibiotic use at

Background At diagnosis, there are prognostic implications of low-level leukemic blasts (CNS 2) in the cerebrospinal fluid (CSF) of patients with acute lymphoblastic leukemia (ALL). However, the significance of post-induction CNS 2 results and the impact of equipment on CNS 2 prevalence have not been well studied. Procedure A single-institution retrospective cohort study was conducted to analyze the outcome of patients with ≥1 post-induction CNS 2. A subanalysis compared the proportion of CNS 2 CSF results using 2 different cytocentrifuges; the Shandon Cytospin used from 2005 to 2008 and the Wescor Cytopro used from 2010 to 2014. Results Over 4500 post-induction CSF samples were analyzed, of which 59 were CNS 2. In covariate-adjusted analyses, post-induction CNS 2 did not significantly increase relapse risk. The proportion of CNS 2 results increased 4.3-fold in noninfants and 6.3-fold in infants using the Wescor Cytopro. Cytocentrifuge machine did not affect CNS 3 prevalence. Conclusions These findings support our current practice of not changing management based on a post-induction CNS 2 CSF and highlight how equipment changes can significantly influence testing results. More data are needed to analyze relapse by subpopulations, such as those with repeated CNS 2 findings.

Atypical chronic myeloid leukemia, BCR-ABL1-negative, (aCML) is a rare myeloid neoplasm. Recent adult data suggest the leukemic cells in a subset of patients are dependent on JAK/STAT signaling and harbor CSF3R-activating mutations. We hypothesized that, similar to adult patients, the presence of CSF3R-activating mutations would be clinically relevant in pediatric myeloid neoplasms as patients would be sensitive to the JAK inhibitor, ruxolitinib. We report two cases of morphologically similar pediatric aCML, BCR-ABL1-negative based on WHO 2008 criteria. One patient had CSF3R-activating mutation (T618I) and demonstrated a robust response to ruxolitinib, which was used to bridge to a successful stem cell transplant. The other patient did not have a CSF3R-activating mutation and succumbed to refractory disease

Objective This article reviews the data available in 3 large databases for use in conducting studies of children with autism spectrum disorder (ASD). Methods The article describes the data structure, data elements, and strengths and weaknesses of the 3 data sets. Results Each of the 3 data sets, the Interactive Autism Network (IAN), the Autism Treatment Network (ATN), and PEDSnet have large cohorts of children with ASD. IAN has strengths in patient-reported measures, ATN in clinical characterization, and PEDSnet in health care encounters and electronic medical record data. Conclusion The data sets described here have potential for further studies that could help improve the care and well-being of children with ASD and their families.

Observational data resources based on the capture of clinical data in the electronic health record (EHR) have produced significant learning opportunities in many areas of medicine. These large data resources can span multiple hospital systems and employ common semantics, ontologies, and data models. They have uncovered critical safety issues for patients, and spurred observational research and clinical decision support. In the age of precision medicine there is also an increased need to obtain genomic and clinical data to discover novel treatments for the deadliest of diseases. With this, there are efforts to create deep-dive disease specific repositories that include tissue in biobanks. The latter require significant human annotation of biospecimens. Securing the data is especially critical in rare pediatric brain tumors. In the specific case of The Children's Brain Tumor Tissue Consortium (CBTTC) an international rare pediatric brain tumor repository, the number of patients that need to be followed prospectively is outpacing the ability of human annotation. In this preliminary study, we perform a prescribed data exploration analysis on simulation data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) employed by the pediatric data network PEDSNet with the intention to ascertain feasibility in automatic annotation of patient records in the CBTTC.

Background: In the United States, cancer is common, with high morbidity and mortality; cancer incidence varies between states. Online searches reflect public awareness, which could be driven by the underlying regional cancer epidemiology. Objective: The objective of our study was to characterize the relationship between cancer incidence and online Google search volumes in the United States for 6 common cancers. A secondary objective was to evaluate the association of search activity with cancer-related public events and celebrity news coverage. Methods: We performed a population-based, retrospective study of state-level cancer incidence from 2004 through 2013 reported by the Centers for Disease Control and Prevention for breast, prostate, colon, lung, and uterine cancers and leukemia compared to Google Trends (GT) relative search volume (RSV), a metric designed by Google to allow interest in search topics to be compared between regions. Participants included persons in the United States who searched for cancer terms on Google. The primary measures were the correlation between annual state-level cancer incidence and RSV as determined by Spearman correlation and linear regression with RSV and year as independent variables and cancer incidence as the dependent variable. Temporal associations between search activity and events raising public awareness such as cancer awareness months and cancer-related celebrity news were described. Results: At the state level, RSV was significantly correlated to incidence for breast (r=.18, P=.001), prostate (r=–.27, P

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.

Objective To evaluate the validity of multi-institutional electronic health record (EHR) data sharing for surveillance and study of childhood obesity. Methods We conducted a non-concurrent cohort study of 528,340 children with outpatient visits to six pediatric academic medical centers during 2007–08, with sufficient data in the EHR for body mass index (BMI) assessment. EHR data were compared with data from the 2007–08 National Health and Nutrition Examination Survey (NHANES). Results Among children 2–17 years, BMI was evaluable for 1,398,655 visits (56%). The EHR dataset contained over 6,000 BMI measurements per month of age up to 16 years, yielding precise estimates of BMI. In the EHR dataset, 18% of children were obese versus 18% in NHANES, while 35% were obese or overweight versus 34% in NHANES. BMI for an individual was highly reliable over time (intraclass correlation coefficient 0.90 for obese children and 0.97 for all children). Only 14% of visits with measured obesity (BMI ≥95%) had a diagnosis of obesity recorded, and only 20% of children with measured obesity had the diagnosis documented during the study period. Obese children had higher primary care (4.8 versus 4.0 visits, p

We describe a novel algorithm (ChipStat) for detecting gene-expression changes utilizing probe-level comparisons of replicate Affymetrix oligonucleotide microarray data. A combined detection approach is shown to yield greater sensitivity than a number of widely used methodologies including SAM, dChip and logit-T. Using this approach, we identify alterations in functional pathways during murine neonatal-pubertal mammary development that include the coordinate upregulation of major urinary proteins and the downregulation of loci exhibiting reciprocal imprinting.