Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow

Emergency management and asthma risk in young Medicaid-enrolled children with recurrent wheeze

OBJECTIVES

To describe clinical characteristics of young children presenting to the emergency department (ED) for early recurrent wheeze, and determine factors associated with subsequent persistent wheeze and risk for early childhood asthma.

METHODS

Retrospective cohort study of Medicaid-enrolled children 0-3 years old with an index ED visit for wheeze (e.g. bronchiolitis, reactive airway disease) from 2009 to 2013, and at least one prior documented episode of wheeze at an ED or primary care visit. The primary outcome was persistent wheeze between 4 and 6 years of age. Demographics and clinical characteristics were collected from the index ED visit. Logistic regression was used to estimate the association between potential risk factors and subsequent persistent wheeze.

RESULTS

During the study period, 41,710 children presented to the ED for recurrent wheeze. Mean age was 1.3 years; 59% were male, 42% Black, and 6% Hispanic. At index ED visits, the most common diagnosis was acute bronchiolitis (40%); 77% of children received an oral corticosteroid prescription. Between 4 and 6 years of age, 11,708 (28%) children had persistent wheeze. A greater number of wheezing episodes was associated with an increased odds of ED treatment with asthma medications. Subsequent persistent wheeze was associated with male sex, Black race, atopy, prescription for bronchodilators or corticosteroids, and greater number of visits for wheeze.

CONCLUSIONS

Young children with persistent wheeze are at risk for childhood asthma. Thus, identification of risk factors associated with persistent wheeze in young children with recurrent wheeze might aid in early detection of asthma and initiation of preventative therapies.

Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review

BACKGROUND

The integration of machine learning (ML) in predicting asthma-related outcomes in children presents a novel approach in pediatric health care.

OBJECTIVE

This scoping review aims to analyze studies published since 2019, focusing on ML algorithms, their applications, and predictive performances.

METHODS

We searched Ovid MEDLINE ALL and Embase on Ovid, the Cochrane Library (Wiley), CINAHL (EBSCO), and Web of Science (core collection). The search covered the period from January 1, 2019, to July 18, 2023. Studies applying ML models in predicting asthma-related outcomes in children aged <18 years were included. Covidence was used for citation management, and the risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

From 1231 initial articles, 15 met our inclusion criteria. The sample size ranged from 74 to 87,413 patients. Most studies used multiple ML techniques, with logistic regression (n=7, 47%) and random forests (n=6, 40%) being the most common. Key outcomes included predicting asthma exacerbations, classifying asthma phenotypes, predicting asthma diagnoses, and identifying potential risk factors. For predicting exacerbations, recurrent neural networks and XGBoost showed high performance, with XGBoost achieving an area under the receiver operating characteristic curve (AUROC) of 0.76. In classifying asthma phenotypes, support vector machines were highly effective, achieving an AUROC of 0.79. For diagnosis prediction, artificial neural networks outperformed logistic regression, with an AUROC of 0.63. To identify risk factors focused on symptom severity and lung function, random forests achieved an AUROC of 0.88. Sound-based studies distinguished wheezing from nonwheezing and asthmatic from normal coughs. The risk of bias assessment revealed that most studies (n=8, 53%) exhibited low to moderate risk, ensuring a reasonable level of confidence in the findings. Common limitations across studies included data quality issues, sample size constraints, and interpretability concerns.

CONCLUSIONS

This review highlights the diverse application of ML in predicting pediatric asthma outcomes, with each model offering unique strengths and challenges. Future research should address data quality, increase sample sizes, and enhance model interpretability to optimize ML utility in clinical settings for pediatric asthma management.

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.

Prediction of Asthma Exacerbations in Children

Asthma exacerbations are common in asthmatic children, even among those with good disease control. Asthma attacks result in the children and their parents missing school and work days; limit the patient's social and physical activities; and lead to emergency department visits, hospital admissions, or even fatal events. Thus, the prompt identification of asthmatic children at risk for exacerbation is crucial, as it may allow for proactive measures that could prevent these episodes. Children prone to asthma exacerbation are a heterogeneous group; various demographic factors such as younger age, ethnic group, low family income, clinical parameters (history of an exacerbation in the past 12 months, poor asthma control, poor adherence to treatment, comorbidities), Th2 inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered. This paper aims to review the existing evidence regarding the predictors of asthma exacerbations in children and offer practical monitoring guidance for promptly recognizing patients at risk.

