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Background: Accumulating evidence shows that peri-conceptional and in-utero exposures have lifetime health impacts for mothers and their offspring., Objectives: We conducted a Follow-Up Study of the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial with two objectives. First, we determined if women who enrolled at the Utah site (N = 1001) of the EAGeR trial (2007-2011, N = 1228) could successfully be contacted and agree to complete an online questionnaire on their reproductive, cardio-metabolic, and offspring respiratory health 9-14 years after original enrollment. Second, we evaluated if maternal exposure to low-dose aspirin (LDA) during pregnancy was associated with maternal cardio-metabolic health and offspring respiratory health., Methods: The original EAGeR study population included women, 18-40 years of age, who had 1-2 prior pregnancy losses, and who were trying to become pregnant. At follow-up (2020-2021), participants from the Utah cohort completed a 13-item online questionnaire on reproductive and cardio-metabolic health, and those who had a live birth during EAGeR additionally completed a 7-item questionnaire on the index child's respiratory health. Primary maternal outcomes included hypertension and hypercholesterolemia; primary offspring outcomes included wheezing and asthma., Results: Sixty-eight percent (n = 678) of participants enrolled in the follow-up study, with 10% and 15% reporting maternal hypertension and hypercholesterolemia, respectively; and 18% and 10% reporting offspring wheezing and asthma. We found no association between maternal LDA exposure and hypertension (risk difference [RD] -0.001, 95% confidence interval [CI] -0.05, 0.04) or hypercholesterolemia (RD -0.01, 95% CI -0.06, 0.05) at 9-14 years follow-up. Maternal LDA exposure was not associated with offspring wheezing (RD -0.002, 95% CI -0.08, 0.08) or asthma (RD 0.13, 95% CI 0.11, 0.37) at follow-up. Findings remained robust after considering potential confounding and selection bias., Conclusions: We observed no association between LDA exposure during pregnancy and maternal cardiometabolic or offspring respiratory health., (© 2024 The Author(s). Paediatric and Perinatal Epidemiology published by John Wiley & Sons Ltd.)

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., (Copyright © 2024 by The Author(s).)

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.

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., (©Flory L Nkoy, Bryan L Stone, Yue Zhang, Gang Luo. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.04.2024.)

Background: Descriptive epidemiological data on incidence rates (IRs) of asthma with recurrent exacerbations (ARE) are sparse., Objectives: This study hypothesized that IRs for ARE would vary by time, geography, age, and race and ethnicity, irrespective of parental asthma history., Methods: The investigators leveraged data from 17,246 children born after 1990 enrolled in 59 US with 1 Puerto Rican cohort in the Environmental Influences on Child Health Outcomes (ECHO) consortium to estimate IRs for ARE., Results: The overall crude IR for ARE was 6.07 per 1000 person-years (95% CI: 5.63-6.51) and was highest for children aged 2-4 years, for Hispanic Black and non-Hispanic Black children, and for those with a parental history of asthma. ARE IRs were higher for 2- to 4-year-olds in each race and ethnicity category and for both sexes. Multivariable analysis confirmed higher adjusted ARE IRs (aIRRs) for children born 2000-2009 compared with those born 1990-1999 and 2010-2017, 2-4 versus 10-19 years old (aIRR = 15.36; 95% CI: 12.09-19.52), and for males versus females (aIRR = 1.34; 95% CI 1.16-1.55). Black children (non-Hispanic and Hispanic) had higher rates than non-Hispanic White children (aIRR = 2.51; 95% CI 2.10-2.99; and aIRR = 2.04; 95% CI: 1.22-3.39, respectively). Children born in the Midwest, Northeast and South had higher rates than those born in the West (P < .01 for each comparison). Children with a parental history of asthma had rates nearly 3 times higher than those without such history (aIRR = 2.90; 95% CI: 2.43-3.46)., Conclusions: Factors associated with time, geography, age, race and ethnicity, sex, and parental history appear to influence the inception of ARE among children and adolescents., (Copyright © 2023 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Objectives: To 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., Methods: We 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., Results: Implementation 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., Conclusions: A 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., Competing Interests: POTENTIAL CONFLICTS OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2021 by the American Academy of Pediatrics.)

