A simple asthma prediction tool for preschool children with wheeze or cough

The wheezy infant: A viewpoint from low-middle income countries

OBJECTIVE

To review the recent evidence in the literature of various aspects of recurrent/severe wheezing in children under 3 in low-middle income countries [LMICS].

SOURCES

A non-systematic review including articles in English. We mainly selected publications from the last 5 years. Studies on epidemiology, aetiology, diagnosis, treatment, and prevention were included in the search. We reviewed differential diagnoses of wheezing that focused on LMICS. We also reviewed aspects of prevention.

SUMMARY OF THE FINDINGS

Many epidemiological studies have shown a variable but significant number of wheezy infants [WI] cases in LMICS when compared to other countries. The differential diagnosis of causes of wheezing in this age group is mandatory, taking into account local facilities. Few treatment options have been well studied for this age group. In LMICS, a pragmatic approach could be considered, as described in the article. It is difficult to study primary prevention for WI and secondary prevention (mainly environmental) may have some impact. A schematic approach for recurrent wheezers is presented, which takes into account settings with limited resources.

CONCLUSION

Severely or recurrently wheezy children under 3 is a common clinical issue in LMICS. Studies on this age group are needed to reduce the significant morbidity. It may be possible to lower the high burden of wheezing in this age group by selecting the phenotype which may respond to inhaled steroids.

Artificial intelligence and wheezing in children: where are we now?

Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs. Moreover, in the last decade, ML algorithms have been applied in wheezing phenotyping, also contributing to identifying specific genes, and have been proven to even predict asthma in preschoolers. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.

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.

Genetics of preschool wheeze and its progression to childhood asthma

Wheezing is a common and heterogeneous condition in preschool children. In some countries, the prevalence can be as high as 30% and up to 50% of all children experience wheezing before the age of 6. Asthma often starts with preschool wheeze, but not all wheezing children will develop asthma at school age. At this moment, it is not possible to accurately predict which wheezing children will develop asthma. Recently, studying the genetics of wheeze and the childhood-onset of asthma have grown in interest. Childhood-onset asthma has a stronger heritability in comparison with adult-onset asthma. In early childhood asthma exacerbations, CDHR3, which encodes the receptor for Rhinovirus C, was identified, as well as IL33, and the 17q locus that includes GSDMB and ORMDL3 genes. The 17q locus is the strongest wheeze and childhood-onset asthma locus, and was shown to interact with many environmental factors, including smoking and infections. Finally, ANXA1 was recently associated with early-onset, persistent wheeze. ANXA1 may help resolve eosinophilic inflammation. Overall, despite its complexities, genetic approaches to unravel the early-onset of wheeze and asthma are promising, since these shed more light on mechanisms of childhood asthma-onset. Implicated genes point toward airway epithelium and its response to external factors, such as viral infections. However, the heterogeneity of wheeze phenotypes complicates genetic studies. It is therefore important to define accurate wheezing phenotypes and forge larger international collaborations to gain a better understanding of the pathways underlying early-onset asthma.

Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review

BACKGROUND

Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined.

OBJECTIVE

Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice.

METHODS

The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor.

RESULTS

In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent.

CONCLUSIONS

Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.

Prediction of adult asthma risk in early childhood using novel adult asthma predictive risk scores

BACKGROUND

Numerous risk scores have been developed to predict childhood asthma. However, they may not predict asthma beyond childhood. We aim to create childhood risk scores that predict development and persistence of asthma up to young adult life.

METHODS

The Isle of Wight Birth Cohort (n = 1456) was prospectively assessed up to 26 years of age. Asthma predictive scores were developed based on factors during the first 4 years, using logistic regression and tested for sensitivity, specificity and area under the curve (AUC) for prediction of asthma at (i) 18 and (ii) 26 years, and persistent asthma (PA) (iii) at 10 and 18 years, and (iv) at 10, 18 and 26 years. Models were internally and externally validated.