Identifying asthma-related risks during hospitalization using the child asthma risk assessment tool

Objective: The Child Asthma Risk Assessment Tool (CARAT) identifies risk factors for asthma morbidity. We hypothesized that CARAT-identified risk factors (using a CARAT adapted for inpatient use) would be associated with future healthcare utilization and would identify areas for intervention.Methods: We reviewed CARAT data collected during pediatric asthma admissions from 2010-2015, assessing for risk factors in environmental, medical, and social domains and providing prompts for inpatient (specialist consultation or social services engagement) and post-discharge interventions (home care visit or home environmental assessment). Confirmatory factor analysis identified groups of CARAT-identified risk factors with similar effects on healthcare utilization (latent factors). Structural equation models then evaluated relationships between latent factors and future utilization.Results: There were 2731 unique patients admitted for asthma exacerbations; 1015 (37%) had complete CARAT assessments and were included in analyses. Those with incomplete CARAT assessments were more often younger and privately-insured. CARAT-identified risk factors across domains were common in children hospitalized for exacerbations. Risks in the environmental domain were most common. Inpatient asthma consults by pulmonologists or allergists and home care referrals were the most frequent interventions indicated (62%, 628/1015, and 50%, 510/1015, respectively). Two latent factors were positively associated with healthcare utilization in the year after index stay - social stressors and known/suspected allergies (both p < 0.05). Stratified analyses analyzing data just from those children with prior healthcare utilization also indicated known/suspected allergies to be positively associated with future utilization.Conclusions: Inpatient interventions to address social stressors and allergic profiles may be warranted to reduce subsequent asthma morbidity.

Composite predictive models for asthma exacerbations or asthma deterioration in pediatric asthmatic patients: A systematic review of the literature

A variety of factors have shown to be useful in predicting which children are at high risk for future asthma exacerbations, some of them combined into composite predictive models. The objective of the present review was to systematically identify all the available published composite predictive models developed for predicting which children are at high risk for future asthma exacerbations or asthma deterioration. A systematic search of the literature was performed to identify studies in which a composite predictive model developed for predicting which children are at high risk for future asthma exacerbations or asthma deterioration was described. Methodological quality assessment was performed using accepted criteria for prediction rules and prognostic models. A total of 18 articles, describing a total of 17 composite predictive models were identified and included in the review. The number of predictors included in the models ranged from 2-149. Upon analyzing the content of the models, use of healthcare services for asthma and prescribed or dispensed asthma medications were the most frequently used items (in 8/17, 47.0% of the models). Seven (41.2%) models fulfilled all the quality criteria considered in our evaluation. The identified models may help clinicians dealing with asthmatic children to identify which children are at a higher risk for future asthma exacerbations or asthma deterioration, therefore targeting and/or reinforcing specific interventions for these children in an attempt to prevent exacerbations or deterioration of the disease.

Asthma Quality Measurement and Adverse Outcomes in Medicaid-Enrolled Children

OBJECTIVES

To determine the association between the asthma medication ratio (AMR) quality measure and adverse outcomes among Medicaid-enrolled children with asthma in Arkansas, given concerns regarding the utility of the AMR in evaluating pediatric risk of asthma-related adverse events (AAEs).

METHODS

We used the Arkansas All-Payer Claims Database to identify Medicaid-enrolled children with asthma using a nonrestrictive case definition and additionally using the standard Healthcare Effectiveness Data and Information Set (HEDIS) persistent asthma definition. We assessed the AMR using the traditional dichotomous HEDIS AMR categorization and across 4 expanded AMR categories. Regression models assessed associations between AMR and AAE including hospitalization and emergency department utilization, with models conducted overall and by race and ethnicity.

RESULTS

Of the 22 788 children in the analysis, 9.0% had an AAE (6.7% asthma-related emergency department visits; 3.0% asthma-related hospitalizations). We found poor correlation between AMR and AAE, with higher rates of AAE (10.5%) among children with AMR ≥0.5 compared with AMR <0.5 (8.5%; P < .001), and similar patterns stratified by racial and ethnic subgroups. Expanded AMR categorization revealed notable differences in associations between AMR and AAEs, compared with traditional dichotomous categorization, with worse performance in Black children.

CONCLUSIONS

The AMR performed poorly in identifying risk of adverse outcomes among Medicaid-enrolled children with asthma. These findings underscore concerns of the utility of the AMR in population health management and reliance on restrictive HEDIS definitions. New population health frameworks incorporating broader considerations that accurately identify at-risk children are needed to improve equity in asthma management and outcomes.

Clinical performance of spirometry and respiratory oscillometry for prediction of severe exacerbations in schoolchildren with asthma

OBJECTIVE

To determine the performance of spirometry and respiratory oscillometry (RO) in the prediction of severe asthma exacerbations (SAEs) in children.

