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Children with glomerular disease have unique risk factors for compromised bone health. Studies addressing skeletal complications in this population are lacking.This retrospective cohort study utilized data from PEDSnet, a national network of pediatric health systems with standardized electronic health record data for more than 6.5 million patients from 2009 to 2021. Incidence rates (per 10,000 person-years) of fracture, slipped capital femoral epiphysis (SCFE), and avascular necrosis/osteonecrosis (AVN) in 4598 children and young adults with glomerular disease were compared with those among 553,624 general pediatric patients using Poisson regression analysis. The glomerular disease cohort was identified using a published computable phenotype. Inclusion criteria for the general pediatric cohort were two or more primary care visits 1 year or more apart between 1 and 21 years of age, one visit or more every 18 months if followed3 years, and no chronic progressive conditions defined by the Pediatric Medical Complexity Algorithm. Fracture, SCFE, and AVN were identified using SNOMED-CT diagnosis codes; fracture required an associated x-ray or splinting/casting procedure within 48 hours.We found a higher risk of fracture for the glomerular disease cohort compared with the general pediatric cohort in girls only (incidence rate ratio [IRR], 1.6; 95% CI, 1.3 to 1.9). Hip/femur and vertebral fracture risk were increased in the glomerular disease cohort: adjusted IRR was 2.2 (95% CI, 1.3 to 3.7) and 5 (95% CI, 3.2 to 7.6), respectively. For SCFE, the adjusted IRR was 3.4 (95% CI, 1.9 to 5.9). For AVN, the adjusted IRR was 56.2 (95% CI, 40.7 to 77.5).Children and young adults with glomerular disease have significantly higher burden of skeletal complications than the general pediatric population.

PURPOSE: Chemotherapy-induced nausea and vomiting (CINV) is a very common side effect of pediatric cancer therapy. High-quality, evidence-based, pediatric-specific guidelines for prophylaxis and treatment of CINV are available. At many centers, guideline-concordant care is uncommon. We formed a multidisciplinary quality improvement team to implement guideline-concordant care for CINV prophylaxis at our center. We present the results following the first year of our interventions. METHODS: We planned and implemented a multipronged approach in three key phases: (1) developing and publishing an acute CINV prophylaxis pathway, (2) education of providers, and (3) updating the computerized provider order entry system. We used iterative, sequential Plan-Do-Study-Act cycles and behavioral economic strategies to improve adherence to guideline-concordant CINV prophylaxis. We focused on aprepitant usage as a key area for improvement. RESULTS: At the beginning of the study period, < 50% of patients were receiving guideline-concordant CINV prophylaxis and < 15% of eligible patients were receiving aprepitant. After 1 year, more than 60% of patients were receiving guideline-concordant care and 50% of eligible patients were receiving aprepitant. CONCLUSION: We describe the development and implementation of a standardized pathway for prevention of acute CINV in pediatric oncology patients. With a multidisciplinary, multifaceted approach, we demonstrate significant improvements to guideline-congruent CINV prophylaxis.

Study Objectives Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). Methods We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. Results Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. Conclusions Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

BackgroundPrior studies have documented differences in the age, racial, and ethnic characteristics among patients with SARS-CoV-2 infection. However, little is known about how these characteristics changed over time during the pandemic and whether racial, ethnic, and age disparities evident early in the pandemic were persistent over time. This study reports on trends in SARS-CoV-2 infections among U.S. adults from March 1, 2020 to January, 31 2022, using data from electronic health records.Methods and FindingsWe captured repeated cross-sectional information from 43 large healthcare systems in 52 U.S. States and territories, participating in PCORnet®, the National Patient-Centered Clinical Research Network. Using distributed queries executed at each participating institution, we acquired information for all patients ≥ 20 years of age who were tested for SARS-CoV-2 (both positive and negative results), including care setting, age, sex, race, and ethnicity by month as well as comorbidities (assessed with diagnostic codes).During this time period, 1,325,563 patients had positive (13% inpatient) and 6,705,868 patients had negative (25% inpatient) viral tests for SARS-CoV-2. Disparities in testing positive were present across racial and ethnic groups, especially in the inpatient setting. Compared to White patients, Black or African American and other race patients had relative risks for testing positive of 1.5 or greater in the inpatient setting for 12 of the 23-month study period. Compared to non-Hispanic patients, Hispanic patients had relative risks for testing positive in the inpatient setting of 1.5 or greater for 16 of 23. Ethnic and racial differences were present in emergency department and ambulatory settings but were less common across time than in inpatient settings. Trends in infections by age group demonstrated higher test positivity for older patients in the inpatient setting only for most months, except for June and July of 2020, April to August 2021, and January 2022. Comorbidities were common, with much higher rates among those hospitalized; hypertension (38% of patients SARS-CoV-2 positive vs. 29% for those negative) and type 2 diabetes mellitus (22% vs. 13%) were the most common.Conclusion and RelevanceRacial and ethnic disparities changed over time among persons infected with SARS-CoV-2. These trends highlight potential underlying mechanisms, such as poor access to care and differential vaccination rates, that may have contributed to greater disparities, especially early in the pandemic. Monitoring data on characteristics of patients testing positive in real time could allow public health officials and policymakers to tailor interventions to ensure that patients and communities most in need are receiving adequate testing, mitigation strategies, and treatment.