RESULTS

Four models were generated for prediction of each asthma outcome. ASthma PredIctive Risk scorE (ASPIRE)-1: a 2-factor model (recurrent wheeze [RW] and positive skin prick test [+SPT] at 4 years) for asthma at 18 years (sensitivity: 0.49, specificity: 0.80, AUC: 0.65). ASPIRE-2: a 3-factor model (RW, +SPT and maternal rhinitis) for asthma at 26 years (sensitivity: 0.60, specificity: 0.79, AUC: 0.73). ASPIRE-3: a 3-factor model (RW, +SPT and eczema at 4 years) for PA-18 (sensitivity: 0.63, specificity: 0.87, AUC: 0.77). ASPIRE-4: a 3-factor model (RW, +SPT at 4 years and recurrent chest infection at 2 years) for PA-26 (sensitivity: 0.68, specificity: 0.87, AUC: 0.80). ASPIRE-1 and ASPIRE-3 scores were replicated externally. Further assessments indicated that ASPIRE-1 can be used in place of ASPIRE-2-4 with same predictive accuracy.

CONCLUSION

ASPIRE predicts persistent asthma up to young adult life.

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.

Development and validation of a prediction model to predict school-age asthma in preschool children

OBJECTIVE

To develop and validate a clinical prediction model to identify school-age asthma in preschool asthmatic children.

STUDY DESIGN

In this retrospective prognosis cohort study, asthmatic children aged 3-5 years were enrolled with at least 2 years of follow-up, and their potential variables at baseline and the prognosis of school-age asthma were collected from medical records. A clinical prediction model was developed using Logistic regression. The performance of prediction model was assessed and quantified by discrimination of the area under the receiver operating characteristic curve (AUC) and calibration of Brier score. The model was validated by the temporal-validation method.

RESULTS

In the development dataset, 2748 preschool asthmatic children were included for model development, and 883 (32.13%) children were translated to school-age asthma. The independent prognostic variables with an increased risk for school-age asthma were used to develop the prediction model, including: age, parental asthma, early frequent wheezing, allergic rhinitis, eczema, allergic conjunctivitis, obesity, and aeroallergen of dust mite. While assessing model performance, the discrimination power of AUC was moderate [0.788 (0.770-0.805)] with sensitivity (81.5%) and specificity (60.9%), and the calibration of Brier score was 0.169, supporting the calibration ability. In the temporal-validation dataset of 583 preschool asthmatic children, our model showed satisfactory discrimination (AUC 0.818) and calibration (Brier score 0.150). The prediction model was presented by the web-based calculator (https://casthma.shinyapps.io/dynnomapp/) and a nomogram for clinical application.

CONCLUSION

In preschool asthmatic children, our prediction model could be used to predict the risk of school-age asthma.

Standardization of Reporting Obstructive Airway Disease in Children: A National Delphi Process

BACKGROUND

Pediatric pulmonologists report asthma and obstructive bronchitis in medical records in a variety of ways, and there is no consensus for standardized reporting.

OBJECTIVE

We investigated which diagnostic labels and features pediatric pulmonologists use to describe obstructive airway disease in children and aimed to reach consensus for standardized reporting.

METHODS

We obtained electronic health records from 562 children participating in the Swiss Pediatric Airway Cohort from 2017 to 2018. We reviewed the diagnosis section of the letters written by pediatric pulmonologists to referring physicians and extracted the terms used to describe the diagnosis. We grouped these terms into diagnostic labels (eg, asthma) and features (eg, triggers) using qualitative thematic framework analysis. We also assessed how frequently the different terms were used. Results were fed into a modified Delphi process to reach consensus on standardized reporting.

RESULTS

Pediatric pulmonologists used 123 different terms to describe the diagnosis, which we grouped into 6 diagnostic labels and 17 features. Consensus from the Delphi process resulted in the following recommendations: (i) to use the diagnostic label "asthma" for children older than 5 years and "obstructive bronchitis" or "suspected asthma" for children younger than 5 years; (ii) to accompany the diagnosis with relevant features: diagnostic certainty, triggers, symptom control, risk of exacerbation, atopy, treatment adherence, and symptom perception.

CONCLUSION

We found great heterogeneity in the reporting of obstructive airway disease among pediatric pulmonologists. The proposed standardized reporting will simplify communication among physicians and improve quality of research based on electronic health records.

