Predicting asthma exacerbations in children

Risk factors associated with uncontrolled asthma in children - a systematic review and meta-analysis

OBJECTIVE

We aim to assess the risk factors of uncontrolled asthma in children and adolescents.

METHODS

A systemic search was conducted from electronic databases (PubMed/Medline, Cochrane Library, and Google Scholar) from inception to July 17, 2023. All statistical analyses were conducted in Review Manager 5.4.1. Studies meeting inclusion criteria were selected. A random-effects model was used when heterogeneity was seen to pool the studies, and the result was reported in the odds ratio and the corresponding 95% confidence interval. We also used a narrative approach where it was not feasible to quantitatively assess the outcome.

RESULTS

Ten observational studies were used to conduct this systematic review and meta-analysis. A quantitative analysis of five factors was done. Pooled analysis showed a statistically significant risk of uncontrolled asthma in association with past hypersensitivity reactions (standardized mean difference [SMD] = 1.51 (1.16, 1.98); p = .002; I2 = 84%) and incomplete controller adherence (SMD = 3.15 (1.83, 5.41); p < .0001; I2 = 94%). While non-significant relation was seen in parental asthma (SMD = 1.23 (0.98, 1.55); p = .07; I2 = 15%), oral corticosteroid use (SMD = 0.99 (0.72, 1.36); p = .96; I2 = 81%) and education of caregivers (SMD = 0.99 (0.72, 1.36); p = .96; I2 = 81%). Some other factors were also discussed qualitatively.

CONCLUSION

Our study shows that some significant risk factors might cause uncontrolled asthma in children and adolescents like past hypersensitivity reactions and incomplete controller adherence.

Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model

OBJECTIVE

Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes.

METHODS

Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5-18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5-11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model.

RESULTS

The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications.

CONCLUSIONS

Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy.

Pediatric asthma exacerbation and COVID-19 pandemic: Impacts, challenges, and future considerations

OBJECTIVE

Asthma, a common disease among children and adolescents, poses a great health risk when ignored; therefore, a thorough follow-up to prevent exacerbations is emphasized. The aim of the present study is to investigate asthma exacerbation in children during the Coronavirus disease 2019 (COVID-19) era.

DATA SOURCES

This narrative review has been done by searching the PubMed and Embase databases using Asthma, COVID-19, Pandemic, and Symptom flare up as keywords.

STUDY SELECTIONS

Studies related to asthma exacerbation in COVID-19 pandemic were included.

RESULTS

Based on studies, controlled or mild to moderate asthma has not been considered a risk factor for COVID-19 severity and has not affected hospitalization, intensive care unit (ICU) admission, and mortality. Surprisingly, emergent and non-emergent visits and asthmatic attacks decreased during the pandemic. The three main reasons for decreased incidence and exacerbation of asthma episodes in the COVID-19 era included reduced exposure to environmental allergens, increasing the acceptance of treatment by pediatrics and caregivers, and decreased risk of other respiratory viral infections. Based on the available studies, COVID-19 vaccination had no serious side effects, except in cases of uncontrolled severe asthma, and can be injected in these children. Also, there was no conclusive evidence of asthma exacerbation after the injection of COVID-19 vaccines.

CONCLUSION

Further studies are recommended to follow the pattern of asthma in the post-pandemic situation and to become prepared for similar future conditions.

Relationship between air quality and asthma-related emergency hospital admissions in Mexico City 2017-2019

BACKGROUND

Previous studies found exposure to air pollution leads to exacerbations of asthma in paediatric and adult patients and increases asthma-related emergency hospital admissions (AREHA).

METHODS

AREHAs and levels of air pollutants (PM10, PM2.5 and NO2) were obtained from Mexico City for the period 2017-2019. A time-series approach was used to explore the relationship between air pollutants and AREHA. Relative risks of AREHA were estimated using a negative binomial regression in young children (less than 5 years) and adults (greater than 18 years).

RESULTS

There was a positive association between AREHA and PM10, PM2.5 and NO2 in adults, which remained after mutual adjustment for these pollutants. The relative risk (RR) of admission in adults increased by 3% (95% CI 1% to 4%) for a 10 µg/m3 increase in PM10, 1% (0.03% to 3%) for a 5 µg/m3 increase in PM2.5 and by 1% (0.06% to 2%) for a 5 µg/m3 increase in NO2. In contrast, in young children, AREHAs were negatively associated with PM10 after adjustment for NO2 (RR 0.97 (0.95 to 0.99) for a 10 µg/m3 and with NO2 after adjustment for PM10 and PM2.5 (RR 0.98 (0.96 to 0.99) and 0.97 (0.96 to 0.99), respectively, for a 5 µg/m3 increase in NO2). AREHAs in children were not associated with PM2.5 after adjustment for NO2.

CONCLUSIONS

Ambient air pollution, within the previous week, was associated with emergency hospital admissions for asthma to public hospitals in adults in Mexico City. The relationship in children was less consistent. Further work is needed to explore why differences between adults and children exist to inform appropriate interventions to benefit public health.

Clinical outcomes of children with acute asthma managed with intravenous magnesium sulphate outside intensive care setting

Asthma in children constitutes a well-known respiratory condition with significant mortality. In poorly controlled asthma, multiple adjunct therapies including magnesium sulphate (MgSO4), are recommended to decrease the likelihood of intubation; however, limited evidence exists to support their routine usage in day-to-day situations. Aim of this study is to determine the outcomes of pediatric patients treated with magnesium sulphate during exacerbations of asthma admitted at a tertiary care unit. A retrospective study was conducted at The Aga Khan University Hospital, Karachi, Pakistan from January 2019 to December 2021. Patients aged 6 years to 15 years presented with acute asthma through Emergency Room (ER) having clinical respiratory score (CRS) more than five, admitted in high-dependency unit (HDU) were included in the study. Patients who were started on magnesium sulfate within 24 hours of admission were categorized in magnesium sulfate (MS) group. Patients receiving all standard acute asthma treatment but were not started on magnesium therapy within 24 hours of admission were categorized in the non-magnesium sulfate (non-MS) group. Different outcome variables were compared between the groups. A total of 110 patients with asthma were enrolled. Fifty-four patients were categorized into MS group while 56 were included in non-MS group. Fewer patients were transferred from HDU to pediatric intensive care unit (PICU) (24.07%) in MS group compared to non-MS group (42.85%), (p=0.02). In MS group, the mean number of days spent on oxygen in HDU were 2.38±0.81, while non-MS group spent more days (3.10±0.84 (p<0.01). This study demonstrates that for pediatric patients with severe asthma exacerbations, administration of IV MgSO4 (within 24 hours) is beneficial and results in fewer admissions to PICU and reduces the mean number of days spent on oxygen therapy.

