This study investigated an association between the cytochrome P450 (CYP) 2C8*3 polymorphism with asthma symptom control in children and changes in lipid metabolism and pro-inflammatory signaling by human bronchial epithelial cells (HBECs) treated with cigarette smoke condensate (CSC). CYP genes are inherently variable in sequence, and while such variations are known to produce clinically relevant effects on drug pharmacokinetics and pharmacodynamics, the effects on endogenous substrate metabolism and associated physiologic processes are less understood. In this study, CYP2C8*3 was associated with improved asthma symptom control among children: Mean asthma control scores were 3.68 (n = 207) for patients with one or more copies of the CYP2C8*3 allele versus 4.42 (n = 965) for CYP2C8*1/*1 (P = 0.0133). In vitro, CYP2C8*3 was associated with an increase in montelukast 36- hydroxylation and a decrease in linoleic acid metabolism despite lower mRNA and protein expression. Additionally, CYP2C8*3 was associated with reduced mRNA expression of interleukin-6 (IL-6) and C-X-C motif chemokine ligand 8 (CXCL-8) by HBECs in response to CSC, which was replicated using the soluble epoxide hydrolase inhibitor, 12-[[(tricyclo[3.3.1.13,7]dec-1-ylamino)carbonyl]amino]-dodecanoic acid. Interestingly, 9(10)- and 12(13)- dihydroxyoctadecenoic acid, the hydrolyzed metabolites of 9(10)- and 12(13)- epoxyoctadecenoic acid, increased the expression of IL-6 and CXCL-8 mRNA by HBECs. This study reveals previously undocumented effects of the CYP2C8*3 variant on the response of HBECs to exogenous stimuli. SIGNIFICANCE STATEMENT These findings suggest a role for CYP2C8 in regulating the epoxyoctadecenoic acid:dihydroxyoctadecenoic acid ratio leading to a change in cellular inflammatory responses elicited by environmental stimuli that exacerbate asthma.

BACKGROUND AND OBJECTIVES: Viral bronchiolitis is a common pediatric illness. Treatment is supportive; however, some children have concurrent serious bacterial infections (cSBIs) requiring antibiotics. Identifying children with cSBI is challenging and may lead to unnecessary treatment. Improved understanding of the prevalence of and risk factors for cSBI are needed to guide treatment. We sought to determine the prevalence of cSBI and identify factors associated with cSBI in children hospitalized with bronchiolitis. METHODS: We performed a retrospective cohort study of children <2 years old hospitalized with bronchiolitis at a free-standing children’s hospital from 2012 to 2019 identified by International Classification of Diseases codes. cSBI was defined as bacteremia, urinary tract infection, meningitis, or pneumonia. Risk factors for cSBI were identified using logistic regression. RESULTS: We identified 7871 admissions for bronchiolitis. At least 1 cSBI occurred in 4.2% of these admissions; with 3.5% meeting our bacterial pneumonia definition, 0.4% bacteremia, 0.3% urinary tract infection, and 0.02% meningitis. cSBI were more likely to occur in children with invasive mechanical ventilation (odds ratio [OR] 2.53, 95% confidence interval [CI] 1.78–3.63), a C-reactive protein ≥4 mg/dL (OR 2.20, 95% CI 1.47–3.32), a concurrent complex chronic condition (OR 1.67, 95% CI 1.22–2.25) or admission to the PICU (OR 1.46, 95% CI 1.02–2.07). CONCLUSIONS: cSBI is uncommon among children hospitalized with bronchiolitis, with pneumonia being the most common cSBI. Invasive mechanical ventilation, elevated C-reactive protein, presence of complex chronic conditions, and PICU admission were associated with an increased risk of cSBI.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Genetic variation among inhaled corticosteroid (ICS)-metabolizing enzymes may affect asthma control, but evidence is limited. This study tested the hypothesis that single-nucleotide polymorphisms (SNPs) in Cytochrome P450 3A5 (CYP3A5) would affect asthma outcomes. Patients aged 2–18 years with persistent asthma were recruited to use the electronic AsthmaTracker (e-AT), a self-monitoring tool that records weekly asthma control, medication use, and asthma outcomes. A subset of patients provided saliva samples for SNP analysis and participated in a pharmacokinetic study. Multivariable regression analysis adjusted for age, sex, race, and ethnicity was used to evaluate the impact of CYP3A5 SNPs on asthma outcomes, including asthma control (measured using the asthma symptom tracker, a modified version of the asthma control test or ACT), exacerbations, and hospital admissions. Plasma corticosteroid and cortisol concentrations post-ICS dosing were also assayed using liquid chromatography–tandem mass spectrometry. Of the 751 patients using the e-AT, 166 (22.1%) provided saliva samples and 16 completed the PK study. The e-AT cohort was 65.1% male, and 89.6% White, 6.0% Native Hawaiian, 1.2% Black, 1.2% Native American, 1.8% of unknown race, and 15.7% Hispanic/Latino; the median age was 8.35 (IQR: 5.51–11.3) years. CYP3A5*3/*3 frequency was 75.8% in White subjects, 50% in Native Hawaiians and 76.9% in Hispanic/Latino subjects. Compared with CYP3A5*3/*3, the CYP3A5*1/*x genotype was associated with reduced weekly asthma control (OR: 0.98; 95% CI: 0.97–0.98; p < 0.001), increased exacerbations (OR: 6.43; 95% CI: 4.56–9.07; p < 0.001), and increased asthma hospitalizations (OR: 1.66; 95% CI: 1.43–1.93; p < 0.001); analysis of 3/*3, *1/*1 and *1/*3 separately showed an allelic copy effect. Finally, PK analysis post-ICS dosing suggested muted changes in cortisol concentrations for patients with the CYP3A5*3/*3 genotype, as opposed to an effect on ICS PK. Detection of CYP3A5*3/3, CYPA35*1/*3, and CYP3A5*1/*1 could impact inhaled steroid treatment strategies for asthma in the future.