Association between Environmental Exposures and Asthma among Children in King Williams Town, South Africa

The study aimed to assess the association between environmental exposure and asthma among children between 3 and 12 years old in King Williams Town, South Africa. A quantitative case-control study was conducted at Grey Hospital to assess the association between environmental exposure and asthma among children who reside in King Williams Town. Of the total 566 study participants, 50.5% (286) had asthma while 49.5% did not. Socio-demographic factors associated with asthma in children were being within the age group 9-12 years (OR 1.74, CI 95% 1.09-2.78) and India ethnicity (OR 0.20, CI 95% 0.08-0.48). Factors associated with asthma were weight within 25-35 kg (OR 1.64, CI 95% 1.11-2.42) and BMI within 15-20 (OR 4.80, CI 95% 2.80-8.22). Environmental risk factors associated with asthma were indoor exposure to tobacco smoke from mothers of the participants (OR 5.45, CI 95% 3.08-9.65) and from fathers (OR 4.37; CI 95% 2.77-6.90). Abstaining from eating seafood appeared to be protective from developing asthma (OR 0.01; CI 95% 0.00-0.05). The study found no significant association between outdoor environmental exposures and childhood asthma. The age of participant, weight, BMI, exposure to environmental tobacco smoke (ETS), and eating seafood had significant correlations with childhood asthma. Strengthening the evaluation of children healthcare and encouraging smoking cessation among parents could reduce exposure to environmental asthma triggers among children.

Diagnosis and management of asthma in infants and preschoolers

Asthma is one of the most common chronic disease affecting children, and it often starts in infancy and preschool years. In previous birth cohorts, frequent wheezing in early life was associated with the development of asthma in later childhood and reduced lung function persisting into adulthood. Preschool wheezing is considered an umbrella term for distinctive diseases with different clinical features (phenotypes), each of which may be related to different underlying pathophysiologic mechanisms (endotypes). The classification of phenotypes of early wheezing is needed to identify children at high risk for developing asthma later who might benefit from early intervention. However, diagnosis of asthma in infants and preschoolers is particularly difficult because objective lung function tests cannot be performed and definitive biomarkers are lacking. Moreover, management of early asthma is challenging because of its different phenotypic presentations. Many prediction models and asthma guidelines have been developed to provide useful information for physicians to assess young children with recurrent wheezing and manage them appropriately. Many recent studies have investigated the application of personalized medicine for early asthma by identifying specific phenotypes and biomarkers. Further researches, including genetic and molecular studies, are needed to establish a clear definition of asthma and develop more targeted therapeutic approaches in this age group.

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.

The external validation of the asthma prediction tool in the French ELFE cohort

BACKGROUND

Existing predictive scores for early identification of children at high risk of developing asthma include invasive procedures, and hence have limited utility in a primary care setting. The Leicestershire respiratory cohort (LRC) has developed a noninvasive asthma prediction tool (APT) for children with promising results. We aimed to perform its external validation in the French general population Étude Longitudinale Française depuis l'Enfance (ELFE) cohort.

METHODS

Predictive scores were determined at Age 1 and the primary outcome of asthma was defined as parental reporting of "asthma ever or "wheezing in the past 12 months" at Age 5. Logistic regression was used to calculate the odds ratio (OR) and performance measures, and discriminative performance was reported using the receiver operating curve and area under curve (AUC). Calibration was assessed using Hosmer-Lemeshow goodness-of-fit test and visualized with a calibration plot. Overall performance was determined using Brier scores.

RESULTS

Of the 10,689 children analyzed: 84.9% were at low, 13.1% medium, and 2% at high risk of developing asthma at Age 5. Children in the medium-risk category were three times more likely to develop asthma (OR = 3.3, 95% confidence interval [CI] = 2.97-3.78) whereas 13 times more likely in the high-risk category (OR = 13.8, 95% CI = 10.2-18.8). The tool's AUC was comparable: LRC 0.74 versus ELFE 0.68; as were the Brier scores LRC 0.16 versus ELFE 0.14. The tool's performance was robust to changes in inclusion criteria and outcome definitions.

CONCLUSIONS AND RELEVANCE

Results of the present study and previous validation studies performed in high-risk populations provide a comprehensive measure of the effectiveness of the APT, providing encouragement for its application by general practitioners.

