Validation of childhood asthma predictive tools: A systematic review

An integrated molecular risk score early in life for subsequent childhood asthma risk

BACKGROUND

Numerous children present with early wheeze symptoms, yet solely a subgroup develops childhood asthma. Early identification of children at risk is key for clinical monitoring, timely patient-tailored treatment, and preventing chronic, severe sequelae. For early prediction of childhood asthma, we aimed to define an integrated risk score combining established risk factors with genome-wide molecular markers at birth, complemented by subsequent clinical symptoms/diagnoses (wheezing, atopic dermatitis, food allergy).

METHODS

Three longitudinal birth cohorts (PAULINA/PAULCHEN, n = 190 + 93 = 283, PASTURE, n = 1133) were used to predict childhood asthma (age 5-11) including epidemiological characteristics and molecular markers: genotype, DNA methylation and mRNA expression (RNASeq/NanoString). Apparent (ap) and optimism-corrected (oc) performance (AUC/R2) was assessed leveraging evidence from independent studies (Naïve-Bayes approach) combined with high-dimensional logistic regression models (LASSO).

RESULTS

Asthma prediction with epidemiological characteristics at birth (maternal asthma, sex, farm environment) yielded an ocAUC = 0.65. Inclusion of molecular markers as predictors resulted in an improvement in apparent prediction performance, however, for optimism-corrected performance only a moderate increase was observed (upto ocAUC = 0.68). The greatest discriminate power was reached by adding the first symptoms/diagnosis (up to ocAUC = 0.76; increase of 0.08, p = .002). Longitudinal analysis of selected mRNA expression in PASTURE (cord blood, 1, 4.5, 6 years) showed that expression at age six had the strongest association with asthma and correlation of genes getting larger over time (r = .59, p < .001, 4.5-6 years).

CONCLUSION

Applying epidemiological predictors alone showed moderate predictive abilities. Molecular markers from birth modestly improved prediction. Allergic symptoms/diagnoses enhanced the power of prediction, which is important for clinical practice and for the design of future studies with molecular markers.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Bacteria and viruses and their role in the preschool wheeze to asthma transition

Wheezing is the cardinal symptom of asthma; its presence early in life, mostly caused by viral infections, is a major risk factor for the establishment of persistent or recurrent disease. Early-life wheezing and asthma exacerbations are triggered by common respiratory viruses, mainly rhinoviruses (RV), and to a lesser extent, respiratory syncytial virus, parainfluenza, human metapneumovirus, coronaviruses, adenoviruses, influenza, and bocavirus. The excess presence of bacteria, several of which are part of the microbiome, has also been identified in association with wheezing and acute asthma exacerbations, including haemophilus influenza, streptococcus pneumoniae, moraxella catarrhalis, mycoplasma pneumoniae, and chlamydophila pneumonia. While it is not clear when asthma starts, its characteristics develop over time. Airway remodeling already appears between the ages of 1 and 3 years of age even prior to the presence of atopic inflammation or an asthma diagnosis. The role of genetic defect or variations hampering the airway epithelium in response to environmental stimuli and severe disease morbidity are now considered as major determinants for early structural changes. Repeated viral infections can induce and perpetuate airway hyperresponsiveness. Allergic sensitization, that often precedes infection-induced wheezing, shifts inflammation toward type-2, while common respiratory infections themselves promote type-2 inflammation. Nevertheless, most children who wheeze with viral infections during infancy and during preschool years do not develop persistent asthma. Multiple factors, including illness severity, viral etiology, allergic sensitization, and the exposome, are associated with disease persistence. Here, we summarize current knowledge and developments in infection epidemiology of asthma in children, describing the known impact of each individual agent and mechanisms of transition from recurrent wheeze to asthma.

Pre-asthma: a useful concept for prevention and disease-modification? A EUFOREA paper. Part 1-allergic asthma

Asthma, which affects some 300 million people worldwide and caused 455,000 deaths in 2019, is a significant burden to suffers and to society. It is the most common chronic disease in children and represents one of the major causes for years lived with disability. Significant efforts are made by organizations such as WHO in improving the diagnosis, treatment and monitoring of asthma. However asthma prevention has been less studied. Currently there is a concept of pre- diabetes which allows a reduction in full blown diabetes if diet and exercise are undertaken. Similar predictive states are found in Alzheimer's and Parkinson's diseases. In this paper we explore the possibilities for asthma prevention, both at population level and also investigate the possibility of defining a state of pre-asthma, in which intensive treatment could reduce progression to asthma. Since asthma is a heterogeneous condition, this paper is concerned with allergic asthma. A subsequent one will deal with late onset eosinophilic asthma.

