Rationale: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Objectives: Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. Methods: We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP’s ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children’s asthma control over time and compared PP’s performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Results: Four phenotypes were discovered in both datasets: allergic not obese (A+/O−), obese not allergic (A−/O+), allergic and obese (A+/O+), and not allergic not obese (A−/O−). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A+/O+ children’s asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Conclusions: Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

Objective: To describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma.Materials and Methods: The Biomedical REAl-Time Health Evaluation (BREATHE) platform connects different sensors and data streams, contextualizing an individual’s symptoms and daily activities over time to understand pediatric asthma’s presentation and its management. A smartwatch/smartphone combination serves as a hub for personal/wearable sensing devices collecting data on health (eg, heart rate, spirometry, medications), motion, and personal exposures (eg, particulate matter, ozone); securely transmitting information to BREATHE’s servers; and interacting with the user (eg, ecological momentary assessments). Server-side integration of electronic health record data and spatiotemporally correlated information (eg, weather, traffic) elaborates on these observations. An initial panel study involving pediatric asthma patients was conducted to assess BREATHE.Results: Twenty subjects were enrolled, during which BREATHE accrued seven consecutive days of continuous data per individual. The data were used to confirm knowledge about asthma (use of controller inhalers, time-activity behaviors, personal air pollution exposure), and additional analyses provided insights into within-day associations of environmental triggers and asthma exacerbations. Exit surveys focusing on mHealth usability, while positive, noted several translational challenges.Discussion: Based on these promising results, a longitudinal panel study to evaluate individual microenvironments and exposures is ongoing. Lessons learned thus far reflect the need to address various usability aspects, including convenience and ongoing engagement.Conclusion: BREATHE enables multi-sensor mHealth studies, capturing new types of information alongside an evolving understanding of personal exposomes.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8–16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM_2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM_2.5, nitrogen dioxide (NO₂), ozone (O₃)) and traffic-related (nitrogen oxides (NO_x) and PM_2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV₁) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4–16.8) with mean (SD) morning %predicted FEV₁ of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O₃ exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV₁ (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM_2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of “feeling scared of trouble breathing” symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O₃, NO_x, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O₃ 1.52 [1.02, 2.27], NO_x 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and “feeling scared of trouble breathing.” Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

Objectives: Pediatric asthma is heterogeneous with phenotypes that reflect differing underlying inflammation and pathophysiology. Little is known about the national prevalence of certain obesity- and allergy-related asthma phenotypes or associated characteristics. We therefore assessed the national prevalence, risk factors, and caregiver-reported severity of four asthma phenotypes: not-allergic-not-obese, allergic-not-obese, obese-not-allergic, and allergic-and-obese. Methods: We analyzed data from the 2007–2008 National Survey of Children's Health (NSCH) of 10–17 year-olds with caregiver-reported asthma. We described sociodemographic and health risk factors of each phenotype and then applied logistic and ordinal regression models to identify associated risk factors and level of severity of the phenotypes. Results: Among 4427 children with asthma in this NSCH cohort, the association between race and phenotype was statistically significant (p < 0.0001); white children with asthma were most likely to have allergic-not-obese asthma while black and Hispanic children with asthma were most likely to have the obese-nonallergic phenotype (p < 0.001). Attention-deficit disorder/attention-deficit hyperactivity disorder was more likely to be present in allergic-not-obese children (odds ratio (OR) 1.50, confidence interval (CI) 1.14–1.98, p = 0.004). The phenotype with the highest risk for more severe compared to mild asthma was the obese-and-allergic asthma phenotype (OR 3.34, CI 2.23–5.01, p < 0.001). Conclusions: Allergic-not-obese asthma comprised half of our studied asthma phenotypes, while obesity-related asthma (with or without allergic components) comprised one-fifth of asthma phenotypes in this cohort representative of the US population. Children with both obese and allergic asthma are most likely to have severe asthma. Future management of childhood asthma might consider more tailoring of treatment and management plans based upon different childhood asthma phenotypes.