OBJECTIVES:High concern about child's health is a common reason parents of children with medical complexity (CMC) seek care in emergency departments and hospitals. Factors driving parental concern are unknown. This study explores associations of parent's sociodemographic and child's clinical factors with high parental concern.PATIENT AND METHODS:Secondary analysis of a pilot study of CMC and parents who used daily for 3 months MyChildCMC, a home monitoring app. Parents recorded their child's vital signs (temperature, heart rate, respiratory rate, oximetry), symptoms (pain, seizures, fluid intake/feeding, mental status), and oxygen use, and received immediate feedback. Parents rated their child's health concern on a 4-point Likert scale. Concern scores were dichotomized (3-4 = high, 1-2 = low) and modeled in a mixed-effects logistic regression to explore important associations.RESULTS:We analyzed 1223 measurements from 24 CMC/parents, with 113 (9.24%) instances of high concern. Child factors associated with high parental concern were increased pain (odds ratio [OR], 5.10; 95% confidence interval [CI], 2.53-10.29; P < .01), increased oxygen requirement (OR, 28.91; 95% CI, 10.07-82.96; P < .01), reduced nutrition/fluid intake (OR, 71.58; 95% CI, 13.01-393.80; P < .01), and worsened mental status (OR, 2.15; 95% CI, 1.10-4.17, P = .02). No other associations existed.CONCLUSIONS:Changes in CMC's clinical parameters were associated with high concern, which may be an early indicator of acute illness in CMC when it is the primary complaint. Monitoring and responding to high parental concerns may support CMC care at home.