STRUCTURED ABSTRACTObjectivesPost-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.Materials and MethodsWe used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N=1309) to children with (N=6545) and without (N=6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls.ResultsWe found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise.DiscussionOur study addresses methodological limitations of prior studies that rely on pre-specified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes.ConclusionWe identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.LAY SUMMARYPediatric long COVID in children does not currently have a precise clinical definition, in part due to its widely varying presentation in kids. By comparing children diagnosed with long COVID to children who had COVID-19 but were not diagnosed with long COVID, this study identified several groups of symptoms and conditions that are associated with pediatric long COVID. These findings can be used towards developing a precise definition of long COVID in children for use in future studies.

BackgroundAs clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data.Methods and FindingsIn this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values.ConclusionsThe model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.Funding SourceThis research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.DisclaimerThe content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.

BackgroundPost-acute sequelae of SARS-Co-V-2 infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory.ObjectiveTo explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis [DKA] or severe hypoglycemia [SH]) or Hemoglobin (Hb) A1c trajectory.MethodsWe included children ResultsFrom 03/01/2020-06/22/2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow cohorts, respectively. The hazard ratio for utilization was (HR 1.45 [95%CI,0.97,2.16]). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91-180 days after cohort entry for those with COVID-19 (0.138 vs. -0.002, p=0.172). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (-0.104 vs. 0.008 per month, p=0.024).ConclusionWhile a trend towards worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.Authorship StatementAuthorship has been determined according to ICMJE recommendationsDisclaimerThe content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.Funding Source⍰This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.

BackgroundMulti-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters.MethodsWe conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns.FindingsWe identified 1186 children hospitalized with MIS-C. The highest proportion of children (44·4%) were aged between 5-11 years, with a male predominance (61.0%), and non- Hispanic white ethnicity (40·2%). Most (67·8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72·5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51·1% of children early in the pandemic, which decreased to 33·9% from the pre-delta period to the omicron period.InterpretationMIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time.Research in contextEvidence before this studyWe searched PubMed and preprint articles from December 2019, to July 2022, for studies published in English that investigated the clinical subphenotypes of MIS-C using the terms “multi-system inflammatory syndrome in children” or “pediatric inflammatory multisystem syndrome” and “phenotypes”. Most previous research described the symptoms, clinical characteristics and risk factors associated with MIS-C and how these differ from acute COVID-19, Kawasaki Disease and Toxic Shock Syndrome. One single-center study of 63 patients conducted in 2020 divided patients into Kawasaki and non-Kawasaki disease subphenotypes. Another CDC study evaluated 3 subclasses of MIS-C in 570 children, with one class representing the highest number of organ systems, a second class with predominant respiratory system involvement, and a third class with features overlapping with Kawasaki Disease. However, this study evaluated cases from March to July 2020, during the early phase of the pandemic when misclassification of cases as Kawasaki disease or acute COVID-19 may have occurred. Therefore, it is not known from the existing literature whether the presentation of MIS-C has changed with newer variants such as delta and omicron.Added value of this studyPEDSnet provides one of the largest MIS-C cohorts described so far, providing sufficient power for detailed analyses on MIS-C subphenotypes. Our analyses span the entire length of the pandemic, including the more recent omicron wave, and provide an update on the presentations of MIS-C and its temporal dynamics. We found that children have a spectrum of illness that can be characterized as mild (lower inflammatory markers, fewer organ systems involved), moderate (4-6 organ involvement with clinical overlap with acute COVID-19) and severe (higher inflammatory markers, critically ill, more likely to have cardiac involvement, with hypotension/shock and need for vasopressors).Implications of all the available evidenceThese results provide an update to the subphenotypes of MIS-C including the more recent delta and omicron periods and aid in the understanding of the various presentations of MIS-C. These and other findings provide a useful framework for clinicians in the recognition of MIS-C, identify factors associated with children at risk for increased severity, including the importance of laboratory parameters, for risk stratification, and to facilitate early evaluation, diagnosis and treatment.