Efficacy of three different budesonide treatments in Chinese preschool children with recurrent wheezing

To explore and compare the clinical control of three atomized inhalation budesonide (BUD) regimens for Chinese preschool children with recurrent wheezing using Test for Respiratory and Asthma Control (TRACK) scores. A total of 474 preschool children with positive Modified Asthma Predictive Index (mAPI) were randomly assigned to a daily group (initially given inhaled BUD 1 mg once a day and assessed every 4 weeks; if symptom were well controlled for 12 weeks, the dose was reduced to 25-50% of the previous dose until afinal dose of 0.25 mg once a day, maintained until 52 weeks), an intermittent high-dose group (1 mg twice daily for 7 days starting early during a predefined respiratory tract illness) and an intermittent medium-dose group (0.5 mg twice daily as soon as they contacted allergens or experienced nasal congestion, a runny nose, cough or other suspicious respiratory symptoms and continuing until symptoms were reduced or risk factors were absent for 3 days) for 52 weeks of treatment. The TRACK questionnaire was administered every 4 weeks. When TRACK scores were ≥ 80, symptoms were considered to be controlled. The average TRACK scores of the three groups after treatment were significantly higher than those before treatment (P < 0.001). There were no significant differences in the average TRACK scores and control rate after treatment at every 4 weeks in the three groups (P > 0.05). Te number of systemic glucocorticoid courses, urgent care visits for wheezing, and wheezing episodes before and after treatment were significantly different within each of the three groups (P < 0.001), but not among the three groups (P > 0.05). In clinical treatment of children, one of the three treatment options can be selected according to the specific situation case of mAPI- positive recurrent wheezing children.

Development of a Symptom-Based Tool for Screening of Children at High Risk of Preschool Asthma

IMPORTANCE

Despite advances in asthma therapeutics, the burden remains highest in preschool children; therefore, it is critical to identify primary care tools that distinguish preschool children at high risk for burdensome disease for further evaluation. Current asthma prediction tools, such as the modified Asthma Predictive Index (mAPI), require invasive tests, limiting their applicability in primary care and low-resource settings.

OBJECTIVE

To develop and evaluate the use of a symptom-based screening tool to detect children at high risk of asthma, persistent wheeze symptoms, and health care burden.

DESIGN, SETTING, AND PARTICIPANTS

The cohort for this diagnostic study included participants from the CHILD Study (n = 2511) from January 1, 2008, to December 31, 2012, the Raine Study from January 1, 1989, to December 31, 2012 (n = 2185), and the Canadian Asthma Primary Prevention Study (CAPPS) from January 1, 1989, to December 31, 1995 (n = 349), with active follow-up to date. Data analysis was performed from November 1, 2019, to May 31, 2022.

EXPOSURES

The CHILDhood Asthma Risk Tool (CHART) identified factors associated with asthma in patients at 3 years of age (timing and number of wheeze or cough episodes, use of asthma medications, and emergency department visits or hospitalizations for asthma or wheeze) to identify children with asthma or persistent symptoms at 5 years of age.

MAIN OUTCOMES AND MEASURES

Within the CHILD Study cohort, CHART was evaluated against specialist clinician diagnosis and the mAPI. External validation was performed in both a general population cohort (Raine Study [Australia]) and a high-risk cohort (CAPPS [Canada]). Predictive accuracy was measured by sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and positive and negative predicted values.

RESULTS

Among 2511 children (mean [SD] age at 3-year clinic visit, 3.08 [0.17] years; 1324 [52.7%] male; 1608 of 2476 [64.9%] White) with sufficient questionnaire data to apply CHART at 3 years of age, 2354 (93.7%) had available outcome data at 5 years of age. CHART applied in the CHILD Study at 3 years of age outperformed physician assessments and the mAPI in predicting persistent wheeze (AUROC, 0.94; 95% CI, 0.90-0.97), asthma diagnosis (AUROC, 0.73; 95% CI, 0.69-0.77), and health care use (emergency department visits or hospitalization for wheeze or asthma) (AUROC, 0.70; 95% CI, 0.61-0.78). CHART had a similar predictive performance for persistent wheeze in the Raine Study (N = 2185) in children at 5 years of age (AUROC, 0.82; 95% CI, 0.79-0.86) and CAPPS (N = 349) at 7 years of age (AUROC, 0.87; 95% CI, 0.80-0.94).