Abstract Background The Meningitis/Encephalitis FilmArray® Panel (ME panel) was approved by the U.S. Food and Drug Administration in 2015 and provides rapid results when assessing patients with suspected meningitis or encephalitis. These patients are evaluated by various subspecialties including pediatric hospital medicine (PHM), pediatric emergency medicine (PEM), pediatric infectious diseases, and pediatric intensive care unit (PICU) physicians. The objective of this study was to evaluate the current use of the ME panel and describe the provider and subspecialty practice variation. Methods We conducted an online cross-sectional survey via the American Academy of Pediatrics Section of Hospital Medicine (AAP-SOHM) ListServe, Brown University PEM ListServe, and PICU Virtual pediatric system (VPS) Listserve. Results A total of 335 participants out of an estimated 6998 ListServe subscribers responded to the survey. 68% reported currently using the ME panel at their institutions. Among test users, most reported not having institutional guidelines on test indications (75%) or interpretation (76%). 58% of providers self-reported lack of knowledge of the test’s performance characteristics. Providers from institutions that have established guidelines reported higher knowledge compared to those that did not (51% vs. 38%; p = 0.01). More PHM providers reported awareness of ME panel performance characteristics compared to PEM physicians (48% vs. 27%; p = 0.004); confidence in test interpretation was similar between both groups (72 vs. 69%; p = 0.80). Conclusion Despite the widespread use of the ME panel, few providers report having institutional guidelines on test indications or interpretation. There is an opportunity to provide knowledge and guidance about the ME panel among various pediatric subspecialties.

Background Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. Objective To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. Methods We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. Results We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. Conclusions Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. International Registered Report Identifier (IRRID) PRR1-10.2196/27065

OBJECTIVESTo determine if the implementation of a weight-based high-flow nasal cannula (HFNC) protocol for infants with bronchiolitis was associated with improved outcomes, including decreased ICU use.METHODSWe implemented a weight-based HFNC protocol across a tertiary care children's hospital and 2 community hospitals that admit pediatric patients on HFNC. We included all patients who were <2 years old and had a discharge diagnosis of bronchiolitis or viral pneumonia during the preimplementation (November 2013 to April 2018) and postimplementation (November 2018 to April 2020) respiratory seasons. Data were analyzed by using an interrupted time series approach. The primary outcome measure was the proportion of patients treated in the ICU. Patients with a complex chronic condition were excluded.RESULTSImplementation of the weight-based HFNC protocol was associated with an immediate absolute decrease in ICU use of 4.0%. We also observed a 6.2% per year decrease in the slope of ICU admissions pre- versus postintervention. This was associated with an immediate reduction in median cost per bronchiolitis encounter of $661, a 2.3% immediate absolute reduction in the proportion of patients who received noninvasive ventilation, and a 3.4% immediate absolute reduction in the proportion of patients who received HFNC.CONCLUSIONSA multicenter, weight-based HFNC protocol was associated with decreased ICU use and noninvasive ventilation use. In hospitals where HFNC is used in non-ICU units, weight-based approaches may lead to improved resource use.

BACKGROUND AND OBJECTIVES:To investigate caregivers' perceptions of physician communication and hospital ratings for hospitalized children with and without autism and assess associations between perceived quality of physician communication and overall ratings of the hospital.METHODS:We studied survey data from caregivers of 543 patients with autism compared with a 2:1 matched control sample of 1086 patients with similar characteristics but without autism from a single children's hospital. We analyzed survey items related to physician communication and hospital ratings from the Consumer Assessment of Healthcare Physicians and Systems Child Hospital Survey. We constructed multivariable regression models to examine the relationship between caregiver-perceived physician communication and caregivers' overall ratings of the hospital.RESULTS:A similar proportion of caregivers of children with and without autism reported that doctors "always" listened carefully to them (71.4% vs 74.3%; adjusted prevalence ratio 0.96; 95% confidence interval 0.90-1.03) and "always" treated them with respect (80.0% vs 84.1%; adjusted prevalence ratio 0.95; 95% confidence interval 0.90-1.00). Caregivers of children with autism were less likely to "definitely" recommend the hospital (87.0% vs 92.3%; adjusted prevalence ratio 0.94; 95% confidence interval 0.91-0.98). All items related to caregiver-perceived physician communication were associated with the highest hospital rating and the highest recommendation of the hospital in both groups.CONCLUSIONS:When matched on the basis of medical and social factors, caregivers of children with and without autism reported similar frequencies of highest-quality physician communication. Improvement of physician communication with caregivers of medically and socially complex children with and without autism may improve caregivers' overall ratings of the hospital.