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., (©Gang Luo, Bryan L Stone, Xiaoming Sheng, Shan He, Corinna Koebnick, Flory L Nkoy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 18.05.2021.)

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., (©Gang Luo, Michael D Johnson, Flory L Nkoy, Shan He, Bryan L Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 31.12.2020.)

Objectives: We previously reported improved outcomes after implementing the electronic-AsthmaTracker (e-AT), a 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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: The workup and surveillance strategies for infant hydronephrosis (HN) vary, although this could be due to grade-dependent differences in imaging intensity. We aimed to describe the frequency of imaging studies for HN within the first year of life, stratified by initial HN grade, within a large regional healthcare system. Study Design and Data Source . Retrospective cohort using Intermountain Healthcare Data Warehouse. Inclusion criteria: (1) birth between 1/1/2005 and 12/31/2013, (2) CPT code for HN, and (3) ultrasound (U/S) confirmed HN within four months of birth. Data Collection . Grade of HN on initial postnatal U/S; number of HN-associated radiologic studies (renal U/Ss, voiding cystourethrograms (VCUGs), and diuretic renal scans); demographic and medical variables. Primary Outcome . Sum of radiologic studies within the first year of life or prior to pyeloplasty. Statistical Analysis . Multivariate poisson regression to analyze association between the primary outcome and the initial HN grade., Results: Of 1,380 subjects (993 males and 387 females), 990 (72%), 230 (17%), and 160 (12%) had mild, moderate, and severe HN, respectively. Compared with those with mild HN, patients with moderate (RR: 1.57; 95% CI: 1.42-1.73) and severe (RR: 2.09; 95% CI: 1.88-2.32) HN had a significantly higher rate of imaging use over 12 months (or prior to surgery) after controlling for potential confounders., Conclusions: In a large regional healthcare system, imaging use for HN is proportional to its initial grade. This suggests that within our system, clinicians treating this condition are using a risk-stratified approach to imaging., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Anthony J. Schaeffer et al.)

Background: As 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., Objective: The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients., Methods: Secondary 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., Results: The 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., Conclusions: Our 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., (©Gang Luo, Shan He, Bryan L Stone, Flory L Nkoy, Michael D Johnson. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 21.01.2020.)

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., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2019 by the American Academy of Pediatrics.)

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., (©Gang Luo, Bryan L Stone, Corinna Koebnick, Shan He, David H Au, Xiaoming Sheng, Maureen A Murtaugh, Katherine A Sward, Michael Schatz, Robert S Zeiger, Giana H Davidson, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 06.06.2019.)

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., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2019 by the American Academy of Pediatrics.)