CONCLUSIONS AND RELEVANCE

In this diagnostic study, CHART was able to identify children at high risk of asthma at as early as 3 years of age. CHART could be easily incorporated as a routine screening tool in primary care to identify children who need monitoring, timely symptom control, and introduction of preventive therapies.

Atopic Dermatitis With Coexisting Food Allergy in Early Life Is Associated With Childhood Asthma

PURPOSE

Atopic dermatitis (AD) and food allergy (FA) are associated with respiratory comorbidities, in the concept of 'atopic march.' However, children with AD and a coexisting FA have various disease courses, and the mechanism of atopic march remains unclear. In this study, we investigated whether the phenotype of AD with coexisting FA in early life affected asthma or allergic rhinitis (AR) in school children.

METHODS

A total of 1,579 children from the Panel Study on Korean Children (PSKC) cohort were followed-up in 2013. The participants diagnosed with AD in this cohort were classified by the age of AD onset and persistence as well as FA history. We compared the presence of comorbidities-asthma and rhinitis-among different AD phenotypes.

RESULTS

Asthma and AR with current symptoms within 12 months at age 6-8 years were associated with early-onset persistent AD phenotype, regardless of coexisting FA. AD with FA conferred a higher risk of recent wheezing at 8 years of age than AD without FA (adjusted odds ratio, 8.09; 95% confidence interval, 2.54-25.76). Children with early-onset persistent AD with FA manifested a distinctive trajectory with a higher prevalence of wheezing and AR at age 5-8 years than those without AD.

CONCLUSIONS

AD with FA in early life is strongly associated with asthma and AR in school children, and the early-onset persistent AD with FA had a strong additive effect on the risk of asthma at school age. Classifying AD phenotypes regarding FA in early life will help predict and prevent asthma and AR in school children.

Management of Preschool Wheezing: Guideline from the Emilia-Romagna Asthma (ERA) Study Group

Preschool wheezing should be considered an umbrella term for distinctive diseases with different observable and measurable phenotypes. Despite many efforts, there is a large gap in knowledge regarding management of preschool wheezing. In order to fill this lack of knowledge, the aim of these guidelines was to define management of wheezing disorders in preschool children (aged up to 5 years). A multidisciplinary panel of experts of the Emilia-Romagna Region, Italy, addressed twelve different key questions regarding the management of preschool wheezing. Clinical questions have been formulated by the expert panel using the PICO format (Patients, Intervention, Comparison, Outcomes) and systematic reviews have been conducted on PubMed to answer these specific questions, with the aim of formulating recommendations. The GRADE approach has been used for each selected paper, to assess the quality of the evidence and the degree of recommendations. These guidelines represent, in our opinion, the most complete and up-to-date collection of recommendations on preschool wheezing to guide pediatricians in the management of their patients, standardizing approaches. Undoubtedly, more research is needed to find objective biomarkers and understand underlying mechanisms to assess phenotype and endotype and to personalize targeted treatment.

The impact of a digital wheeze detector on parental disease management of pre-school children suffering from wheezing-a pilot study

BACKGROUND

Viral airway infections are a major reason for doctor's visits at pre-school age, especially when associated with wheezing. While proper treatment requires adequate recognition of airway obstruction, caretakers are often struggling with this judgment, consequently leading to insufficient or late treatment and an unnecessary discomfort of the patient. Digital technologies may serve to support parental decision taking. The aim of the present pilot study is to acquire data on the feasibility of recruitment and observation procedures for a randomized controlled trial on the impact of a digital wheeze detector in a home management setting of pre-school wheezing.

METHODS

This single-armed pilot study enrolled patients with a doctor's diagnosis of wheezing aged 9 to 72 months. Participants were asked to use a digital wheeze detector (WheezeScan, Omron Healthcare, Japan) 2×/day for 30 days and record the child's respiratory symptoms, detection of wheezing, and medication intake via an electronic diary (eDiary) app. Demographic and clinical data were collected at the recruitment visit. The asthma control test and the Parent Asthma Management Self-Efficacy Scale (PAMSES) were assessed both, at recruitment and follow-up.