Clinical Predictors of Severe Exacerbations in Pediatric Patients With Recurrent Wheezing

Introduction Wheezing is common in preschool-aged children, affecting about half of all children within their first six years of life. Children who have recurrent wheezing experience disease-related morbidity, including increased emergency visits and hospitalizations. Early-life lower respiratory tract viral infections are linked to recurrent wheezing and eventual asthma onset. Identifying high-risk children is crucial, with the frequency and severity of wheezing episodes being good predictors of long-term outcomes. Aim To identify predictors of severe exacerbations in children with recurrent wheezing. Methods We conducted a retrospective cohort study involving 168 pediatric patients with recurrent wheezing followed up at our outpatient clinic. The outcome of interest was the occurrence of a severe exacerbation, defined as any exacerbation requiring hospitalization and the need for supplemental oxygenation or ventilatory support. Results The median age of the first wheezing exacerbation was five months, with a predominance of the male gender. Approximately two-thirds of the patients had a family history of atopy. Comorbid allergic rhinitis and atopic dermatitis were present in 15.4% and 16.7% of patients, respectively. Twenty percent of patients had a severe wheezing exacerbation as the first form of presentation, and 30% presented at least one severe exacerbation from the first presentation to the last follow-up. Patients with severe exacerbations were younger at the first episode (median age 4 months, IQR 2-7, versus 7 months, IQR 4-12, p=0.027) and more frequently had a family history of atopy (71.7% versus 55.6%, p=0.050). In this cohort, patients who initially presented with a severe episode are at increased risk of incident severe exacerbations during follow-up, HR 2.24 (95%CI 1.01-4.95). Conclusions We know that the severity of exacerbations in children with recurrent wheezing correlates with the long-term outcomes of the disease. Therefore, preventing severe exacerbations can positively impact the prognosis of these patients. In this analysis, we found independent predictors of severe exacerbations to be the first clinical episode before the age of three months and a family history of atopy. We also found that patients whose initial presentation was severe have a higher risk of new severe exacerbations. Therefore, these subgroups of patients should be closely monitored by pediatricians.

Early Prediction of Asthma

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.

Phenotype and endotype based treatment of preschool wheeze

INTRODUCTION

Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease.

AREAS COVERED

We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored.

EXPERT OPINION

Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.

Blood Eosinophils for Prediction of Exacerbation in Preschool Children With Recurrent Wheezing

BACKGROUND

Although clinical features of type 2 inflammation have been associated with poorer longitudinal outcomes in preschool children with recurrent wheezing, it remains difficult to predict which children are at highest risk for poor outcomes during a routine clinical encounter.

OBJECTIVE

We tested the hypothesis that prespecified cut points of blood eosinophil counts would predict exacerbation and treatment response outcomes in preschool children with recurrent wheezing and that prediction could be improved with the addition of a second biomarker.

METHODS

Data from 3 clinical trials of 1,074 preschool children aged 12 to 71 months with recurrent wheezing were merged. The primary outcome was the occurrence of any exacerbation during follow-up. Secondary outcomes included the annualized rate of wheezing exacerbations and the occurrence of any exacerbation requiring hospitalization. Exploratory analyses focused on exacerbation outcomes, offline exhaled nitric oxide concentrations, and caregiver-reported asthma control scores after inhaled corticosteroid treatment initiation.

RESULTS

Each blood eosinophil cut point was associated with increased odds of exacerbation, higher exacerbation rates, and greater hospitalization occurrence in preschool children with recurrent wheezing. However, outcome detection was improved in children with more elevated blood eosinophil counts. Addition of a second biomarker of type 2 inflammation improved outcome detection and was further associated with an improved response to initiation of daily inhaled corticosteroids in exploratory analyses. However, the specificity of blood eosinophils was poor.

CONCLUSIONS

Although validation studies are warranted, blood eosinophil cut points may be useful for clinical assessment and future studies of exacerbation and treatment response in preschool children with recurrent wheezing.