Asthma exacerbations and body mass index in children and adolescents: experience from a tertiary care center

The prevalence and incidence of asthma continue to rise globally. Obesity has been identified as a potential risk factor for asthma exacerbations. The association between body mass index (BMI) and asthma is not well studied in some regions. This study aims to investigate the impact of BMI in pediatric asthmatic patients. This retrospective study was conducted at the Aga Khan University Hospital from 2019 to 2022. Children and adolescents with asthma exacerbation were included. The patients were classified into four groups based on their BMI: underweight, healthy weight, overweight, and obese. The demographic characteristics, medications used, predicted FEV1 measurements, asthma exacerbations per year, length of stay per admission, and the number of patients requiring High Dependency Unit (HDU) care were recorded and analyzed. Our results demonstrated that patients in the healthy weight category had the highest percentage of FEV1 (91.46±8.58) and FEV1/FVC (85.75±9.23) (p<0.001). The study found a significant difference in the average number of asthma exacerbations per year between the four groups. Obese patients had the highest number of episodes (3.22±0.94), followed by the underweight group (2.42±0.59) (p<0.01). The length of stay per admission was significantly shorter for patients with a healthy weight (2.0±0.81), and there was a statistically significant difference observed in the number of patients requiring HDU care among the four groups, as well as in the average length of stay at the HDU (p<0.001). Elevated BMI is related to an increased number of annual asthma exacerbations, a low FEV1 and FEV1/FVC, increased length of stay at admission, and increased stay in the HDU.

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

Asthma and Vitamin D Deficiency: Occurrence, Immune Mechanisms, and New Perspectives

Asthma, as a chronic inflammatory condition of the airways, has a considerable prevalence among children. Vitamin D might play a role in asthma pathogenesis by affecting the development of the lung, regulating the immune responses, and remodeling of airway smooth muscle (ASM). Study results on the association between the serum level of vitamin D and asthma severity have suggested a converse relationship between lower vitamin D levels and more severe clinical courses. However, they are not consistent in these findings and have shown insignificant correlations, as well. The possible effects of vitamin D on asthma have led researchers to consider this vitamin a potential prophylactic and therapeutic tool for managing children with variant degrees of asthma. Adding vitamin D to the routine corticosteroid therapy of asthmatic children is another field of interest that has shown promising results. In this narrative review study, we aim to elaborate on the existing knowledge on the role of vitamin D in asthma pathogenesis and prognosis, explain the controversies that exist on the effectiveness of treating patients with vitamin D supplements, and make a general conclusion about how vitamin D actually is linked to asthma in children.

Challenges in uncontrolled asthma in pediatrics: important considerations for the clinician

INTRODUCTION

Despite symptoms control being the primary focus of asthma management according to guidelines, uncontrolled asthma is still an issue worldwide, leading to huge costs and asthma deaths at all ages. In childhood, poor asthma control can be even more harmful, as it can irreversibly compromise the children's lung function and the whole family's well-being.

AREAS COVERED

Given the problem extent, this review aims to discuss the leading modifiable causes of uncontrolled asthma in Pediatrics, giving some practical insights regarding the critical role of families and the main tools for monitoring control and drug adherence, even at a distance. The most recent GINA documents were used as the primary reference, along with the latest evidence regarding the management of asthma control and the impact of the COVID-19 pandemic on asthma.

EXPERT OPINION

In managing pediatric asthma, a multidisciplinary, multi-determinant, personalized approach is needed, actively involving families, schools, and other specialists. In addition to current strategies for implementing control, electronic health strategies, new validated asthma control tools, and the identification of novel inflammatory biomarkers could lead to increasingly tailored therapies with greater effectiveness in reaching asthma control.

Association of vitamin D levels and asthma exacerbations in children and adolescents: Experience from a tertiary care center

The role of vitamin D as an immunosuppressant and anti-inflammatory has been studied previously for different pathologies in different populations globally. Relationships between serum vitamin D levels and its effect on asthma exacerbations in the adolescent asthma population are not well studied in this region. Therefore, this study was conducted to determine the vitamin D status in pediatric and adolescent asthma patients, and its association with asthma exacerbations. A retrospective study was conducted at The Aga Khan University Hospital from 2016 to 2020. Children and adolescents who were diagnosed and admitted with acute asthma exacerbations and who had at least one measurement of 25 hydroxy-vitamin D (25 OHD) were included in the study. Serum vitamin D levels were documented for enrolled patients and their past 2-year data was analyzed for asthma exacerbations, mean length of stay per admission, and admission plus length of stay at High Dependency Unit. 114 patients were included in the study. 41 patients (35.96%) were found to be vitamin D deficient, 38 patients (33.3%) were vitamin D insufficient, and 35 patients (30.7%) were labeled as vitamin D sufficient. The average number of exacerbations per year was significantly high in vitamin D deficient group (2.82±1.11) in comparison with insufficient (2.05±0.92) and sufficient groups (1.37±0.59) (p<0.001). Vitamin D deficiency is related to an increased number of annual asthma exacerbations, length of stay per admission, and admission into High Dependency Unit (HDU).

Longitudinal measures of phthalate exposure and asthma exacerbation in a rural agricultural cohort of Latino children in Yakima Valley, Washington

Phthalates are a class of widely used synthetic chemicals found in commonly used materials and products. Epidemiological studies suggest phthalate exposure is associated with asthma outcomes, though most studies have not investigated phthalates as triggers of exacerbations in children diagnosed with asthma. This study used data from the Home Air in Agriculture Pediatric Intervention Trial (HAPI) to examine relationships between phthalate exposure and outcomes related to childhood asthma exacerbation. We used measures of phthalate metabolites and respiratory health measures including fractional exhaled nitric oxide (FENO), the Asthma Control Test (ACT), caregiver report of symptoms, and urinary leukotriene E₄ (uLTE₄) to estimate longitudinal associations using mixed effects models, adjusted for covariates. For 100% (i.e., doubling) increases in mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono-2-ethylhexyl phthalate (MEHP), and mono-ethyl phthalate (MEP), concentrations of FENO increased by 8.7% (95% CI: 0.7-17.3), 7.2% (95% CI: 0.0-14.9), and 6.4% (95% CI: 0.0-13.3), respectively. All phthalate metabolites demonstrated associations with uLTE₄, effect sizes ranging from an 8.7% increase in uLTE₄ (95% CI: 4.3-12.5) for a 100% increase in MEHP to an 18.1% increase in uLTE₄ (95% CI: 13.3-23.1) for a 100% increase in MNBP. In models of caregiver report of symptoms, no phthalate metabolites were significantly associated in primary models. No phthalate metabolites were associated with standardized ACT score. Our results suggest urinary phthalate metabolites are significant predictors of inflammatory biomarkers related to asthma exacerbation in children but not child and caregiver report of airway symptomatology.

Risk Factors and Outcomes Associated With Antibiotic Therapy in Children Hospitalized With Asthma Exacerbation

OBJECTIVE

Despite lack of benefit, antibiotics are overused in management of asthma exacerbation in children. In this study, data from a single children's hospital were analyzed to identify factors and outcomes associated with antibiotic use in children hospitalized with asthma.

METHODS

The study population was identified by using administrative data from 2012 to 2015, with subsequent verification of asthma. We analyzed factors associated with antibiotic use (demographic, seasonal, clinical) and outcome (length of stay [LOS]) with respect to: 1) disposition to pediatric floor (PF) or pediatric intensive care unit (PICU); and 2) evidence of coexisting bacterial infection and/or fever. Statistical analysis included univariate and controlled regression models. Data are presented as median and IQR for continuous variables and OR and regression coefficient (β) with 95% CIs for regression analyses.

RESULTS

Of 600 patients, 28.8% were admitted to PICU, 14.8% had verified bacterial infection, and 53.8% received antibiotic, mainly azithromycin. Nearly all PICU patients were treated with antibiotic, irrespective of coexisting bacterial infection or fever. Among PF patients, nearly 30% without bacterial infection or fever and 40% with fever alone received antimicrobials. Overall risk for antibiotic treatment was associated with older age, female sex, desaturation events, oxygen supplementation, and PICU admission. Additionally, antibiotic treatment was associated with 13- to 19-hour increased LOS for PF patients without bacterial infection and/or fever.