OBJECTIVES:Mobile apps are suggested for supporting home monitoring and reducing emergency department (ED) visits and hospitalizations for children with medical complexity (CMC). None have been implemented. We sought to assess the MyChildCMC app (1) feasibility for CMC home monitoring, (2) ability to detect early deteriorations before ED and hospital admissions, and (3) preliminary impact.METHODS:Parents of CMC (aged 1-21 years) admitted to a children's hospital were randomly assigned to MyChildCMC or usual care. MyChildCMC subjects recorded their child's vital signs and symptoms daily for 3 months postdischarge and received real-time feedback. Feasibility measures included parent's enrollment, retention, and engagement. The preliminary impact was determined by using quality of life, parent satisfaction with care, and subsequent ED and hospital admissions and hospital days.RESULTS:A total of 62 parents and CMC were invited to participate: 50 enrolled (80.6% enrollment rate) and were randomly assigned to MyChildCMC (n = 24) or usual care (n = 26). Retention at 1 and 3 months was 80% and 74%, and engagement was 68.3% and 62.6%. Run-chart shifts in vital signs were common findings preceding admissions. The satisfaction score was 26.9 in the MyChildCMC group and 24.1 in the control group (P = .035). No quality of life or subsequent admission differences occurred between groups. The 3-month hospital days (pre-post enrollment) decreased from 9.25 to 4.54 days (rate ratio = 0.49; 95% confidence interval = 0.39-0.62; P < .001) in the MyChildCMC group and increased from 1.08 to 2.46 days (rate ratio = 2.29; 95% confidence interval = 1.47-3.56; P < .001) in the control group.CONCLUSIONS:MyChildCMC was feasible and appears effective, with the potential to detect early deteriorations in health for timely interventions that might avoid ED and hospitalizations. A larger and definitive study of MyChildCMC's impact and sustainability is needed.

Medical stabilization in a hospital setting is often necessary for children and adolescents with disordered eating. However, the literature regarding treatment during this acute phase is not robust. This study investigates the outcomes of implementing a standardized multidisciplinary protocol emphasizing oral nutrition for youth admitted to a tertiary children's hospital for medical stabilization. The primary hypothesis was that this standardized approach would decrease the use of nasogastric (NG) feedings. This retrospective cohort analysis compared patients aged 7 to 18 years who were admitted to the hospital for malnutrition due to disordered eating in the 18 months postprotocol implementation to an identified cohort admitted in the 12 months prior to implementation. Statistical analysis utilizing ANOVA and a χ2 test was performed looking at differences pre- and postimplementation in NG tube placement, 6-month readmissions, weight gained during the hospitalization, and hospital length-of-stay (LOS). The preimplementation cohort (n = 29) and postimplementation cohort (n = 85) were similar in mean ages and insurance mix. There was a significant decrease in number of NG placements after implementation of the protocol. At the same time, the hospital LOS significantly increased. There was no difference in the readmission rate after implementation of the protocol nor the amount of weight gained during the implementation. Implementation of a standardized protocol that emphasized oral nutrition decreased the number of NG tube placements yet increased the length of hospital stay and resulted in no change in 6-month readmissions (or weight gain in the hospital). Overall readmission rates remain relatively high regardless of the precise intervention. Limitations to our study include the challenges of appropriately identifying the preimplementation cohort from the electronic health record. Future studies need to focus on the rates and types of outpatient follow-up, along with a better understanding of the variety of severity and diagnoses in youth presenting to the hospital with disordered eating.

