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Objectives: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization.Earlier identification of need for hospital-level care could triage patients more efficiently to high-or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma.Methods: Retrospective analysis of patients ages 2 to 18 years seen at two urban pediatric EDs with asthma exacerbation over 4 years. Asthma exacerbation was defined as receiving both albuterol and systemic corticosteroids. We included patient features, measures of illness severity available in triage, weather features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calculated for each model.Results: There were 29,392 patients included in the analyses: mean (AESD) age of 7.0 (AE4.2) years, 42% female, 77% non-Hispanic black, and 76% public insurance. The AUCs for each model were: decision tree 0.72 (95% confidence interval [CI] = 0.66-0.77), logistic regression 0.83 (95% CI = 0.82-0.83), random forests 0.82 (95% CI = 0.81-0.83), and gradient boosting machines 0.84 (95% CI = 0.83-0.85). In the lowest decile of risk, only 3% of patients required hospitalization; in the highest decile this rate was 100%. After patient vital signs and acuity, age and weight, followed by socioeconomic status (SES) and weather-related features, were the most important for predicting hospitalization.Conclusions: Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.

This is a prepublication version of an article that has undergone peer review and been accepted for publication but is not the final version of record. This paper may be cited using the DOI and date of access. This paper may contain information that has errors in facts, figures, and statements, and will be corrected in the final published version. The journal is providing an early version of this article to expedite access to this information. The American Academy of Pediatrics, the editors, and authors are not responsible for inaccurate information and data described in this version.

BACKGROUND: Firearms are the second leading cause of pediatric death in the United States. There is significant variation in firearm legislation at the state level. Recently, 3 state laws were associated with a reduction in overall deaths from firearms: universal background checks for firearm purchases, universal background checks for ammunition purchases, and identification requirement for firearms. We sought to determine if stricter firearm legislation at the state level is associated with lower pediatric firearm-related mortality. METHODS: This was a cross-sectional study in which we used 2011-2015 Web-based Injury Statistics Query and Reporting System and Census data. We measured the association of the (1) strictness of firearm legislation (gun law score) and (2) presence of the 3 aforementioned gun laws with pediatric firearm-related mortality. We performed negative binomial regression accounting for differences in state-level characteristics (population-based race and ethnicity, education, income, and gun ownership) to derive mortality rate ratios associated with a 10-point change in each predictor and predicted mortality rates. RESULTS: A total of 21 241 children died of firearm-related injuries during the 5-year period. States with stricter gun laws had lower rates of firearm-related pediatric mortality (adjusted incident rate ratio 0.96 [0.93-0.99]). States with laws requiring universal background checks for firearm purchase in effect for $5 years had lower pediatric firearm-related mortality rates (adjusted incident rate ratio 0.65 [0.46-0.90]). CONCLUSIONS: In this 5-year analysis, states with stricter gun laws and laws requiring universal background checks for firearm purchase had lower firearm-related pediatric mortality rates. These findings support the need for further investigation to understand the impact of firearm legislation on pediatric mortality.

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Rationale: Rhinovirus (RV) C can cause asymptomatic infection and respiratory illnesses ranging from the common cold to severe wheezing.Objectives: To identify how age and other individual-level factors are associated with susceptibility to RV-C illnesses.Methods: Longitudinal data from the COAST (Childhood Origins of Asthma) birth cohort study were analyzed to determine relationships between age and RV-C infections. Neutralizing antibodies specific for RV-A and RV-C (three types each) were determined using a novel PCR-based assay. Data were pooled from 14 study cohorts in the United States, Finland, and Australia, and mixed-effects logistic regression was used to identify factors related to the proportion of RV-C versus RV-A detection.Measurements and Main Results: In COAST, RV-A and RV-C infections were similarly common in infancy, whereas RV-C was detected much less often than RV-A during both respiratory illnesses and scheduled surveillance visits (P , 0.001, x 2 ) in older children. The prevalence of neutralizing antibodies to RV-A or RV-C types was low (5-27%) at the age of 2 years, but by the age of 16 years, RV-C seropositivity was more prevalent (78% vs. 18% for RV-A; P , 0.0001). In the pooled analysis, the RV-C to RV-A detection ratio during illnesses was significantly related to age (P , 0.0001), CDHR3 genotype (P , 0.05), and wheezing illnesses (P , 0.05). Furthermore, certain RV types (e.g., C2, C11, A78, and A12) were consistently more virulent and prevalent over time.Conclusions: Knowledge of prevalent RV types, antibody responses, and populations at risk based on age and genetics may guide the development of vaccines or other novel therapies against this important respiratory pathogen.

The rate of suicide among adolescents is rising in the US, yet many adolescents at risk are unidentified and receive no mental health services.OBJECTIVE To develop and independently validate a novel computerized adaptive screen for suicidal youth (CASSY) for use as a universal screen for suicide risk in medical emergency departments (EDs). DESIGN, SETTING, AND PARTICIPANTSStudy 1 of this prognostic study prospectively enrolled adolescent patients at 13 geographically diverse US EDs in the Pediatric Emergency Care Applied Research Network. They completed a baseline suicide risk survey and participated in 3-month telephone follow-ups. Using 3 fixed Ask Suicide-Screening Questions items as anchors and additional items that varied in number and content across individuals, we derived algorithms for the CASSY. In study 2, data were collected from patients at

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