CONCLUSIONS

Almost half of pediatric patients admitted with asthma exacerbation received antibiotic therapy with no clear indication, which was associated with prolonged LOS.

Increased Risk of Hospital Admission for Asthma in Children From Short-Term Exposure to Air Pollution: Case-Crossover Evidence From Northern China

Background: Previous studies suggested that exposure to air pollution could increase risk of asthma attacks in children. The aim of this study is to investigate the short-term effects of exposure to ambient air pollution on asthma hospital admissions in children in Beijing, a city with serious air pollution and high-quality medical care at the same time.

Methods: We collected hospital admission data of asthma patients aged ≤ 18 years old from 56 hospitals from 2013 to 2016 in Beijing, China. Time-stratified case-crossover design and conditional Poisson regression were applied to explore the association between risk of asthma admission in children and the daily concentration of six air pollutants [particulate matter ≤ 2.5 μm (PM_2.5), particulate matter ≤ 10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃)], adjusting for meteorological factors and other pollutants. Additionally, stratified analyses were performed by age, gender, and season.

Results: In the single-pollutant models, higher levels of PM_2.5, SO₂, and NO₂ were significantly associated with increased risk of hospital admission for asthma in children. The strongest effect was observed in NO₂ at lag06 (RR = 1.25, 95%CI: 1.06-1.48), followed by SO₂ at lag05 (RR = 1.17, 95%CI: 1.05–1.31). The robustness of effects of SO₂ and NO₂ were shown in two-pollutant models. Stratified analyses further indicated that pre-school children (aged ≤ 6 years) were more susceptible to SO₂. The effects of SO₂ were stronger in the cold season, while the effects of NO₂ were stronger in the warm season. No significant sex-specific differences were observed.

Conclusions: These results suggested that high levels of air pollution had an adverse effect on childhood asthma, even in a region with high-quality healthcare. Therefore, it will be significant to decrease hospital admissions for asthma in children by controlling air pollution emission and avoiding exposure to air pollution.

Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations

BACKGROUND

Asthma exacerbations commonly lead to unplanned health care utilization and are costly. Early identification of children at increased risk of asthma exacerbations would allow a proactive management approach.

OBJECTIVE

We evaluated common asthma risk factors to predict the probability of exacerbation for individual children aged 0-21 years using data from the electronic medical record (EMR).

METHODS

We analyzed longitudinal EMR data for over 3000 participants with asthma seen at Cincinnati Children's Hospital Medical Center over a 7-year period. The study population was divided into 3 age groups: 0-4, 5-11, and 12-21 years. Each age group was divided into a derivation cohort and a validation cohort, which were used to build a risk score model. We predicted risk of exacerbation in the next 12 months, validated the scores by risk stratum, and developed a clinical tool to determine the risk level based on this model.

RESULTS

Risk model results were confirmed with validation cohorts by calendar year and age groups. Race, allergic sensitization, and smoke exposure were each important risk factors in the 0-4 age group. Abnormal spirometry and obesity were more sensitive predictors of exacerbation in children >12 years. For each age group, a higher expanded score was associated with a higher predicted probability of an asthma exacerbation in the subsequent year.

CONCLUSION

This asthma exacerbation prediction model, and the associated clinical tool, may assist clinicians in identifying children at high risk for exacerbation that may benefit from more aggressive management and targeted risk mitigation.

Asthma and COVID-19: Emphasis on Adequate Asthma Control

Asthmatics are at an increased risk of developing exacerbations after being infected by respiratory viruses such as influenza virus, parainfluenza virus, and human and severe acute respiratory syndrome coronaviruses (SARS-CoV). Asthma, especially when poorly controlled, is an independent risk factor for developing pneumonia. A subset of asthmatics can have significant defects in their innate, humoral, and cell-mediated immunity arms, which may explain the increased susceptibility to infections. Adequate asthma control is associated with a significant decrease in episodes of exacerbation. Because of their wide availability and potency to promote adequate asthma control, glucocorticoids, especially inhaled ones, are the cornerstone of asthma management. The current COVID-19 pandemic affects millions of people worldwide and possesses mortality several times that of seasonal influenza; therefore, it is necessary to revisit this subject. The pathogenesis of SARS-CoV-2, the virus that causes COVID-19, can potentiate the development of acute asthmatic exacerbation with the potential to worsen the state of chronic airway inflammation. The relationship is evident from several studies that show asthmatics experiencing a more adverse clinical course of SARS-CoV-2 infection than nonasthmatics. Recent studies show that dexamethasone, a potent glucocorticoid, and other inhaled corticosteroids significantly reduce morbidity and mortality among hospitalized COVID-19 patients. Hence, while we are waiting for more studies with higher level of evidence that further narrate the association between COVID-19 and asthma, we advise clinicians to try to achieve adequate disease control in asthmatics as it may reduce incidences and severity of exacerbations especially from SARS-CoV-2 infection.

The effects of increasing fruit and vegetable intake in children with asthma: A randomized controlled trial

BACKGROUND

A high fruit and vegetable (F&V) diet reduces asthma exacerbations in adults; this has not been examined in children to date.

OBJECTIVE

To investigate the effect of a 6-month, high F&V diet on the time to first asthma exacerbation in children with asthma, in a parallel-group, randomized, controlled trial.

METHODS

Children (aged 3-11 years) with asthma, history of exacerbations and usual low F&V intake (≤3 serves/day) were randomized to the intervention (high F&V diet) or control group (usual diet) for 6 months. The primary outcome was time to first exacerbation requiring medical intervention. Secondary outcomes included exacerbation rate, lung function, plasma TNF-α, CRP, and IL-6, faecal microbiota and peripheral blood mononuclear cell (PBMC) histone deacetylase (HDAC) activity and G-protein coupled receptor (GPR) 41/43 and HDAC (1-11) expression.

RESULTS

67 children were randomized between September 2015 and July 2018. F&V intake (difference in change (∆): 3.5 serves/day, 95% CI: [2.6, 4.4] p < 0.001) and plasma total carotenoids (∆: 0.44 µg/ml [0.19, 0.70] p = 0.001) increased after 6 months (intervention vs control). Time to first exacerbation (HR: 0.81, 95% CI: [0.38, 1.69], p = 0.569) and exacerbation rate (IRR: 0.84, [0.47, 1.49], p = 0.553) were similar between groups. In per-protocol analysis, airway reactance z-scores increased (X₅ ∆: 0.76 [0.04, 1.48] p = 0.038, X₂₀ ∆: 0.93 [0.23, 1.64] p = 0.009) and changes in faecal microbiota were observed, both in the intervention versus control group, though there was no difference between groups in systemic inflammation or molecular mechanisms. In the control group, CRP and HDAC enzyme activity increased, while GPR41 expression decreased. No adverse events attributable to the interventions were observed.

CONCLUSION & CLINICAL RELEVANCE

A high F&V diet did not affect asthma exacerbations over the 6-month intervention, though warrants further investigation as a strategy for improving lung function and protecting against systemic inflammation in children with asthma.

The predictive value of impulse oscillometry for asthma exacerbations in childhood: A systematic review and meta-analyses

OBJECTIVE

Several studies have explored the predictive value of impulse oscillometry (IOS) for asthma exacerbations in childhood, but its specific parameters are still unclear. Therefore, we designed this meta-analysis to determine the related indicators of acute asthma attacks.