Objectives: We previously reported improved outcomes after implementing the electronic-AsthmaTracker (e-AT), self-monitoring tool for children with asthma, at 11 ambulatory pediatric clinics. This study assesses e-AT adherence and impact across race/ethnicity subgroups. Study Design: Secondary data analysis of a prospective cohort study of children ages 2-17 years with persistent asthma, enrolled from January 2014 to December 2015 to use the e-AT for 1 year. Survival analysis was used to compare e-AT use adherence and generalized estimating equation models to compare outcomes pre and post e AT initiation, between race/ethnicity subgroups. Results: Data from 318 children with baseline measurements were analyzed: 76.4 % white, 11.3 % Hispanic, 7.8 % "other", and 4.4 % unknown race/ethnicity subgroups. Mean e-AT adherence was 82 % (95 %CI: 79-84 %, reference) for whites, 73 % (64-81 %, p = 0.025) for Hispanics, and 78 % (69-86 %, p = 0.373) for other minorities. Compared to whites, Cox proportional hazard ratio for study dropout risk was 2.14 (1.31-3.77, p = 0.001) for Hispanics and 0.95 (0.60-1.50, p = 0.834) for other minorities. Disparities existed at baseline, with lower QOL (74.9 vs 80.6; p = 0.025) and asthma control (18.4 vs 19.7; p = 0.027) among Hispanics, compared to whites. After e-AT initiation, disparities disappeared at 3 months for QOL (87.2 vs 90.5; p = 0.159) and asthma control (23.1 vs 22.4; p = 0.063), persisting until study end. Disparities also existed at baseline, with lower QOL (74.6 vs. 80.6; p = 0.042) and asthma control (18.2 vs. 19.7, p = 0.024) among "other" minorities, compared to whites, and disappeared at 3 months for QOL (92.7 vs. 90.5, p = 0.432) and asthma control (22.7 vs 22.4; p = 0.518), persisting until study end. Subgroup analysis was underpowered to detect a difference in oral steroid use or ED/hospital admissions. Conclusions: Our study shows improved asthma control and QOL among minorities and disparity elimination after e-AT implementation. Future adequately powered studies will explore the impact on oral steroid and ED/hospital use disparities.

BackgroundAs a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. ObjectiveThe goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. MethodsSecondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. ResultsThe model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. ConclusionsOur model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. International Registered Report Identifier (IRRID)RR2-10.2196/resprot.5039

Background Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. Objective This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. Methods Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. Results Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. Conclusions This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.

Abstract Background The ability to quickly recognize and diagnose meningitis is a critical component in its management. The use of the FilmArray Meningitis/Encephalitis (ME) panel has made pathogen identification easier and faster. Although literature exists regarding clinical and laboratory data in patients with bacterial meningitis, less is known about the characteristics of viral meningitis caused by viruses detected by the ME panel This study aims to compare differences in clinical and laboratory characteristics between viral and bacterial meningitis identified by the ME panel. Methods We conducted a multisite retrospective review of pediatric patients with cerebral spinal fluid (CSF) positive for viral or bacterial organisms from January 2015 - February 2019. Demographic, clinical, and lab characteristics were extracted from medical records. Frequencies and percentages are used to explain descriptive data. Fisher’s exact test was used for categorical data. The Mann-Whitney test was used for continuous data. Results Four hundred thirty-eight patients were included in the analysis with baseline characteristics found in Table 1. Among these, 345 and 93 patients tested positive for viral and bacterial organisms respectively. Viral organisms identified are presented in Figure 1. Patients with bacterial organisms were more likely to have a history of prematurity, a prior neurosurgical procedure, neurological symptoms at presentation, neurological complications, and death within 30 days of admission. The median serum white blood cell (WBC) count, C-reactive protein (CRP), procalcitonin, CSF WBC count, CSF neutrophil percentage, and CSF protein were significantly higher in patients with bacterial organisms compared to viral organisms (Table 2). Table 1: Baseline Characteristics of the Study Population Figure 1: Viral organisms identified. Table 2: Comparison of Laboratory Data by CSF Pathogen Group Conclusion Patients positive for bacterial meningitis were more likely to have higher markers of infection compared to patients with viral organisms. Patients with bacterial organisms were also more likely to have death within 30 days of admission and neurological symptoms and subsequent complications, although the small number of patients limits these conclusions. These findings may help clinicians utilize clinical and laboratory data in conjunction with a ME panel result. Disclosures All Authors: No reported disclosures