RESULTS

Twenty families were recruited and completed the monitoring. All but one completed the follow-up after 30 days. The recruitment procedures were feasible, and adherence to daily monitoring reached an average of 81%. The use of the wheeze detector was rated as uncomplicated. Parents detected wheezing without digital support in only 22/708 (3.1%) of the recorded events. By contrast, the wheeze detector indicated an airway obstruction in 140/708 (19.8%) of the recordings.

CONCLUSION

In parallel to feasible recruitment procedures, we observed good usability of the wheeze detection device and high adherence to eDiary recording. The positive outcomes show that the WheezeScan may empower parents by increasing their capacity for wheeze detection. This deserves to be investigated in a larger randomized controlled trial.

Can bronchial challenge test with adenosine or methacholine at preschool age predict school-age asthma?

OBJECTIVE

Bronchial challenge test (BCT) measures current airways-hyperreactivity, however, its predictive role in pre-school children (<6 years) for the diagnosis of asthma at school age is still debatable. We aimed to find whether preschool children with a positive adenosine or methacholine BCT are more prone to asthma at school age.

METHODS

We included children aged 6-13 years with respiratory symptoms that were previously referred to our pulmonary function laboratory for BCT (methacholine or adenosine, depending on the question asked) at age 10 months to 6 years (baseline). BCT was considered positive based on spirometry results or wheezing, desaturation, and tachypnea reactions. The primary outcome measure was asthma diagnosis at school age using the well-validated International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. We used logistic regression analysis to explore whether positive BCT could predict school-age asthma while including age and collected modified asthma predictive index in the model.

RESULTS

One hundred and fifty-one of 189 children (53% males), completed the ISAAC questionnaire (response rate = 80%). Mean ages at BCT and at follow-up were 3.9 ± 1.28 and 9.4 ± 1.85 years, respectively. At baseline, 40 of 67 had a positive adenosine test and 73 of 84 had a positive methacholine BCT. Thirty-nine children were diagnosed with asthma at school age. Logistic regression analysis showed that a positive adenosine test at pre-school age was the best predictor, significantly increasing the odds of asthma at school age by 6.34 (95% CI: 1.23-32.81, p = .028), while methacholine did not show significance (p = .69).

CONCLUSION

Choosing the relevant BCT for the question asked, positive adenosine, but not methacholine test, at pre-school, may predict asthma at school age.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.

The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings

Background

In general practice (GP), the diagnosis of obstructive airway diseases much relies on diagnostic questions, in view of the limited availability of lung function. We systematically assessed the relative importance of such questions for diagnosing asthma and chronic obstructive pulmonary disease (COPD), either without or with information from spirometry.

Methods

We used data obtained in a pulmonary practice to ensure the validity of diagnoses and assessments. Subjects with a diagnosis of COPD (n=260), or asthma (n=433), or other respiratory diseases (n=230), and subjects without respiratory diseases (n=364, controls) were included. The diagnostic questions comprised eight items, covering smoking history, self-attributed allergic rhinitis, dyspnea, cough, phlegm and wheeze. Optionally standard parameters of the flow-volume-curve were included. Decision trees for the diagnosis of COPD and asthma were constructed, moreover a probabilistic diagnostic network based on the results of path analyses describing the relationship between variables.

Results

In the decision trees, age, sex, current smoking, wheezing, dyspnea upon mild exertion, self-attributed allergic rhinitis, phlegm, forced expiratory volume in one second (FEV₁), and expiratory flow rates were relevant, depending on the diagnostic comparison, while cough, dyspnea upon strong exertion and ex-smoker status were not relevant. In contrast, the probabilistic network for the diagnosis of COPD and asthma versus controls incorporated all diagnostic questions, i.e., dyspnea upon mild or strong exertion, current smoking, ex-smoking, wheezing, cough and phlegm but from spirometry only FEV₁. Depending on the individual pattern, the probability for COPD could raise from 25% to 81%, while the diagnostic gain for asthma was lower.