METHODS

A comprehensive literature search was performed on July 9, 2020 based on PubMed, Embase, and Web of Science database. Weighted mean differences (WMDs) were calculated using fixed- or random-effects models.

RESULTS

A total of 615 patients from six trials were included in this analysis. IOS may be a useful tool to predict asthma exacerbations. And the results showed that R5 (WMD = -1.21, 95% CI: -1.55 to -0.87, p < .001), Fres (WMD = -1.34, 95% CI: -2.03 to -0.65, p = .018), and AX (WMD = -7.35, 95% CI: -9.94 to -4.76, p < .001) had significant correlation with asthma exacerbations. In addition, X5 may also predict the acute attack of asthma (WMD = 0.81, 95% CI: 0.56 to 1.01, p < .001).

CONCLUSIONS

R5, AX, Fres, and X5 may be able to identify the risk of an acute attack of asthma. Besides, our research further demonstrated that peripheral airway injury may play an important role in the acute attack of asthma.

The nationwide retrospective cohort study by Health Insurance Review and Assessment Service proves that asthma management decreases the exacerbation risk of asthma

Medical costs have recently increased in South Korea due to the rising rate of asthma. Primary clinics serve an important role in asthma management, as they are the first stop for patients presenting with symptoms. The Health Insurance Review and Assessment Service (HIRA) in South Korea has assessed asthma-management quality since 2013, but studies are lacking on whether these assessments have been performed properly and contribute toward reducing asthma exacerbations. Therefore, we investigated whether the HIRA’s quality assessments have decreased asthma exacerbations using national health insurance claims data from 2013 to 2017 of 83,375 primary-clinic and 15,931 tertiary-hospital patients with asthma. These patients were classified into four groups based on disease severity according to the monthly prescribed amount of asthma medication using K-means clustering. The associations between HIRA assessments and asthma exacerbation were analyzed using a generalized estimating equation. Our results showed that exacerbation odds gradually decreased as the HIRA assessments progressed, especially in the mild-severity group, and that exacerbation risk among patients with asthma decreased in the order of assessment grades: “Unsatisfactory,” “Satisfactory,” and “Tertiary.” Therefore, we may conclude that asthma exacerbations may decrease with high quality asthma management; appropriate quality assessment could be helpful in reducing asthma exacerbations.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recently, various machine-learning algorithms were introduced to provide mechanistic insights about the heterogeneity of clustered data pertaining to the symptoms of each asthma patient and potential environmental risk factors. However, there is limited information on the performance of these machine learning tools. In this study, we compared the performance of ten machine-learning techniques. Using an advanced method of imbalanced sampling (IS), we improved the performance of nine conventional machine learning techniques predicting the association between exposure level to indoor air quality and change in patients’ peak expiratory flow rate (PEFR). We then proposed a deep learning method of transfer learning (TL) for further improvement in prediction accuracy. Our selected final prediction techniques (TL1_IS or TL2-IS) achieved a balanced accuracy median (interquartile range) of 66(56~76) % for TL1_IS and 68(63~78) % for TL2_IS. Precision levels for TL1_IS and TL2_IS were 68(62~72) % and 66(62~69) % while sensitivity levels were 58(50~67) % and 59(51~80) % from 25 patients which were approximately 1.08 (accuracy, precision) to 1.28 (sensitivity) times increased in terms of performance outcomes, compared to NN_IS. Our results indicate that the transfer machine learning technique with imbalanced sampling is a powerful tool to predict the change in PEFR due to exposure to indoor air including the concentration of particulate matter of 2.5 μm and carbon dioxide. This modeling technique is even applicable with small-sized or imbalanced dataset, which represents a personalized, real-world setting.

Asthma attacks in children are always preceded by poor asthma control: myth or maxim?

Some, but not all, asthma exacerbations in children are preceded by poor asthma control https://bit.ly/3muIy6h.

Effect of Vitamin D3 Supplementation on Severe Asthma Exacerbations in Children With Asthma and Low Vitamin D Levels: The VDKA Randomized Clinical Trial

IMPORTANCE

Severe asthma exacerbations cause significant morbidity and costs. Whether vitamin D3 supplementation reduces severe childhood asthma exacerbations is unclear.

OBJECTIVE

To determine whether vitamin D3 supplementation improves the time to a severe exacerbation in children with asthma and low vitamin D levels.

DESIGN, SETTING, AND PARTICIPANTS

The Vitamin D to Prevent Severe Asthma Exacerbations (VDKA) Study was a randomized, double-blind, placebo-controlled clinical trial of vitamin D3 supplementation to improve the time to severe exacerbations in high-risk children with asthma aged 6 to 16 years taking low-dose inhaled corticosteroids and with serum 25-hydroxyvitamin D levels less than 30 ng/mL. Participants were recruited from 7 US centers. Enrollment started in February 2016, with a goal of 400 participants; the trial was terminated early (March 2019) due to futility, and follow-up ended in September 2019.

INTERVENTIONS

Participants were randomized to vitamin D3, 4000 IU/d (n = 96), or placebo (n = 96) for 48 weeks and maintained with fluticasone propionate, 176 μg/d (6-11 years old), or 220 μg/d (12-16 years old).

MAIN OUTCOMES AND MEASURES

The primary outcome was the time to a severe asthma exacerbation. Secondary outcomes included the time to a viral-induced severe exacerbation, the proportion of participants in whom the dose of inhaled corticosteroid was reduced halfway through the trial, and the cumulative fluticasone dose during the trial.

RESULTS

Among 192 randomized participants (mean age, 9.8 years; 77 girls [40%]), 180 (93.8%) completed the trial. A total of 36 participants (37.5%) in the vitamin D3 group and 33 (34.4%) in the placebo group had 1 or more severe exacerbations. Compared with placebo, vitamin D3 supplementation did not significantly improve the time to a severe exacerbation: the mean time to exacerbation was 240 days in the vitamin D3 group vs 253 days in the placebo group (mean group difference, -13.1 days [95% CI, -42.6 to 16.4]; adjusted hazard ratio, 1.13 [95% CI, 0.69 to 1.85]; P = .63). Vitamin D3 supplementation, compared with placebo, likewise did not significantly improve the time to a viral-induced severe exacerbation, the proportion of participants whose dose of inhaled corticosteroid was reduced, or the cumulative fluticasone dose during the trial. Serious adverse events were similar in both groups (vitamin D3 group, n = 11; placebo group, n = 9).

CONCLUSIONS AND RELEVANCE

Among children with persistent asthma and low vitamin D levels, vitamin D3 supplementation, compared with placebo, did not significantly improve the time to a severe asthma exacerbation. The findings do not support the use of vitamin D3 supplementation to prevent severe asthma exacerbations in this group of patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02687815.

Addressing the risk domain in the long-term management of pediatric asthma

There is growing concern regarding the long-term outcomes of early and poorly controlled childhood asthma, either of which can potentially lead to the development of severe asthma in adults and irrecoverable loss of lung function leading to chronic obstructive pulmonary disease. These outcomes of inadequately controlled asthma should prompt a change in practice to better and/or earlier identify children at risk of adverse respiratory outcomes of asthma, to monitor disease progression, and to design intervention strategies that could either prevent or reverse asthma progression in children. The careful follow-up of spirometry over time-in the form of lung function trajectories, the application of biomarkers to assist in the diagnosis of early asthma and medication selection for these patients, as well as methods to identify patients at risk of asthma attacks-can be used to develop individualized management strategies for children with asthma. It is now time for asthma specialists to communicate this information to patients, parents, and primary care physicians and to incorporate them into routine clinical assessments of children with asthma. In time, these concepts of risk management and prevention can be refined to provide a more comprehensive approach to asthma care so as to prevent adverse respiratory outcomes from poorly controlled childhood asthma.