METHODS

In a prospective study, 148 children (age 6-14 years) with asthma were assessed with RO, spirometry and a bronchodilator (BD) test. Based on the findings of spirometry and the BD test, they were classified into three phenotypes: air trapping (AT), airflow limitation (AFL) and normal. Twelve weeks later, they were re-evaluated in relation to the occurrence of SAEs. We analysed the performance of RO, spirometry and AT/AFL phenotypes for prediction of SAEs by means of positive and negative likelihood ratios, ROC curves with the corresponding areas under the curve (AUCs) and a multivariate analysis adjusted for potential confounders.

RESULTS

During the follow-up, 7.4% of patients had SAEs, and there were differences between phenotypes (normal, 2.4%; AFL, 17.9%; AT, 22.2%, P = .005). The best AUC corresponded to the forced expiratory flow between 25% and 75% of vital capacity (FEF_25-75): 0.787; 95% confidence interval, 0.600-0.973. Other significant AUCs were those for the reactance area (AX), forced expiratory volume in the first second (FEV₁), the post-BD change in forced vital capacity (FVC), and the FEV₁/FVC ratio. All of the variables had a low sensitivity for prediction of SAEs. The AT phenotype had the best specificity (93.8%; 95% CI, 87.9-97.0), but the positive and negative likelihood ratios were both significant only for the FEF_25-75. In the multivariate analysis, only some spirometry parameters were significative for prediction of SAEs (AT phenotype, FEF_25-75 and FEV₁/FVC).

CONCLUSIONS

Spirometry performed better than RO for prediction of SAEs in the medium term in schoolchildren with asthma.

Alpha-1 antitrypsin deficiency and Pi*S and Pi*Z SERPINA1 variants are associated with asthma exacerbations

INTRODUCTION AND OBJECTIVES

Asthma is a chronic inflammatory disease of the airways. Asthma patients may experience potentially life-threatening episodic flare-ups, known as exacerbations, which may significantly contribute to the asthma burden. The Pi*S and Pi*Z variants of the SERPINA1 gene, which usually involve alpha-1 antitrypsin (AAT) deficiency, had previously been associated with asthma. The link between AAT deficiency and asthma might be represented by the elastase/antielastase imbalance. However, their role in asthma exacerbations remains unknown. Our objective was to assess whether SERPINA1 genetic variants and reduced AAT protein levels are associated with asthma exacerbations.

MATERIALS AND METHODS

In the discovery analysis, SERPINA1 Pi*S and Pi*Z variants and serum AAT levels were analyzed in 369 subjects from La Palma (Canary Islands, Spain). As replication, genomic data from two studies focused on 525 Spaniards and publicly available data from UK Biobank, FinnGen, and GWAS Catalog (Open Targets Genetics) were analyzed. The associations between SERPINA1 Pi*S and Pi*Z variants and AAT deficiency with asthma exacerbations were analyzed with logistic regression models, including age, sex, and genotype principal components as covariates.

RESULTS

In the discovery, a significant association with asthma exacerbations was found for both Pi*S (odds ratio [OR]=2.38, 95% confidence interval [CI]= 1.40-4.04, p-value=0.001) and Pi*Z (OR=3.49, 95%CI=1.55-7.85, p-value=0.003)Likewise, AAT deficiency was associated with a higher risk for asthma exacerbations (OR=5.18, 95%CI=1.58-16.92, p-value=0.007) as well as AAT protein levels (OR= 0.72, 95%CI=0.57-0.91, p-value=0.005). The Pi*Z association with exacerbations was replicated in samples from Spaniards with two generations of Canary Islander origin (OR=3.79, p-value=0.028), and a significant association with asthma hospitalizations was found in the Finnish population (OR=1.12, p-value=0.007).

CONCLUSIONS

AAT deficiency could be a potential therapeutic target for asthma exacerbations in specific populations.

Exacerbation-prone pediatric asthma is associated with arginine, lysine, and methionine pathway alterations

BACKGROUND

The asthma of some children remains poorly controlled, with recurrent exacerbations despite treatment with inhaled corticosteroids. Aside from prior exacerbations, there are currently no reliable predictors of exacerbation-prone asthma in these children and only a limited understanding of the potential underlying mechanisms.

OBJECTIVE

We sought to quantify small molecules in the plasma of children with exacerbation-prone asthma through mass spectrometry-based metabolomics. We hypothesized that the plasma metabolome of these children would differ from that of children with non-exacerbation-prone asthma.

