The Cytochrome P450 (CYP) 2C8*3 Variant (rs11572080) is Associated with Improved Asthma Symptom Control in Children and Altered Lipid Mediator Production and Inflammatory Response in Human Bronchial Epithelial Cells.

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 physiological 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 vs. 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 (LA) 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, AUDA. Interestingly, 9(10)- and 12(13)-DiHOME, the hydrolyzed metabolites of 9(10)- and 12(13)-EpOME, 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 EpOME:DiHOME ratio leading to a change in cellular inflammatory responses elicited by environmental stimuli that exacerbate asthma.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

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.

Implementation of a Weight-Based High-Flow Nasal Cannula Protocol for Children With Bronchiolitis.

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.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.

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.

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis.

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.

Impact of a self-monitoring application on pediatric asthma disparities.

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.

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis.

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.

Contextual Factors Influencing Implementation of Evidence-Based Care for Children Hospitalized With Asthma.

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.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis.

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.

Ambulatory Management of Childhood Asthma Using a Novel Self-management Application.

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.

Severe Facial Herpes Vegetans and Viremia in NFKB2-Deficient Common Variable Immunodeficiency.

With the accessibility of next-generation sequencing modalities, an increasing number of primary immunodeficiency disorders (PIDDs) such as common variable immunodeficiency (CVID) have gained improved understanding of molecular pathogenesis and disease phenotype with the identification of a genetic etiology. We report a patient with early-onset CVID due to an autosomal dominant loss-of-function mutation in NFKB2 who developed a severe herpes vegetans cutaneous infection as well as concurrent herpes simplex virus viremia. The case highlights features of CVID, unique aspects of NF-κB2 deficiency including susceptibility to herpesvirus infections, the detection of neutralizing anticytokine antibodies, and the complexity of medical management of patients with a PIDD that can be aided by a known genetic diagnosis.

Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis.

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.

Information needs for designing a home monitoring system for children with medical complexity.

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.

Appropriateness of Hospital Admission for Emergency Department Patients with Bronchiolitis: Secondary Analysis.

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.

Neighborhood Deprivation and Childhood Asthma Outcomes, Accounting for Insurance Coverage.

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 (ß: 1.171; 95% confidence interval [CI]: 1.117-1.228; P < .001), higher cost (ß: 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.

Physicians' pharmacogenomics information needs and seeking behavior: a study with case vignettes.

BACKGROUND: Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. We investigated physicians' pharmacogenomics information needs and information-seeking behavior, in order to guide the design of pharmacogenomics information resources that effectively meet clinical information needs. METHODS: We performed a formative, mixed-method assessment of physicians' information-seeking process in three pharmacogenomics case vignettes. Interactions of 6 physicians' with online pharmacogenomics resources were recorded, transcribed, and analyzed for prominent themes. Quantitative data included information-seeking duration, page navigations, and number of searches entered. RESULTS: We found that participants searched an average of 8 min per case vignette, spent less than 30 s reviewing specific content, and rarely refined search terms. Participants' information needs included a need for clinically meaningful descriptions of test interpretations, a molecular basis for the clinical effect of drug variation, information on the logistics of carrying out a genetic test (including questions related to cost, availability, test turn-around time, insurance coverage, and accessibility of expert support).Also, participants sought alternative therapies that would not require genetic testing. CONCLUSION: This study of pharmacogenomics information-seeking behavior indicates that content to support their information needs is dispersed and hard to find. Our results reveal a set of themes that information resources can use to help physicians find and apply pharmacogenomics information to the care of their patients.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

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.

Direct concurrent comparison of multiple pediatric acute asthma scoring instruments.

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.

Characterization of Transient Receptor Potential Vanilloid-1 (TRPV1) Variant Activation by Coal Fly Ash Particles and Associations with Altered Transient Receptor Potential Ankyrin-1 (TRPA1) Expression and Asthma.

Transient receptor potential (TRP) channels are activated by environmental particulate materials. We hypothesized that polymorphic variants of transient receptor potential vanilloid-1 (TRPV1) would be uniquely responsive to insoluble coal fly ash compared with the prototypical soluble agonist capsaicin. Furthermore, these changes would manifest as differences in lung cell responses to these agonists and perhaps correlate with changes in asthma symptom control. The TRPV1-I315M and -T469I variants were more responsive to capsaicin and coal fly ash. The I585V variant was less responsive to coal fly ash particles due to reduced translation of protein and an apparent role for Ile-585 in activation by particles. In HEK-293 cells, I585V had an inhibitory effect on wild-type TRPV1 expression, activation, and internalization/agonist-induced desensitization. In normal human bronchial epithelial cells, IL-8 secretion in response to coal fly ash treatment was reduced for cells heterozygous for TRPV1-I585V. Finally, both the I315M and I585V variants were associated with worse asthma symptom control with the effects of I315M manifesting in mild asthma and those of the I585V variant manifesting in severe, steroid-insensitive individuals. This effect may be due in part to increased transient receptor potential ankyrin-1 (TRPA1) expression by lung epithelial cells expressing the TRPV1-I585V variant. These findings suggest that specific molecular interactions control TRPV1 activation by particles, differential activation, and desensitization of TRPV1 by particles and/or other agonists, and cellular changes in the expression of TRPA1 as a result of I585V expression could contribute to variations in asthma symptom control.

Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods.

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.