Machine learning approach to single nucleotide polymorphism-based asthma prediction

Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma.

Bronchodilator Dose Responsiveness in Children and Adolescents: Clinical Features and Association with Future Asthma Exacerbations

BACKGROUND

Bronchodilator reversibility measures are often associated with poor asthma outcomes in children. Whether bronchodilator dose responsiveness is similarly useful in children is unclear.

OBJECTIVE

We hypothesized that children and adolescents requiring higher doses of bronchodilator to achieve maximal bronchodilation would have unique risk factors and increased risk of future exacerbation.

METHODS

Children (6-11 years, N = 299) and adolescents (12-21 years, N = 331) with confirmed asthma underwent clinical phenotyping procedures and a test of maximal bronchodilation with escalating doses of albuterol sulfate up to 720 mcg. Outcome measures were assessed at 12 months and included exacerbations treated with systemic corticosteroids, emergency department (ED) visits, and hospitalizations for asthma.

RESULTS

A total of 6.7% of children and 9.3% of adolescents had poor bronchodilator dose responsiveness, defined as attainment of maximal forced expiratory volume in 1 second with 720 mcg albuterol. Risk factors included type 2 inflammation, prior exacerbations, and greater asthma severity; historical pneumonia and tobacco exposure were also risk factors in children. Children and adolescents with poor bronchodilator dose responsiveness did not have increased current symptoms or impaired quality of life, but had approximately 2-fold increased odds of exacerbation or ED visit and approximately 3-fold increased odds of hospitalization by 12 months, independent of airflow obstruction.

CONCLUSIONS

Bronchodilator dose responsiveness may be useful for phenotyping and may be of utility in practice and future studies focused on asthma outcomes or quantification of treatment responses. In children and adolescents, this phenotype of poor bronchodilator responsiveness may be associated with periods of relatively stable disease yet marked airway constriction in response to triggers, including tobacco smoke, respiratory infections/pneumonia, and aeroallergens.

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

Exploring the Utility of Noninvasive Type 2 Inflammatory Markers for Prediction of Severe Asthma Exacerbations in Children and Adolescents

BACKGROUND

Noninvasive markers of type 2 inflammation are needed to identify children and adolescents who might benefit from personalized biologic therapy.

OBJECTIVE

We hypothesized that blood eosinophil counts would predict 1 or more acute visits for asthma and that prediction could be improved with the addition of a second, noninvasive type 2 inflammatory biomarker.

METHODS

Children and adolescents 5 to 21 years (N = 589) with an asthma exacerbation necessitating systemic corticosteroid treatment in the previous year completed a characterization visit and telephone calls at 6 and 12 months. The primary outcome was an acute visit for asthma with receipt of systemic corticosteroids. Acute visits were verified by medical record review. Exploratory outcomes included time to first acute visit and hospitalization.

RESULTS

Acute visits occurred in 106 (35.5%) children and 72 (24.8%) adolescents. Elevated blood eosinophils were associated with increased odds and shorter time to first acute visit, but optimal cut-points differed by age (≥150 vs ≥300 cells/μL for children vs adolescents, respectively). The addition of a second marker of type 2 inflammation did not improve prediction in children, but increased the odds and hazard of an acute visit up to 16.2% and 11.9%, respectively, in adolescents. Similar trends were noted for hospitalizations.

CONCLUSIONS

Blood eosinophils and other noninvasive markers of type 2 inflammation may be useful in the clinical assessment of children and adolescents with asthma. However, features of type 2 inflammation vary by age. Whether children and adolescents also respond differently to management of type 2 inflammation is unclear and warrants further evaluation.

Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach

BACKGROUND

Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms.

METHODS

Schoolchildren from the population-based southern California Children's Health Study were visited annually from 2003 to 2012. Bronchitic symptoms over the prior 12 months were assessed by questionnaire. A gradient boosting model was fit using groups of risk factors (including traffic/air pollution exposures) for all children and by asthma status. Training data consisted of one observation per participant in a random study year (for 50% of participants). Validation data consisted of: (1) a random (later) year in the same participants (within-participant); (2) a random year in participants excluded from the training data (across-participant).

RESULTS

At baseline, 13.2% of children had asthma and 18.1% reported bronchitic symptoms. Models performed similarly within- and across-participant. Previous year symptoms/medication use provided much of the predictive ability (across-participant area under the receiver operating characteristic curve (AUC): 0.76 vs 0.78 for all risk factors, in all participants). Traffic/air pollution exposures added modestly to prediction as did body mass index percentile, age and parent stress.

CONCLUSIONS

Regardless of asthma status, previous symptoms were the most important predictors of current symptoms. Traffic/air pollution variables contribute modest predictive information, but impact large populations. Methods proposed here could be generalized to personalized exacerbation predictions in future longitudinal studies to support targeted prevention efforts.

The Role of the Microbiome in Asthma: The Gut⁻Lung Axis

Asthma is one of the most common chronic respiratory diseases worldwide. It affects all ages but frequently begins in childhood. Initiation and exacerbations may depend on individual susceptibility, viral infections, allergen exposure, tobacco smoke exposure, and outdoor air pollution. The aim of this review was to analyze the role of the gut⁻lung axis in asthma development, considering all asthma phenotypes, and to evaluate whether microbe-based therapies may be used for asthma prevention. Several studies have confirmed the role of microbiota in the regulation of immune function and the development of atopy and asthma. These clinical conditions have apparent roots in an insufficiency of early life exposure to the diverse environmental microbiota necessary to ensure colonization of the gastrointestinal and/or respiratory tracts. Commensal microbes are necessary for the induction of a balanced, tolerogenic immune system. The identification of commensal bacteria in both the gastroenteric and respiratory tracts could be an innovative and important issue. In conclusion, the function of microbiota in healthy immune response is generally acknowledged, and gut dysbacteriosis might result in chronic inflammatory respiratory disorders, particularly asthma. Further investigations are needed to improve our understanding of the role of the microbiome in inflammation and its influence on important risk factors for asthma, including tobacco smoke and host genetic features.

Predictors of repeated acute hospital attendance for asthma in children: A systematic review and meta-analysis

BACKGROUND

Asthma attacks are common and have significant physical, psychological, and financial consequences. Improving the assessment of a child's risk of subsequent asthma attacks could support front-line clinicians' decisions on augmenting chronic treatment or specialist referral. We aimed to identify predictors for emergency department (ED) or hospital readmission for asthma from the published literature.

METHODS

We searched MEDLINE, EMBASE, AMED, PsycINFO, and CINAHL with no language, location, or time restrictions. We retrieved observational studies and randomized controlled trials (RCT) assessing factors (personal and family history, and biomarkers) associated with the risk of ED re-attendance or hospital readmission for acute childhood asthma.

RESULTS

Three RCTs and 33 observational studies were included, 31 from Anglophone countries and none from Asia or Africa. There was an unclear or high risk of bias in 14 of the studies, including 2 of the RCTs. Previous history of emergency or hospital admissions for asthma, younger age, African-American ethnicity, and low socioeconomic status increased risk of subsequent ED and hospital readmissions for acute asthma. Female sex and concomitant allergic diseases also predicted hospital readmission.