OBJECTIVE To compare hospital outcomes associated with commonly used antibiotic therapies for aspiration pneumonia in children with neurologic impairment (NI). DESIGN/METHODS A retrospective study of children with NI hospitalized with aspiration pneumonia at 39 children's hospitals in the Pediatric Health Information System database. Exposure was empiric antibiotic therapy classified by antimicrobial activity. Outcomes included acute respiratory failure, intensive care unit (ICU) transfer, and hospital length of stay (LOS). Multivariable regression evaluated associations between exposure and outcomes and adjusted for confounders, including medical complexity and acute illness severity. RESULTS In the adjusted analysis, children receiving Gram-negative coverage alone had two-fold greater odds of respiratory failure (odds ratio [OR] 2.15; 95% CI: 1.41-3.27), greater odds of ICU transfer (OR 1.80; 95% CI: 1.03-3.14), and longer LOS [adjusted rate ratio (RR) 1.28; 95% CI: 1.16-1.41] than those receiving anaerobic coverage alone. Children receiving anaerobic and Gram-negative coverage had higher odds of respiratory failure (OR 1.65; 95% CI: 1.19-2.28) than those receiving anaerobic coverage alone, but ICU transfer (OR 1.15; 95% CI: 0.73-1.80) and length of stay (RR 1.07; 95% CI: 0.98-1.16) did not statistically differ. For children receiving anaerobic, Gram-negative, and P. aeruginosa coverage, LOS was shorter (RR 0.83; 95% CI: 0.76-0.90) than those receiving anaerobic coverage alone; odds of respiratory failure and ICU transfer rates did not significantly differ. CONCLUSIONS Anaerobic therapy appears to be important in the treatment of aspiration pneumonia in children with NI. While Gram-negative coverage alone was associated with worse outcomes, its addition to anaerobic therapy may not yield improved outcomes.

BACKGROUND AND OBJECTIVES:The translation of research findings into routine care remains slow and challenging. We previously reported successful implementation of an asthma evidence-based care process model (EB-CPM) at 8 (1 tertiary care and 7 community) hospitals, leading to a high health care provider (HCP) adherence with the EB-CPM and improved outcomes. In this study, we explore contextual factors perceived by HCPs to facilitate successful EB-CPM implementation.METHODS:Structured and open-ended questions were used to survey HCPs (n = 260) including physicians, nurses, and respiratory therapists, about contextual factors perceived to facilitate EB-CPM implementation. Quantitative analysis was used to identify significant factors (correlation coefficient ≥0.5; P ≤ .05) and qualitative analysis to assess additional facilitators.RESULTS:Factors perceived by HCPs to facilitate EB-CPM implementation were related to (1) inner setting (leadership support, adequate resources, communication and/or collaboration, culture, and previous experience with guideline implementation), (2) intervention characteristics (relevant and applicable to the HCP's practice), (3) individuals (HCPs) targeted (agreement with the EB-CPM and knowledge of supporting evidence), and (4) implementation process (participation of HCPs in implementation activities, teamwork, implementation team with a mix of expertise and professional's input, and data feedback). Additional facilitators included (1) having appropriate preparation and (2) providing education and training.CONCLUSIONS:Multiple factors were associated with successful EB-CPM implementation and may be used by others as a guide to facilitate implementation and dissemination of evidence-based interventions for pediatric asthma and other chronic diseases in the hospital setting.