METHODS

Plasma metabolites were extracted from 4 pediatric asthma cohorts (215 total subjects, with 41 having exacerbation-prone asthma) and detected with a mass spectrometer. High-confidence annotations were retained for univariate analysis and were confirmed by a sensitivity analysis in subjects receiving high-dose inhaled corticosteroids. Metabolites that varied by cohort were excluded. MetaboAnalyst software was used to identify pathways of interest. Concentrations were calculated by reference standardization.

RESULTS

We identified 32 unique, cohort-independent metabolites that differed in children with exacerbation-prone asthma compared to children with non-exacerbation-prone asthma. Comparison of metabolite concentrations to literature-reported values for healthy children revealed that most metabolites were decreased in both asthma groups, but more so in exacerbation-prone asthma. Pathway analysis identified arginine, lysine, and methionine pathways as most impacted.

CONCLUSIONS

Several plasma metabolites are perturbed in children with exacerbation-prone asthma and are largely related to arginine, lysine, and methionine pathways. While validation is needed, plasma metabolites may be potential biomarkers for exacerbation-prone asthma in children.

Machine learning: A modern approach to pediatric asthma

Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma.

Implementation of a Children's Safe Asthma Discharge Care Pathway Reduces the Risk of Future Asthma Attacks in Children-A Retrospective Quality Improvement Report

BACKGROUND

Many children attend Emergency Departments (ED) and Out of Hours (OoH) frequently for acute asthma. Follow up care is often suboptimal leaving these children at risk of a future attacks. We report on the development, implementation and evaluation of a safe asthma discharge care pathway (SADCP).

METHODS

This is a retrospective report on the development, implementation and evaluation of outcomes of a SADCP. The pathway was based on the Teach-to-goal educational methodology that supported the mastery correct inhaler technique and ability to action the personalized asthma action plan (PAAP). Children with frequent asthma attacks were entered as they were discharged from the Emergency Department or ward. The first training session occurred within 1-3 weeks of the index asthma attack with 2 further sessions in the following 8 weeks. Children exiting the pathway were discharged either back to primary care or to a hospital clinic.

RESULTS

81 children entered the pathway (median age 5 years) with 72 discharged from the ED and 9 from the medical wards of the Royal Belfast Hospital for Sick Children. At pathway entry 13% had correct inhaler technique, 10% had a Personalized Asthma Action Plan (PAAP), and 5% had >80% (45% >50%) repeat refill evidence of adherence to inhaled corticosteroid over the previous 12 months. On pathway exit all children demonstrated correct inhaler technique and were able to action their PAAP. One year later 51% and 95% had refill evidence of >80% and >50% adherence. Comparisons of the 12 months before and 12 months after exit from the pathway the median number of emergency ED or OoH asthma attendances and courses of oral corticosteroids reduced to zero with >75% having no attacks requiring this level of attention. Similar findings resulted when the SADCP was implemented in a district general hospital pediatric unit.

CONCLUSION

Implementing an asthma care pathway, using Teach-to-Goal skill training methods and frequent early reviews after an index asthma attack can reduce the future risk of asthma attacks in the next 6 to 12 months.

Evaluation of efficiency and safety of oral corticosteroid therapy in children patients with exacerbations of asthma: A protocol for systematic review and meta-analysis

BACKGROUND

Asthma is the most frequently occurring obstructive airway disease, it inflicts the highest morbidity among children. Among the paediatric populace, severe exacerbations of asthma are a common reason behind patient consultations and hospitalizations. Oral corticosteroids are a primary component in the treatment of asthma exacerbations; however, there is controversy regarding how corticosteroids functions.

METHODS

The present review will conduct a search on MEDLINE, EMBASE, Cochrane Library, China National Knowledge Infrastructure, and Chinese BioMedical Literature. The search will cover the databases from their beginning to May 2021. The search aims to identify all the randomized controlled studies on oral corticosteroids in treating children with asthma exacerbations. Two independent authors will choose studies, perform data extraction, and use an appropriate tool to assess the bias risk in the selected articles. Moreover, a sensitivity analysis will be performed to assess the robustness of the results. The RevMan (version 5.3) software will be employed to perform data synthesis and statistical analysis.

RESULTS

This study will examine the efficiency and safeness of oral corticosteroid therapy to treat children with asthma exacerbations by pooling the results of individual studies.

CONCLUSION

The findings of this study will provide vigorous evidence to judge whether oral corticosteroid therapy is an efficiency strategy to treat patients with asthmatic exacerbations.

OSF REGISTRATION NUMBER

May 20, 2021.osf.io/3ghjt. (https://osf.io/3ghjt/).