CONCLUSION

Despite the global importance of this issue, there are relatively few high quality studies or studies from outside North America. Factors other than symptoms are associated with the risk of emergency re-attendance for acute asthma among children. Further research is required to better quantify the risk of future attacks and to assess the role of commonly used biomarkers.

Circulating microRNAs and prediction of asthma exacerbation in childhood asthma

BACKGROUND

Circulating microRNAs have shown promise as non-invasive biomarkers and predictors of disease activity. Prior asthma studies using clinical, biochemical and genomic data have not shown excellent prediction of exacerbation. We hypothesized that a panel of circulating microRNAs in a pediatric asthma cohort combined with an exacerbation clinical score might predict exacerbation better than the latter alone.

METHODS

Serum samples from 153 children at randomization in the Childhood Asthma Management Program were profiled for 754 microRNAs. Data dichotomized for asthma exacerbation one year after randomization to inhaled corticosteroid treatment were used for binary logistic regression with miRNA expressions and exacerbation clinical score.

RESULTS

12 of 125 well-detected circulating microRNAs had significant odd ratios for exacerbation with miR-206 being most significant. Each doubling of expression of the 12 microRNA corresponded to a 25-67% increase in exacerbation risk. Stepwise logistic regression yielded a 3-microRNA model (miR-146b, miR-206 and miR-720) that, combined with the exacerbation clinical score, had excellent predictive power with a 0.81 AUROC. These 3 microRNAs were involved in NF-kβ and GSK3/AKT pathways.

CONCLUSIONS

This combined circulating microRNA-clinical score model predicted exacerbation in asthmatic subjects on inhaled corticosteroids better than each constituent feature alone.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00000575 .

Difficult vs. Severe Asthma: Definition and Limits of Asthma Control in the Pediatric Population

Evaluating the degree of disease control is pivotal when assessing a patient with asthma. Asthma control is defined as the degree to which manifestations of the disease are reduced or removed by therapy. Two domains of asthma control are identified in the guidelines: symptom control and future risk of poor asthma outcomes, including asthma attacks, accelerated decline in lung function, or treatment-related side effects. Over the past decade, the definition and the tools of asthma control have been substantially implemented so that the majority of children with asthma have their disease well controlled with standard therapies. However, a small subset of asthmatic children still requires maximal therapy to achieve or maintain symptom control and experience considerable morbidity. Childhood uncontrolled asthma is a heterogeneous group and represents a clinical and therapeutic challenge requiring a multidisciplinary systematic assessment. The identification of the factors that may contribute to the gain or loss of control in asthma is essential in differentiating children with difficult-to-treat asthma from those with severe asthma that is resistant to traditional therapies. The aim of this review is to focus on current concept of asthma control, describing monitoring tools currently used to assess asthma control in clinical practice and research, and evaluating comorbidities and modifiable and non-modifiable factors associated with uncontrolled asthma in children, with particular reference to severe asthma.

Efficacy and safety of dupilumab in perennial allergic rhinitis and comorbid asthma

BACKGROUND

Dupilumab, an anti-IL-4 receptor α mAb, inhibits IL-4/IL-13 signaling, key drivers of type 2/T_H2 immune diseases (eg, atopic/allergic disease). In a pivotal, phase 2b study (NCT01854047), dupilumab reduced severe exacerbations, improved lung function and quality of life, and was generally well tolerated in patients with uncontrolled persistent asthma despite using medium-to-high-dose inhaled corticosteroids plus long-acting β2-agonists.

OBJECTIVE

To examine dupilumab's effect on the 22-item Sino-Nasal Outcome Test (SNOT-22) total score and its allergic rhinitis (AR)-associated items in asthma patients with comorbid perennial allergic rhinitis (PAR).

METHODS

A post hoc analysis reporting data from the phase 2b study for the 200 and 300 mg every 2 week (q2w) doses under investigation in phase 3 (NCT02414854) was carried out. PAR was defined at study entry as a specific response to typical perennial antigens (IgE ≥0.35 Ku/L).

RESULTS

Overall, 241 (61%) patients had PAR. In asthma patients with PAR, dupilumab 300 mg q2w versus placebo significantly improved SNOT-22 total score (least squares mean difference, -5.98; 95% CI, -10.45 to -1.51; P = .009) and all 4 AR-associated symptoms evaluated (nasal blockage, -0.60; 95% CI, -0.96 to -0.25; runny nose, -0.67; 95% CI, -1.04 to -0.31; sneezing, -0.55; 95% CI, -0.89 to -0.21; postnasal discharge, -0.49; 95% CI, -0.83 to -0.16; all P < .01). Dupilumab 200 mg q2w demonstrated numerical, but not statistically significant, decreases in SNOT-22 total score (-1.82; 95% CI, -6.46 to 2.83; P = .443 vs placebo) and in each AR-associated symptom. In patients without PAR, no differences were observed for these measures versus placebo.

CONCLUSIONS

Dupilumab 300 mg q2w significantly improved AR-associated nasal symptoms in patients with uncontrolled persistent asthma and comorbid PAR.

A missed primary care appointment correlates with a subsequent emergency department visit among children with asthma

OBJECTIVE

Since the Affordable Care Act's implementation, emergency department (ED) visits have increased. Poor asthma control increases the risk of acute exacerbations and preventable ED visits. The Centers for Medicare and Medicaid Services support the reduction of preventable ED visits to reduce healthcare spending. Implementation of interventions to avoid preventable ED visits has become a priority for many healthcare systems yet little data exist examining children's missed asthma management primary care (PC) appointments and subsequent ED visits.

METHODS

Longitudinal, retrospective review at a children's hospital was conducted for children with diagnosed asthma (ICD-9 493.xx), ages 2-18 years, scheduled for a PC visit between January 1, 2010, and June 30, 2012 (N = 3895). Records were cross-referenced with all asthma-related ED visits from January 1, 2010 to December 31, 2012. Logistic regression with maximum likelihood estimation was conducted.

RESULTS

None of the children who completed a PC appointment experienced an ED visit in the subsequent 6 months whereas 2.7% of those with missed PC appointments had an ED visit (χ2 = 64.28, p <.0001). Males were significantly more likely to have an ED visit following a missed PC appointment than females (χ2 = 34.37, p <.0001). There was a statistically significant interaction of sex × age. Younger children (<12 years) made more visits than older children.

CONCLUSIONS

The importance of adherence to PC appointments for children with asthma as one mechanism for preventing ED visits was demonstrated. Interventions targeting missed visits could decrease asthma-related morbidity, preventable ED visits, and healthcare costs.

A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations

OBJECTIVE

To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation.

MATERIALS AND METHODS

Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1-pathways." The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided.

RESULTS

Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%).

DISCUSSION

Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes.

CONCLUSION

The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

A Roadmap for Optimizing Asthma Care Management via Computational Approaches

Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations, and 1.8 million emergency room visits annually. A small fraction of asthma patients with high vulnerabilities, severe disease, or great barriers to care consume most health care costs and resources. An effective approach is urgently needed to identify high-risk patients and intervene to improve outcomes and to reduce costs and resource use. Care management is widely used to implement tailored care plans for this purpose, but it is expensive and has limited service capacity. To maximize benefit, we should enroll only patients anticipated to have the highest costs or worst prognosis. Effective care management requires correctly identifying high-risk patients, but current patient identification approaches have major limitations. This paper pinpoints these limitations and outlines multiple machine learning techniques to address them, providing a roadmap for future research.