Background Children with medical complexity (CMC) are a growing population of medically fragile children with unique healthcare needs, who have recurrent emergency department (ED) and hospital admissions due to frequent acute escalations of their chronic conditions. Mobile health (mHealth) tools have been suggested to support CMC home monitoring and prevent admissions. No mHealth tool has ever been developed for CMC and challenges exist. Objective To: 1) assess information needs for operationalizing CMC home monitoring, and 2) determine technology design functionalities needed for building a mHealth application for CMC. Methods Qualitative descriptive study conducted at a tertiary care children's hospital with a purposive sample of English-speaking caregivers of CMC. We conducted 3 focus group sessions, using semi-structured, open-ended questions. We assessed caregiver's perceptions of early symptoms that commonly precede acute escalations of their child conditions, and explored caregiver's preferences on the design functionalities of a novel mHealth tool to support home monitoring of CMC. We used content analysis to assess caregivers' experience concerning CMC symptoms, their responses, effects on caregivers, and functionalities of a home monitoring tool. Results Overall, 13 caregivers of CMC (ages 18 months to 19 years, mean = 9 years) participated. Caregivers identified key symptoms in their children that commonly presented 1-3 days prior to an ED visit or hospitalization, including low oxygen saturations, fevers, rapid heart rates, seizures, agitation, feeding intolerance, pain, and a general feeling of uneasiness about their child's condition. They believed a home monitoring system for tracking these symptoms would be beneficial, providing a way to identify early changes in their child's health that could prompt a timely and appropriate intervention. Caregivers also reported their own symptoms and stress related to caregiving activities, but opposed monitoring them. They suggested an mHealth tool for CMC to include the following functionalities: 1) symptom tracking, targeting commonly reported drivers (symptoms) of ED/hospital admissions; 2) user friendly (ease of data entry), using voice, radio buttons, and drop down menus; 3) a free-text field for reporting child's other symptoms and interventions attempted at home; 4) ability to directly access a health care provider (HCP) via text/email messaging, and to allow real-time sharing of child data to facilitate care, and 5) option to upload and post a photo or video of the child to allow a visual recall by the HCP. Conclusions Caregivers deemed a mHealth tool beneficial and offered a set of key functionalities to meet information needs for monitoring CMC's symptoms. Our future efforts will consist of creating a prototype of the mHealth tool and testing it for usability among CMC caregivers., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: In children below the age of 2 years, bronchiolitis is the most common reason for hospitalization. Each year in the United States, bronchiolitis causes 287,000 emergency department visits, 32%-40% of which result in hospitalization. Due to a lack of evidence and objective criteria for managing bronchiolitis, clinicians often make emergency department disposition decisions on hospitalization or discharge to home subjectively, leading to large practice variation. Our recent study provided the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis and showed that 6.08% of emergency department disposition decisions for bronchiolitis were inappropriate. An accurate model for predicting appropriate hospital admission can guide emergency department disposition decisions for bronchiolitis and improve outcomes, but has not been developed thus far., Objective: The objective of this study was to develop a reasonably accurate model for predicting appropriate hospital admission., Methods: Using Intermountain Healthcare data from 2011-2014, we developed the first machine learning classification model to predict appropriate hospital admission for emergency department patients with bronchiolitis., Results: Our model achieved an accuracy of 90.66% (3242/3576, 95% CI: 89.68-91.64), a sensitivity of 92.09% (1083/1176, 95% CI: 90.33-93.56), a specificity of 89.96% (2159/2400, 95% CI: 88.69-91.17), and an area under the receiver operating characteristic curve of 0.960 (95% CI: 0.954-0.966). We identified possible improvements to the model to guide future research on this topic., Conclusions: Our model has good accuracy for predicting appropriate hospital admission for emergency department patients with bronchiolitis. With further improvement, our model could serve as a foundation for building decision-support tools to guide disposition decisions for children with bronchiolitis presenting to emergency departments., International Registered Report Identifier (irrid): RR2-10.2196/resprot.5155., (©Gang Luo, Bryan L Stone, Flory L Nkoy, Shan He, Michael D Johnson. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.01.2019.)

Background: Bronchiolitis is the leading cause of hospitalization in children under 2 years of age. Each year in the United States, bronchiolitis results in 287,000 emergency department visits, 32%-40% of which end in hospitalization. Frequently, emergency department disposition decisions (to discharge or hospitalize) are made subjectively because of the lack of evidence and objective criteria for bronchiolitis management, leading to significant practice variation, wasted health care use, and suboptimal outcomes. At present, no operational definition of appropriate hospital admission for emergency department patients with bronchiolitis exists. Yet, such a definition is essential for assessing care quality and building a predictive model to guide and standardize disposition decisions. Our prior work provided a framework of such a definition using 2 concepts, one on safe versus unsafe discharge and another on necessary versus unnecessary hospitalization., Objective: The goal of this study was to determine the 2 threshold values used in the 2 concepts, with 1 value per concept., Methods: Using Intermountain Healthcare data from 2005-2014, we examined distributions of several relevant attributes of emergency department visits by children under 2 years of age for bronchiolitis. Via a data-driven approach, we determined the 2 threshold values., Results: We completed the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis. Appropriate hospital admissions include actual admissions with exposure to major medical interventions for more than 6 hours, as well as actual emergency department discharges, followed by an emergency department return within 12 hours ending in admission for bronchiolitis. Based on the definition, 0.96% (221/23,125) of the emergency department discharges were deemed unsafe. Moreover, 14.36% (432/3008) of the hospital admissions from the emergency department were deemed unnecessary., Conclusions: Our operational definition can define the prediction target for building a predictive model to guide and improve emergency department disposition decisions for bronchiolitis in the future., (©Gang Luo, Michael D Johnson, Flory L Nkoy, Shan He, Bryan L Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.11.2018.)