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.

Using electronic health record data combined with primary chart review, we identified 7 children across 8 pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) who were managed as outpatients. These findings should prompt a discussion about modifying the case definition to allow for such a possibility.

BackgroundChronic medical conditions are a risk factor for moderate or severe COVID-19 in children, but little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) in children with chronic medical conditions (CMCs). To understand whether SARS-CoV-2 infection led to potential exacerbation of underlying chronic disease in children, we explored whether children with CMCs had increased healthcare utilization in the post-acute (28 days after infection) period compared to children with CMCs without SARS-CoV-2 infection.MethodsWe conducted a retrospective, matched-cohort study using electronic health record data collected from 8 pediatric health care systems participating in the PEDSnet network. We included children ResultsWe identified 748,692 patients with at least one chronic condition, 78,744 of whom met inclusion criteria for the COVID-19 cohort. 96% of patients from the positive cohort were matched. Cohorts were well-balanced for chronic condition clusters, total number of conditions, time since first diagnosis, baseline utilization, cohort entry period, age, sex, race/ethnicity and test location. We found that among children with chronic medical conditions, those with COVID-19 had higher healthcare utilization than those with no recorded COVID-19 diagnosis or positive test, with utilization rate ratio of 1.21 (95% CI: 1.18-1.24). The utilization was highest for inpatient care with utilization rate ratio of 2.03 (95% CI: 1.85-2.23) but the utilization was increased across all settings. Hazard ratios estimated in time-to-first-utilization analysis mirrored these results. Patients with severe or moderate acute COVID-19 illness had greater increases in utilization in all settings than those with mild or asymptomatic disease.ConclusionsWe found that care utilization in all settings was increased following COVID-19 in children with chronic medical conditions in the post-acute period, particularly in the inpatient setting. Increased utilization was correlated with more severe COVID-19. Additional research is needed to better understand the reasons for higher care utilization by studying condition-specific outcomes in children with chronic disease.

The impact of post-acute sequelae of SARS-CoV-2 infection (PASC) in children is underrecognized. We developed an EHR-based algorithm across eight pediatric institutions to identify children with COVID-19 based on serology testing from 3/2020 through 4/2022 who had not been identified by PCR. Overall, serology tests were used 100-fold less than PCR. Seroprevalence of IgG anti-nucleocapsid antibodies remained stable, while rates of positive IgG anti-spike antibodies increased in teenagers after COVID-19 vaccine approval. Through data harmonization and after excluding 1,410 serology test results that may have been influenced by vaccines, we identified 2,714 children that were COVID-19 positive exclusively by serology. These patients were frequently tested as inpatients (24% vs. 2%), had chronic conditions more frequently (37% vs 24%), and a MIS-C diagnosis (23% vs.

ImportanceThe post-acute sequelae of SARS-CoV-2 (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited.ObjectiveTo identify diagnosed symptoms, diagnosed health conditions and medications associated with PASC in children.Design, Setting and ParticipantsRetrospective cohort study using electronic health records from 9 US children’s hospitals for individuals ExposureSARS-CoV-2 PCR positivity.Main Outcomes and MeasuresWe identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28-179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to obtain a burden of PASC estimate.ResultsAmong 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings. The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16-3.32), myocarditis (aHR 3.10 [95% CI 1.94-4.96]), and cough and cold preparations (aHR 1.52 [95% CI 1.18-1.96]). The incidence of at least one systemic/syndromic/medication feature of PASC was 41.9% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.7% (95% CI 3.2-4.2%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions.Conclusions and RelevanceIn this large-scale, exploratory study, the burden of pediatric PASC that presented to health systems was low. Myocarditis was the most commonly diagnosed PASC-associated condition. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC.Key PointsQuestionWhat are the incidence and clinical features of post-acute sequelae of SARS-CoV-2 infection (PASC) in children?FindingsIn this retrospective cohort study of 659,286 children tested for SARS-CoV-2 by polymerase chain reaction (PCR), the symptom, condition and medication with the strongest associations with SARS-CoV-2 infection were loss of taste/smell, myocarditis, and cough and cold preparations. The incidence proportion of non-MIS-C related PASC in the PCR-positive group exceeded the PCR-negative group by 3.7% (95% CI 3.2-4.2), with increased rates associated with acute illness severity, young age, and medical complexity.MeaningPASC in children appears to be uncommon, with features that differ from adults.

New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear.To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2.This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing.International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated).A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01];20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%.In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.