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.

Comparing executive functions between patients with chronic asthma and healthy subjects

OBJECTIVE

Allergic diseases have different levels of prevalence all over the world. Among them, asthma is the most common chronic disease. Up to now, little attention has been paid to the impact of this chronic disease on people's executive functions. The present study aimed at comparing the executive functions in patients with chronic asthma and healthy subjects.

METHODS

The study population was patients with chronic asthma who were referred to Al-Zahra hospital in Isfahan Province and their visitors who were assigned as the control group. Thirty patients with chronic asthma and 30 patient visitors were enrolled in this study, and three software programs (Wisconsin, Stroop, and Continuous Performance Tests) were used.

RESULTS

The results of multivariate variance analysis showed that there is a significant difference between patients with chronic asthma and healthy subjects in terms of set shifting, inhibition, and attention performance.

CONCLUSIONS

This study revealed that the executive functions of patients with chronic asthma have significant defects.

Effect of outdoor air pollution on asthma exacerbations in children and adults: Systematic review and multilevel meta-analysis

Background

Several observational studies have suggested that outdoor air pollution may induce or aggravate asthma. However, epidemiological results are inconclusive due to the presence of numerous moderators which influence this association. The goal of this study was to assess the relationship between outdoor air pollutants and moderate or severe asthma exacerbations in children and adults through a systematic review and multilevel meta-analysis.

Material and methods

We searched studies published in English on PubMed, Scopus, and Google Scholar between January 2000 and October 2016. Studies following a case-crossover design with records of emergency departments and/or hospital admissions as a surrogate of moderate or severe asthma exacerbations were selected. A multilevel meta-analysis was employed, taking into account the potential clustering effects within studies examining more than one lag. Odds ratios (ORs) and 95% confidence intervals were estimated. A subgroup analysis in children aged 0 to 18 years and a sensitivity analysis based on the quality of the included studies as defined in the Newcastle-Ottawa Scale were performed. Publication bias was evaluated through visual inspection of funnel plots and by a complementary search of grey literature. (Prospero Registration number CRD42015032323).

Results

Database searches retrieved 208 records, and finally 22 studies were selected for quantitative analysis. All pollutants except SO₂ and PM₁₀ showed a significant association with asthma exacerbations (NO₂: 1.024; 95% CI: 1.005,1.043, SO₂: 1.039; 95% CI: 0.988,1.094), PM₁₀: 1.024; 95% CI: 0.995,1.053, PM_2.5: 1.028; 95% CI: 1.009,1.047, CO: 1.045; 95% CI: 1.005,1.086, O₃: 1.032; 95% CI: 1.005,1.060. In children, the association was significant for NO₂, SO₂ and PM_2.5.

Conclusion

This meta-analysis provides evidence of the association between selected air pollutants and asthma exacerbations for different lags.

Impulse Oscillometry as a Predictor of Asthma Exacerbations in Young Children

BACKGROUND

In a post-hoc analysis of a pediatric asthma study, we identified the predictors of asthma exacerbations (AEs) and related them to forced expiratory volume (FEV1), the FEV1/FVC ratio, and bronchial hyperresponsiveness (BHR).

OBJECTIVES

We sought to detect predictors of AEs in a prospective study that utilizes impulse oscillometry (IOS) and to compare the results to previously determined predictors.

METHODS

A moderate AE was defined as an increased use of salbutamol during coughing episodes. Pulmonary function and BHR were measured during symptom- and medication-free periods. Additionally, allergen testing and IOS were included. To calculate the sensitivity and specificity of AE detection, a receiver-operating characteristic (ROC) curve was plotted, and accuracy was measured with the area under the ROC curve (AUC). A logistic regression analysis was used to predict the probability of an exacerbation.

RESULTS

Seventy-five pediatric patients (4-7 years of age) with intermittent asthma were included. In 69 patients, the following cut-off values demonstrated the best sensitivity and specificity combination for predicting an AE: FEV1 103.2% (AUC 0.62), BHR (PD20methacholine) 0.13 mg (AUC 0.61), and, in 54 children, Rrs5 0.78 kPa × l-1 × s (AUC 0.80). Logistic regression analysis demonstrated that the combination of all parameters predicted the individual risk of AEs with an accuracy of 86%.

CONCLUSIONS

IOS, a simple method, predicted the probability of AEs in young children. Airway resistance, measured by IOS, was superior to FEV1 and methacholine testing. The current data suggest that peripheral airway obstruction is present during symptom-free periods and that these children more likely experience AEs.

Combined use of fractional exhaled nitric oxide and bronchodilator response in predicting future loss of asthma control among children with atopic asthma

BACKGROUND AND OBJECTIVE

Recognition of patients at risk of asthma exacerbation is important for future asthma care and improved outcome. The aim of the present study was to see whether measurements of bronchodilator response (BDR) and fractional exhaled nitric oxide (FeNO) in combination provide prognostic information superior to either measurement alone in children with atopic asthma.

METHODS

A total of 201 atopic children aged 8-16 years with intermittent or mild persistent asthma were included. Pulmonary function tests including BDR and FeNO were serially monitored 10 times or more over 2 years when subjects were not receiving controller medications. After completion of monitoring, 1-year observation for a loss of asthma control was performed.

RESULTS

During the monitoring period, positive BDRs (≥12% in forced expiratory volume in 1 s (FEV₁ ) from pre-bronchodilator value) and FeNO higher than 35 parts per billion (ppb) were observed at least once in 59% and 77% of participants. When analysed as continuous variables, both BDR (hazard ratio (HR): 1.21; 95% CI: 1.04-1.41; P = 0.014) and FeNO (HR: 1.27; 95% CI: 1.09-1.49; P = 0.003) were associated with increased risks for a control loss. Compared with patients showing either positive BDRs (HR: 3.19; 95% CI: 1.05-9.64) or FeNO higher than 35 ppb (HR: 4.70; 95% CI: 1.68-13.11), patients with both findings (HR: 7.08; 95% CI: 2.57-19.49) had greater risks for a control loss.

CONCLUSION

These data support that combined use of BDR and FeNO measurements can modify predictive risk obtained from either measurement alone.

Housing code violation density associated with emergency department and hospital use by children with asthma

Local agencies that enforce housing policies can partner with the health care system to target pediatric asthma care. These agencies retain data that can be used to pinpoint potential clusters of high asthma morbidity. We sought to assess whether the density of housing code violations in census tracts-the in-tract asthma-relevant violations (such as the presence of mold or cockroaches) divided by the number of housing units-was associated with population-level asthma morbidity and could be used to predict a hospitalized patient's risk of subsequent morbidity. We found that increased density in housing code violations was associated with population-level morbidity independent of poverty, and that the density explained 22 percent of the variation in rates of asthma-related emergency department visits and hospitalizations. Children who had been hospitalized for asthma had 1.84 greater odds of a revisit to the emergency department or a rehospitalization within twelve months if they lived in the highest quartile of housing code violation tracts, compared to those living in the lowest quartile. Integrating housing and health data could highlight at-risk areas and patients for targeted interventions.