Objectives: Collecting social determinants data is challenging. We assigned patients a neighborhood-level social determinant measure, the area of deprivation index (ADI), by using census data. We then assessed the association between neighborhood deprivation and asthma hospitalization outcomes and tested the influence of insurance coverage., Methods: A retrospective cohort study of children 2 to 17 years old admitted for asthma at 8 hospitals. An administrative database was used to collect patient data, including hospitalization outcomes and neighborhood deprivation status (ADI scores), which were grouped into quintiles (ADI 1, the least deprived neighborhoods; ADI 5, the most deprived neighborhoods). We used multivariable models, adjusting for covariates, to assess the associations and added a neighborhood deprivation status and insurance coverage interaction term., Results: A total of 2270 children (median age 5 years; 40.6% girls) were admitted for asthma. We noted that higher ADI quintiles were associated with greater length of stay, higher cost, and more asthma readmissions ( P < .05 for most quintiles). Having public insurance was independently associated with greater length of stay (β: 1.171; 95% confidence interval [CI]: 1.117-1.228; P < .001), higher cost (β: 1.147; 95% CI: 1.093-1.203; P < .001), and higher readmission odds (odds ratio: 1.81; 95% CI: 1.46-2.24; P < .001). There was a significant deprivation-insurance effect modification, with public insurance associated with worse outcomes and private insurance with better outcomes across ADI quintiles ( P < .05 for most combinations)., Conclusions: Neighborhood-level ADI measure is associated with asthma hospitalization outcomes. However, insurance coverage modifies this relationship and needs to be considered when using the ADI to identify and address health care disparities., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2018 by the American Academy of Pediatrics.)

Objective: Appropriate delivery of Emergency Department (ED) treatment to children with acute asthma requires clinician assessment of acute asthma severity. Various clinical scoring instruments exist to standardize assessment of acute asthma severity in the ED, but their selection remains arbitrary due to few published direct comparisons of their properties. Our objective was to test the feasibility of directly comparing properties of multiple scoring instruments in a pediatric ED., Methods: Using a novel approach supported by a composite data collection form, clinicians categorized elements of five scoring instruments before and after initial treatment for 48 patients 2-18 years of age with acute asthma seen at the ED of a tertiary care pediatric hospital ED from August to December 2014. Scoring instruments were compared for inter-rater reliability between clinician types and their ability to predict hospitalization., Results: Inter-rater reliability between clinician types was not different between instruments at any point and was lower (weighted kappa range 0.21-0.55) than values reported elsewhere. Predictive ability of most instruments for hospitalization was higher after treatment than before treatment (p < 0.05) and may vary between instruments after treatment (p = 0.054)., Conclusions: We demonstrate the feasibility of comparing multiple clinical scoring instruments simultaneously in ED clinical practice. Scoring instruments had higher predictive ability for hospitalization after treatment than before treatment and may differ in their predictive ability after initial treatment. Definitive conclusions about the best instrument or meaningful comparison between instruments will require a study with a larger sample size.

Background: To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets., Objective: This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data., Methods: This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes., Results: We are currently writing Auto-ML's design document. We intend to finish our study by around the year 2022., Conclusions: Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes., (©Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 29.08.2017.)