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

Asthma is a complex disease that varies widely in prevalence across populations. The extent to which genetic variation contributes to these disparities is unclear, as the genetics underlying asthma have been investigated primarily in populations of European descent. As part of the Global Biobank Meta-analysis Initiative, we conducted a large-scale genome-wide association study of asthma (153,763 cases and 1,647,022 controls) via meta-analysis across 22 biobanks spanning multiple ancestries. We discovered 179 asthma-associated loci, 49 of which were not previously reported. Despite the wide range in asthma prevalence among biobanks, we found largely consistent genetic effects across biobanks and ancestries. The meta-analysis also improved polygenic risk prediction in non-European populations compared with previous studies. Additionally, we found considerable genetic overlap between age-of-onset subtypes and between asthma and comorbid diseases. Our work underscores the multi-factorial nature of asthma development and offers insight into its shared genetic architecture.

Implementing predictive tools in surgery: A narrative review in the context of orthopaedic surgery

Clinical predictive tools are a topic gaining interest. Many tools are developed each year to predict various outcomes in medicine and surgery. However, the proportion of predictive tools that are implemented in clinical practice is small in comparison to the total number of tools developed. This narrative review presents key principles to guide the translation of predictive tools from academic bodies of work into useful tools that complement clinical practice. Our review identified the following principles: (1) identifying a clinical gap, (2) selecting a target user or population, (3) optimizing predictive tool performance, (4) externally validating predictive tools, (5) marketing and disseminating the tool, (6) navigating the challenges of integrating a tool into existing healthcare systems, and (7) developing an ongoing monitoring and evaluation strategy. Although the review focuses on examples in orthopaedic surgery, the principles can be applied to other disciplines in medicine and surgery.

External validation of the Predicting Asthma Risk in Children tool in a clinical cohort

INTRODUCTION

The Predicting Asthma Risk in Children (PARC) tool uses questionnaire-based respiratory symptoms collected from preschool children to predict asthma risk 5 years later. The tool was developed and validated in population cohorts but not validated using a clinical cohort. We aimed to externally validate the PARC tool in a pediatric pulmonology clinic setting.

METHODS

The Swiss Paediatric Airway Cohort (SPAC) is a prospective cohort of children seen in pediatric pulmonology clinics across Switzerland. We included children aged 1-6 years with cough or wheeze at baseline who completed the 2-year follow-up questionnaire. The outcome was defined as current wheeze plus use of asthma medication. We assessed performance using: sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV), area under the curve (AUC), scaled Brier's score, and Nagelkerke's R² scores. We compared performance in SPAC to that in the original population, the Leicester Respiratory Cohort (LRC).

RESULTS

Among 346 children included, 125 (36%) reported the outcome after 2 years. At a PARC score of 4: sensitivity was higher (95% vs. 79%), specificity lower (14% vs. 57%), and NPV and PPV comparable (0.84 vs. 0.87 and 0.37 vs. 0.42) in SPAC versus LRC. AUC (0.71 vs. 0.78), R² (0.18 vs. 0.28) and Brier's scores (0.13 vs. 0.22) were lower in SPAC.

CONCLUSIONS

The PARC tool shows some clinical utility, particularly for ruling out the development of asthma in young children, but performance limitations highlight the need for new prediction tools to be developed specifically for the clinical setting.

Predicting allergic diseases in children using genome-wide association study (GWAS) data and family history

The recent rise in the prevalence of chronic allergic diseases among children has increased disease burden and reduced quality of life, especially for children with comorbid allergic diseases. Predicting the occurrence of allergic diseases can help prevent its onset for those in high risk groups. Herein, we aimed to construct prediction models for asthma, atopic dermatitis (AD), and asthma-AD comorbidity (also known as atopic march) using a genome-wide association study (GWAS) and family history data from patients of Korean heritage. Among 973 patients and 481 healthy controls, we evaluated single nucleotide polymorphism (SNP) heritability for each disease using genome-based restricted maximum likelihood (GREML) analysis. We then compared the performance of prediction models constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and penalized ridge regression methods. Our results indicate that the addition of family history risk scores to the prediction model greatly increase the predictability of asthma and asthma-AD comorbidity. However, prediction of AD was mostly attributable to GWAS SNPs.