Machine learning approaches to personalize early prediction of asthma exacerbations

Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

Dupilumab efficacy and safety in adults with uncontrolled persistent asthma despite use of medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist: a randomised double-blind placebo-controlled pivotal phase 2b dose-ranging trial

BACKGROUND

Dupilumab, a fully human anti-interleukin-4 receptor α monoclonal antibody, inhibits interleukin-4 and interleukin-13 signalling, key drivers of type-2-mediated inflammation. Adults with uncontrolled persistent asthma who are receiving medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist require additional treatment options as add-on therapy. We aimed to assess the efficacy and safety of dupilumab as add-on therapy in patients with uncontrolled persistent asthma on medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist, irrespective of baseline eosinophil count.

METHODS

We did this randomised, double-blind, placebo-controlled, parallel-group, pivotal phase 2b clinical trial at 174 study sites across 16 countries or regions. Adults (aged ≥18 years) with an asthma diagnosis for 12 months or more based on the Global Initiative for Asthma 2009 Guidelines receiving treatment with medium-to-high-dose inhaled corticosteroids plus a long-acting β2 agonist were eligible for participation. Patients were randomly assigned (1:1:1:1:1) to receive subcutaneous dupilumab 200 mg or 300 mg every 2 weeks or every 4 weeks, or placebo, over a 24-week period. The primary endpoint was change from baseline at week 12 in forced expiratory volume in 1 s (FEV1 in L) in patients with baseline blood eosinophil counts of at least 300 eosinophils per μL assessed in the intention-to-treat population. Safety outcomes were assessed in all patients that received at least one dose or part of a dose of study drug. This trial is registered at ClinicalTrials.gov, number NCT01854047, and with the EU Clinical Trials Register, EudraCT number 2013-000856-16.

FINDINGS

769 patients (158 in the placebo group and 611 in the dupilumab groups) received at least one dose of study drug. In the subgroup with at least 300 eosinophils per μL, the greatest increases (200 mg every 2 weeks, p=0·0008; 300 mg every 2 weeks, p=0·0063) in FEV1 compared with placebo were observed at week 12 with doses every 2 weeks in the 300 mg group (mean change 0·39 L [SE 0·05]; mean difference 0·21 [95% CI 0·06-0·36; p=0·0063]) and in the 200 mg group (mean change 0·43 L [SE 0·05]; mean difference 0·26 [0·11-0·40; p=0·0008]) compared with placebo (0·18 L [SE 0·05]). Similar significant increases were observed in the overall population and in the fewer than 300 eosinophils per μL subgroup (overall population: 200 mg every 2 weeks, p<0·0001; 300 mg every 2 weeks, p<0·0001; <300 eosinophils per μL: 200 mg every 2 weeks, p=0·0034; 300 mg every 2 weeks, p=0·0086), and were maintained to week 24. Likewise, dupilumab every 2 weeks produced the greatest reductions in annualised rates of exacerbation in the overall population (70-70·5%), the subgroup with at least 300 eosinophils per μL (71·2-80·7%), and the subgroup with fewer than 300 eosinophils per μL (59·9-67·6%). The most common adverse events with dupilumab compared with placebo were upper respiratory tract infections (33-41% vs 35%) and injection-site reactions (13-26% vs 13%).

INTERPRETATION

Dupilumab increased lung function and reduced severe exacerbations in patients with uncontrolled persistent asthma irrespective of baseline eosinophil count and had a favourable safety profile, and hence in addition to inhaled corticosteroids plus long-acting β2-agonist therapy could improve the lives of patients with uncontrolled persistent asthma compared with standard therapy alone.

FUNDING

Sanofi-Genzyme and Regeneron Pharmaceuticals.

Predictors of asthma exacerbation among patients with poorly controlled asthma despite inhaled corticosteroid treatment

BACKGROUND

Asthma exacerbations are associated with decreased quality of life and increased health care usage. Identification of characteristics that predict increased risk of future exacerbations in patients with suboptimal control of asthma could guide treatment decisions.

OBJECTIVE

To examine patient characteristics associated with risk of asthma exacerbations in patients with uncontrolled persistent asthma.

METHODS

A retrospective analysis of adults and children with inadequately controlled asthma despite asthma controller therapy and enrolled in 2 randomized trials was conducted. Baseline characteristics of subjects who experienced an asthma exacerbation during the treatment period were compared with those of subjects who did not experience an exacerbation.

RESULTS

Of 718 subjects (402 adults and 295 children), 108 adults (27%) and 110 children (37%) experienced an asthma exacerbation during the study period. Unscheduled health care visits for asthma or use of oral corticosteroids in the previous year were significantly associated with asthma exacerbation during the study period (P < .01). Adult subjects who experienced an exacerbation had significantly lower forced expiratory volume in 1 second compared with those who did not (2.3 vs 2.5 L, respectively, P = .02). Children who experienced an exacerbation had lower baseline pre- and post-bronchodilator ratios of forced expiratory volume in 1 second to forced vital capacity (77% vs 81%, P < .01; 82% vs 86%, P < .001, respectively). Symptom scores on validated questionnaires were significantly worse in adults but not in children who developed an exacerbation.

CONCLUSION

Spirometric measurements can help identify adults and children at increased risk for asthma exacerbation. Symptom scores could be helpful in identifying adults who are at high risk for exacerbations but could be less helpful in children.

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods

Background

Chronic diseases affect 52% of Americans and consume 86% of health care costs. A small portion of patients consume most health care resources and costs. More intensive patient management strategies, such as case management, are usually more effective at improving health outcomes, but are also more expensive. To use limited resources efficiently, risk stratification is commonly used in managing patients with chronic diseases, such as asthma, chronic obstructive pulmonary disease, diabetes, and heart disease. Patients are stratified based on predicted risk with patients at higher risk given more intensive care. The current risk-stratified patient management approach has 3 limitations resulting in many patients not receiving the most appropriate care, unnecessarily increased costs, and suboptimal health outcomes. First, using predictive models for health outcomes and costs is currently the best method for forecasting individual patient’s risk. Yet, accuracy of predictive models remains poor causing many patients to be misstratified. If an existing model were used to identify candidate patients for case management, enrollment would miss more than half of those who would benefit most, but include others unlikely to benefit, wasting limited resources. Existing models have been developed under the assumption that patient characteristics primarily influence outcomes and costs, leaving physician characteristics out of the models. In reality, both characteristics have an impact. Second, existing models usually give neither an explanation why a particular patient is predicted to be at high risk nor suggestions on interventions tailored to the patient’s specific case. As a result, many high-risk patients miss some suitable interventions. Third, thresholds for risk strata are suboptimal and determined heuristically with no quality guarantee.

Objective

The purpose of this study is to improve risk-stratified patient management so that more patients will receive the most appropriate care.

Methods

This study will (1) combine patient, physician profile, and environmental variable features to improve prediction accuracy of individual patient health outcomes and costs; (2) develop the first algorithm to explain prediction results and suggest tailored interventions; (3) develop the first algorithm to compute optimal thresholds for risk strata; and (4) conduct simulations to estimate outcomes of risk-stratified patient management for various configurations. The proposed techniques will be demonstrated on a test case of asthma patients.

Results

We are currently in the process of extracting clinical and administrative data from an integrated health care system’s enterprise data warehouse. We plan to complete this study in approximately 5 years.

Conclusions

Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs.

Predicting asthma control deterioration 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.

The metabolomics of asthma control: a promising link between genetics and disease

Short-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative "omics" approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), - using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites-monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.