Introduction Secondary use of electronic health record (EHR) data for research requires that the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural conformance and completeness of clinical data extracted from source systems. In this paper, we propose a framework for evaluating semantic DQ that will allow researchers to evaluate fitness for use prior to analyses. Methods We reviewed current DQ literature, as well as experience from recent multisite network studies, and identified gaps in the literature and current practice. Derived principles were used to construct the conceptual framework with attention to both analytic fitness and informatics practice. Results We developed a systematic framework that guides researchers in assessing whether a data source is fit for use for their intended study or project. It combines tools for evaluating clinical context with DQ principles, as well as factoring in the characteristics of the data source, in order to develop semantic DQ checks. Conclusions Our framework provides a systematic process for DQ development. Further work is needed to codify practices and metadata around both structural and semantic data quality.

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

BackgroundNational data from diverse institutions across the United States are critical for guiding policymakers as well as clinical and public health leaders. This study characterized a large national cohort of patients diagnosed with COVID-19 in the U.S., compared to patients diagnosed with viral pneumonia and influenza.Methods and FindingsWe captured cross-sectional information from 36 large healthcare systems in 29 U.S. states, participating in PCORnet®, the National Patient-Centered Clinical Research Network. Patients included were those diagnosed with COVID-19, viral pneumonia and influenza in any care setting, starting from January 1, 2020. Using distributed queries executed at each participating institution, we acquired information for patients on care setting (any, ambulatory, inpatient or emergency department, mechanical ventilator), age, sex, race, state, comorbidities (assessed with diagnostic codes), and medications used for treatment of COVID-19 (hydroxychloroquine with or without azithromycin; corticosteroids, anti-interleukin-6 agents).During this time period, 24,516 patients were diagnosed with COVID-19, with 42% in an emergency department or inpatient hospital setting; 79,639 were diagnosed with viral pneumonia (53% inpatient/ED) and 163,984 with influenza (41% inpatient/ED). Among COVID-19 patients, 68% were 20 to Conclusion and RelevancePCORnet leverages existing data to capture information on one of the largest U.S. cohorts to date of patients diagnosed with COVID-19 compared to patients diagnosed with viral pneumonia and influenza.

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.

In most electronic health record (EHR) systems, clinicians record diagnoses using interface terminologies, such as Intelligent Medical Objects (IMO). When extracting data from EHRs for collaborative research, local codes are often transformed to standard terminologies for consistent analyses despite the potential for loss of fidelity. EHR diagnosis codes may be standardized directly during the Extract-Transform-Load (ETL) process to the “Meaningful Use” clinical data exchange standard, SNOMED-CT, or to the International Classification of Diseases (ICD) terminologies commonly used for billing. We examined the performance of ETL standardization via the direct IMO mapping to SNOMED-CT, and via IMO mapping to ICD-9-CM or ICD-10-CM followed by UMLS mapping to SNOMED-CT. We found that for both ICD-9-CM and ICD-10-CM, only 24-27% of diagnosis codes map to the same SNOMED-CT code selected by the direct IMO-SNOMED crosswalk. We identified that differences in mapping lead to loss in the granularity and laterality of the initial diagnosis.

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.

Prior studies of early antibiotic use and growth have shown mixed results, primarily on cross-sectional outcomes. This study examined the effect of oral antibiotics before age 24 months on growth trajectory at age 2-5 years.We captured oral antibiotic prescriptions and anthropometrics from electronic health records through PCORnet, for children with ≥1 height and weight at 0-12 months of age, ≥1 at 12-30 months, and ≥2 between 25 and 72 months. Prescriptions were grouped into episodes by time and by antimicrobial spectrum. Longitudinal rate regression was used to assess differences in growth rate from 25 to 72 months of age. Models were adjusted for sex, race/ethnicity, steroid use, diagnosed asthma, complex chronic conditions, and infections.430,376 children from 29 health U.S. systems were included, with 58% receiving antibiotics before 24 months. Exposure to any antibiotic was associated with an average 0.7% (95% CI 0.5, 0.9, p 0.0001) greater rate of weight gain, corresponding to 0.05 kg additional weight. The estimated effect was slightly greater for narrow-spectrum (0.8% [0.6, 1.1]) than broad-spectrum (0.6% [0.3, 0.8], p 0.0001) drugs. There was a small dose response relationship between the number of antibiotic episodes and weight gain.Oral antibiotic use prior to 24 months of age was associated with very small changes in average growth rate at ages 2-5 years. The small effect size is unlikely to affect individual prescribing decisions, though it may reflect a biologic effect that can combine with others.