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

Severe asthma exacerbations are the primary cause of morbidity and mortality in children with asthma. Accurate prediction of children at risk for severe exacerbations, defined as those requiring systemic corticosteroids, emergency department visit, and/or hospitalization, would considerably reduce health care utilization and improve symptoms and quality of life. Substantial progress has been made in identifying high-risk exacerbation-prone children. Known risk factors for exacerbations include demographic characteristics (ie, low income, minority race/ethnicity), poor asthma control, environmental exposures (ie, aeroallergen exposure/sensitization, concomitant viral infection), inflammatory biomarkers, genetic polymorphisms, and markers from other "omic" technologies. The strongest risk factor for a future severe exacerbation remains having had one in the previous year. Combining risk factors into composite scores and use of advanced predictive analytic techniques such as machine learning are recent methods used to achieve stronger prediction of severe exacerbations. However, these methods are limited in prediction efficiency and are currently unable to predict children at risk for impending (within days) severe exacerbations. Thus, we provide a commentary on strategies that have potential to allow for accurate and reliable prediction of children at risk for impending exacerbations. These approaches include implementation of passive, real-time monitoring of impending exacerbation predictors, use of population health strategies, prediction of severe exacerbation responders versus nonresponders to conventional exacerbation management, and considerations for preschool-age children who can be especially high risk. Rigorous prediction and prevention of severe asthma exacerbations is needed to advance asthma management and improve the associated morbidity and mortality.

Utility of basophil activation test for predicting the outcome of wheezing in children: a pilot study

BACKGROUND

No reliable biological marker for the diagnosis of asthma in younger children is currently available. In this study, we analyzed the differences in basophil activation test (BAT) results among children with recurrent wheezing episodes who had different asthma outcomes.

RESULTS

A prospective cohort study was conducted in children aged under 5 years who visited our pediatric respiratory clinic and ward for wheezing. After enrollment, the participants provided samples for a CD63-based BAT performed using an inhalant allergen mixture as a stimulant. Histories of personal allergic diseases and family allergic diseases were evaluated by using a questionnaire. All participants were followed up for 2 years, and their asthma outcomes were evaluated at the end of the follow-up period. The correlation between the BAT results and asthma outcomes was analyzed. Of the 45 originally enrolled children, 38 completed both the follow-up and a BAT. After stimulation with the inhalant mixture, the CD63 expression on basophils and the rate of positive CD63-based BAT results in children diagnosed with asthma were both significantly higher than those in children who were not diagnosed with asthma (p < 0.05 and p < 0.01, respectively). For the prediction of asthma, the positive predictive value and negative predictive value of CD63-based BAT was 71.8 and 69.2%, respectively. The positive likelihood ratio and negative likelihood ratio of CD63-based BAT were 1.70 and 0.3, respectively.

CONCLUSIONS

Our pilot study indicates that CD63-based BAT has potential clinical value for predicting asthma outcome in young children with wheezing episodes.

Helminthostachys zeylanica Water Extract Ameliorates Airway Hyperresponsiveness and Eosinophil Infiltration by Reducing Oxidative Stress and Th2 Cytokine Production in a Mouse Asthma Model

Helminthostachys zeylanica is a traditional folk herb used to improve inflammation and fever in Taiwan. Previous studies showed that H. zeylanica extract could ameliorate lipopolysaccharide-induced acute lung injury in mice. The aim of this study was to investigate whether H. zeylanica water (HZW) and ethyl acetate (HZE) extracts suppressed eosinophil infiltration and airway hyperresponsiveness (AHR) in asthmatic mice, and decreased the inflammatory response and oxidative stress in tracheal epithelial cells. Human tracheal epithelial cells (BEAS-2B cells) were pretreated with various doses of HZW or HZE (1 μg/ml-10 μg/ml), and cell inflammatory responses were induced with IL-4/TNF-α. In addition, female BALB/c mice sensitized with ovalbumin (OVA), to induce asthma, were orally administered with HZW or HZE. The result demonstrated that HZW significantly inhibited the levels of proinflammatory cytokines, chemokines, and reactive oxygen species in activated BEAS-2B cells. HZW also decreased ICAM-1 expression and blocked monocytic cells from adhering to inflammatory BEAS-2B cells in vitro. Surprisingly, HZW was more effective than HZE in suppressing the inflammatory response in BEAS-2B cells. Our results demonstrated that HZW significantly decreased AHR and eosinophil infiltration, and reduced goblet cell hyperplasia in the lungs of asthmatic mice. HZW also inhibited oxidative stress and reduced the levels of Th2 cytokines in bronchoalveolar lavage fluid. Our findings suggest that HZW attenuated the pathological changes and inflammatory response of asthma by suppressing Th2 cytokine production in OVA-sensitized asthmatic mice.

Prediction models for childhood asthma: A systematic review

BACKGROUND

The inability to objectively diagnose childhood asthma before age five often results in both under-treatment and over-treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school-age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school-age asthma.

METHODS

Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school-age children (6-13 years). Validation studies were evaluated as a secondary objective.

RESULTS

Twenty-four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression-based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression-based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62-0.83).

CONCLUSION

Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school-age asthma prediction.