Background: In young children, bronchiolitis is the most common illness resulting in hospitalization. For children less than age 2, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the United States, 287,000 emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32%-40%. Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital admission or discharge to home) are often made subjectively, resulting in significant practice variation. Studies reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited health care resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen outcomes. Existing clinical guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient level of detail, and do not account for differences in patient and illness characteristics including co-morbidities. Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and improve clinicians' decision making. Used in real time, predictive models can present objective criteria supported by historical data for an individualized disease management plan and guide admission decisions. However, existing predictive models for ED patients with bronchiolitis have limitations, including low accuracy and the assumption that the actual ED disposition decision was appropriate. To date, no operational definition of appropriate admission exists. No model has been built based on appropriate admissions, which include both actual admissions that were necessary and actual ED discharges that were unsafe., Objective: The goal of this study is to develop a predictive model to guide appropriate hospital admission for ED patients with bronchiolitis., Methods: This study will: (1) develop an operational definition of appropriate hospital admission for ED patients with bronchiolitis, (2) develop and test the accuracy of a new model to predict appropriate hospital admission for an ED patient with bronchiolitis, and (3) conduct simulations to estimate the impact of using the model on bronchiolitis outcomes., Results: We are currently extracting administrative and clinical data from the enterprise data warehouse of an integrated health care system. Our goal is to finish this study by the end of 2019., Conclusions: This study will produce a new predictive model that can be operationalized to guide and improve disposition decisions for ED patients with bronchiolitis. Broad use of the model would reduce iatrogenic risk, patient and parental distress, health care use, and costs and improve outcomes for bronchiolitis patients.

Background: Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models., Methods: A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality., Results: The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations., Conclusions: Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.

Background: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence., Methods: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead., Results: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic., Conclusions: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

Objectives: To determine how frequently physicians identify and address overweight/obesity in hospitalized children and to compare physician documentation across training level (medical student, intern, resident, attending)., Study Design: We conducted a retrospective chart review. Using an administrative database, Centers for Disease Control and Prevention body mass index calculator, and random sampling technique, we identified a study population of 300 children aged 2-18 years with overweight/obesity hospitalized on the general medical service of a tertiary care pediatric hospital. We reviewed admission, progress, and discharge notes to determine how frequently physicians and physician trainees identified (documented in history, physical exam, or assessment) and addressed (documented in hospital or discharge plan) overweight/obesity., Results: Physicians and physician trainees identified overweight/obesity in 8.3% (n = 25) and addressed it in 4% (n = 12) of 300 hospitalized children with overweight/obesity. Interns were most likely to document overweight/obesity in history (8.3% of the 266 patients they followed). Attendings were most likely to document overweight/obesity in physical examination (8.3%), assessment (4%), and plan (4%) of the 300 patients they followed. Medical students were least likely to document overweight/obesity including it in the assessment (0.4%) and plan (0.4%) of the 244 hospitalized children with overweight/obesity they followed., Conclusions: Physicians and physician trainees rarely identify or address overweight/obesity in hospitalized children. This represents a missed opportunity for both patient care and physician trainee education., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Evidence-based guidelines recommend smoking cessation treatment, including screening and counseling, for all smokers, including those with chronic diseases exacerbated by smoking. Physician treatment improves smoking cessation. Little data describes smoking treatment guideline uptake for patients with chronic cardiopulmonary smoking-sensitive diseases., Objective: Describe U.S. primary care physician (PCP) smoking cessation treatment during patient visits for chronic cardiopulmonary smoking-sensitive diseases., Methods: The National (Hospital) Ambulatory Medical Care Survey captured PCP visits. We examined smoking screening and counseling time trends for smokers with chronic diseases. Multivariable logistic regression assessed factors associated with smoking counseling for smokers with chronic smoking-sensitive diseases., Results: From 2001-2009 smoking screening and counseling for smokers with chronic smoking-sensitive cardiopulmonary diseases were unchanged. Among smokers with chronic smoking-sensitive diseases, 50%-72% received no counseling. Smokers with chronic obstructive pulmonary disease (COPD) (odds ratio (OR)=6.54, 95% confidence interval (CI) 4.85-8.83) and peripheral vascular disease (OR=4.50, 95% CI 1.72-11.75) were more likely to receive smoking counseling at chronic/preventive care visits, compared with patients without smoking-sensitive diseases. Other factors associated with increased smoking counseling included non-private insurance, preventive and longer visits, and an established PCP. Asthma and cardiovascular disease showed no association with counseling., Conclusions: Smoking cessation counseling remains infrequent for smokers with chronic smoking-sensitive cardiopulmonary diseases. New strategies are needed to encourage smoking cessation counseling., (Copyright © 2014 Elsevier Inc. All rights reserved.)