Background: Clinical data research networks (CDRNs) aggregate electronic health record data from multiple hospitals to enable large-scale research. A critical operation toward building a CDRN is conducting continual evaluations to optimize data quality. The key challenges include determining the assessment coverage on big datasets, handling data variability over time, and facilitating communication with data teams. This study presents the evolution of a systematic workflow for data quality assessment in CDRNs.Implementation: Using a specific CDRN as use case, the workflow was iteratively developed and packaged into a toolkit. The resultant toolkit comprises 685 data quality checks to identify any data quality issues, procedures to reconciliate with a history of known issues, and a contemporary GitHub-based reporting mechanism for organized tracking.Results: During the first two years of network development, the toolkit assisted in discovering over 800 data characteristics and resolving over 1400 programming errors. Longitudinal analysis indicated that the variability in time to resolution (15day mean, 24day IQR) is due to the underlying cause of the issue, perceived importance of the domain, and the complexity of assessment.Conclusions: In the absence of a formalized data quality framework, CDRNs continue to face challenges in data management and query fulfillment. The proposed data quality toolkit was empirically validated on a particular network, and is publicly available for other networks. While the toolkit is user-friendly and effective, the usage statistics indicated that the data quality process is very time-intensive and sufficient resources should be dedicated for investigating problems and optimizing data for research.

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.

Key Points Question Is early antibiotic exposure associated with altered body mass index (BMI) trajectory milestone patterns in childhood? Findings This cohort study of 183 444 children identified small associations with age and magnitude of BMI at peak in infancy and rebound in early childhood after antibiotic exposure before 48 months of age. Meaning The findings of this study suggest these small associations with BMI trajectory milestones after early-life antibiotic exposure should not affect the individual decision to prescribe antibiotics., Importance Past studies have showed associations between antibiotic exposure and child weight outcomes. Few, however, have documented alterations to body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) trajectory milestone patterns during childhood after early-life antibiotic exposure. Objective To examine the association of antibiotic use during the first 48 months of life with BMI trajectory milestones during childhood in a large cohort of children. Design, Setting, and Participants This retrospective cohort study used electronic health record data from 26 institutions participating in the National Patient-Centered Clinical Research Network from January 1, 2009, to December 31, 2016. Participant inclusion required at least 1 valid set of same-day height and weight measurements at each of the following age periods: 0 to 5, 6 to 11, 12 to 23, 24 to 59, and 60 to 131 months (183 444 children). Data were analyzed from June 1, 2019, to June 30, 2020. Exposures Antibiotic use at 0 to 5, 6 to 11, 12 to 23, 24 to 35, and 36 to 47 months of age. Main Outcomes and Measures Age and magnitude of BMI peak and BMI rebound. Results Of 183 444 children in the study (mean age, 3.3 years [range, 0-10.9 years]; 95 228 [51.9%] were boys; 80 043 [43.6%] were White individuals), 78.1% received any antibiotic, 51.0% had at least 1 episode of broad-spectrum antibiotic exposure, and 65.0% had at least 1 episode of narrow-spectrum antibiotic exposure at any time before 48 months of age. Exposure to any antibiotics at 0 to 5 months of age (vs no exposure) was associated with later age (β coefficient, 0.05 months [95% CI, 0.02-0.08 months]) and higher BMI (β coefficient, 0.09 [95% CI, 0.07-0.11]) at peak. Exposure to any antibiotics at 0 to 47 months of age (vs no exposure) was associated with an earlier age (−0.60 months [95% CI, −0.81 to −0.39 months]) and higher BMI at rebound (β coefficient, 0.02 [95% CI, 0.01-0.03]). These associations were strongest for children with at least 4 episodes of antibiotic exposure. Effect estimates for associations with age at BMI rebound were larger for those exposed to antibiotics at 24 to 35 months of age (β coefficient, −0.63 [95% CI, −0.83 to −0.43] months) or 36 to 47 (β coefficient, −0.52 [95% CI, −0.72 to −0.31] months) than for those exposed at 0 to 5 months of age (β coefficient, 0.26 [95% CI, 0.01-0.51] months) or 6 to 11 (β coefficient, 0.00 [95% CI, −0.20 to 0.20] months). Conclusions and Relevance In this cohort study, antibiotic exposure was associated with statistically significant, but small, differences in BMI trajectory milestones in infancy and early childhood. The small risk of an altered BMI trajectory milestone pattern associated with early-life antibiotic exposure is unlikely to be a key factor during prescription decisions for children., This cohort study uses data from the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics Childhood Growth Study to examine the association of antibiotic use during the first 4 years of life and body mass index trajectory milestones during childhood in a large cohort of children.

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