BACKGROUND AND OBJECTIVES:Pediatric ambulatory asthma control is suboptimal, reducing quality of life (QoL) and causing emergency department (ED) and hospital admissions. We assessed the impact of the electronic-AsthmaTracker (e-AT), a self-monitoring application for children with asthma.METHODS:Prospective cohort study with matched controls. Participants were enrolled January 2014 to December 2015 in 11 pediatric clinics for weekly e-AT use for 1 year. Analyses included: (1) longitudinal changes for the child (QoL, asthma control, and interrupted and missed school days) and parents (interrupted and missed work days and satisfaction), (2) comparing ED and hospital admissions and oral corticosteroid (OCS) use pre- and postintervention, and (3) comparing ED and hospital admissions and OCS use between e-AT users and matched controls.RESULTS:A total of 327 children and parents enrolled; e-AT adherence at 12 months was 65%. Compared with baseline, participants had significantly (P < .001) increased QoL, asthma control, and reduced interrupted and missed school and work days at all assessment times. Compared with 1 year preintervention, they had reduced ED and hospital admissions (rate ratio [RR]: 0.68; 95% confidence interval [CI]: 0.49-0.95) and OCS use (RR: 0.74; 95% CI: 0.61-0.91). Parent satisfaction remained high. Compared with matched controls, participants had reduced ED and hospital admissions (RR: 0.41; 95% CI: 0.22-0.75) and OCS use (RR: 0.65; 95% CI: 0.46-0.93).CONCLUSIONS:e-AT use led to high and sustained participation in self-monitoring and improved asthma outcomes. Dissemination of this care model has potential to broadly improve pediatric ambulatory asthma care.

Background Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues. Objective To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective. Methods By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians’ acceptance of early warnings and on perceived care plan quality. Results We are obtaining clinical and administrative datasets from 3 leading health care systems’ enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025. Conclusions Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases. International Registered Report Identifier (IRRID) PRR1-10.2196/13783

............................................................................................................................. 4 BACKGROUND ....................................................................................................................... 6 PARTICIPATION OF PATIENTS AND OTHER STAKEHOLDERS IN THE DESIGN AND CONDUCT OF RESEARCH AND DISSEMINATION .......................................................................................... 10 C.1. Parent/Stakeholder Engagement in the Current Proposal ........................................ 10 C.2. Methods to Identify/Recruit Parents/Stakeholders .................................................. 10 C.3. Engagement Frequency ............................................................................................. 11 C.4. Perceived or Measured Impact of Parent/Stakeholder Engagement ....................... 11 C.5. Study Rigor and Quality ............................................................................................. 12 C.6. Transparency of the Research Process ...................................................................... 13 C.7. Adoption of Research Evidence Into Practice ............................................................ 13 METHODS .......................................................................................................................... 14 D.1. Setting/Initial Design/Objectives ............................................................................... 14 D.2. Study Design Change/Updated Objectives ............................................................... 14 Figure 2. Study design and randomization procedures .................................................... 15 D.3. Forming the Study Cohort ......................................................................................... 16 D.4. Interventions ............................................................................................................. 18 D.5. Follow-up ................................................................................................................... 22 D.6. Study Outcomes ........................................................................................................ 23 D.7. Data Collection and Sources ...................................................................................... 23 D.8. Statistical Analysis ..................................................................................................... 25 D.9. OBJECTIVE 1: Assess the Effectiveness of the New Ambulatory Care Model ........... 28 D.9.1. Descriptive Analyses: .............................................................................................. 28 D.9.2. Randomized Comparisons Between Standard and Intensive e-AT Interventions: See Figure 2. ...................................................................................................................... 28 D.9.3. Five Nonrandomized Comparisons Between e-AT Interventions vs Usual Care ... 30 D.10. OBJECTIVE 2: Assess Whether the Effect on Child Outcomes Varies Across Parent Characteristics (High vs Low Education, Medicaid vs Private Insurance, Frequent vs Infrequent Users) .............................................................................................................. 34 D.11. OBJECTIVE 3: Determine Factors Associated With Sustained Parent Participation in Asthma Self-management ................................................................................................ 35 D.12. Strategies for Handling Missing Data ...................................................................... 36 D.13. Conduct of the Study ............................................................................................... 36 RESULTS ............................................................................................................................. 37