Conclusions

The study developed simple diagnostic algorithms for asthma and COPD that take into account the relative importance of clinical signs and history, as well as spirometric data if available. The diagnostic accuracy was especially high for COPD. These algorithms may be helpful as a starting point in the standardisation of diagnostic strategies in GP practices.

Trial registration

The study is registered under DRKS00013935 at German Clinical Trials Register (DRKS, Date of registration 01/03/2018).

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.

Preschool Wheezing: Trajectories and Long-Term Treatment

Wheezing is very common in infancy affecting one in three children during the first 3 years of life. Several wheeze phenotypes have been identified and most rely on temporal pattern of symptoms. Assessing the risk of asthma development is difficult. Factors predisposing to onset and persistence of wheezing such as breastfeeding, atopy, indoor allergen exposure, environmental tobacco smoke and viral infections are analyzed. Inhaled corticosteroids are recommended as first choice of controller treatment in all preschool children irrespective of phenotype, but they are particularly beneficial in terms of fewer exacerbations in atopic children. Other therapeutic options include the addition of montelukast or the intermittent use of inhaled corticosteroids. Overuse of inhaled steroids must be avoided. Therefore, adherence to treatment and correct administration of the medications need to be checked at every visit.

Asthma predictive index as a useful diagnostic tool in preschool children: a cross-sectional study in Korea

BACKGROUND

It is challenging to diagnose asthma in preschool children. The asthma predictive index (API) has been used to predict asthma and decide whether to initiate treatment in preschool children.

PURPOSE

This study aimed to investigate the association between questionnaire-based current asthma with API, pulmonary function, airway hyperreactivity (AHR), fractional expiratory nitric oxide (FeNO), and atopic sensitization in preschool children.

METHODS

We performed a population-based cross-sectional study in 916 preschool children aged 4-6 years. We defined current asthma as the presence of both physician-diagnosed asthma and at least one wheezing episode within the previous 12 months using a modified International Study of Asthma and Allergies in Childhood questionnaire. Clinical and laboratory parameters were compared between groups according to the presence of current asthma.

RESULTS

The prevalence of current asthma was 3.9% in the study population. Children with current asthma showed a higher rate of positive bronchodilator response and loose and stringent API scores than children without current asthma. The stringent API was associated with current asthma with 72.2% sensitivity and 82.0% specificity. The diagnostic accuracy of the stringent API for current asthma was 0.771. However, no intergroup differences in spirometry results, methacholine provocation test results, FeNO level, or atopic sensitization rate were observed.

CONCLUSION

The questionnaire-based diagnosis of current asthma is associated with API, but not with spirometry, AHR, FeNO, or atopic sensitization in preschool children.

A Novel, Portable MESH Nebulizer-An Alternative to Metered Dose Inhaler: Efficacy and Usability in Preschool Wheezers

Introduction and Objectives: Wheezing episodes are the first causes of doctor's consultation in preschool age. Treatment is usually administered with a metered dose inhaler (MDI) spacer. At variance, many parents and doctors prefer to use a compressor nebulizer, which cannot be easily carried. The study is aimed at testing whether a pocket mesh nebulizer has similar efficacy and acceptability than a standard MDI device. Materials and Methods: The IPAC study was a randomized, controlled, non-inferiority trial (number: 1616/2018, Ospedale Pediatrico Bambino Gesu'-IRCCS). The study had two arms: cases, using MicroAIR U100, and controls, using MDI+spacer device. Both devices were adopted for long-term treatment and for exacerbations. Follow-up was organized with clinical visits and a daily e-diary connected to an application for mobile phone. Results: One hundred patients were enrolled. The frequency of asthmatic symptoms showed a non-inferiority for MicroAIR U100 group vs. MDI. Accordingly, no significant difference was found in the average % of days with cough, wheezing, breathlessness after exercise, days lost at school, and not-programmed visits. Considering only patients with >1 day with symptoms, no significant sdifferences were found in the number of exacerbations nor in the cumulative days with symptoms. The acceptance and usability of both devices have been favorable. However, the MDI+AeroChamber® device showed better acceptability. Conclusions: Our study shows that MicroAIR U-100, a mesh nebulizer, has similar clinical efficacy but lower acceptance and usability than an MDI plus Aerochamber® in delivering therapy in preschool wheezers. Therefore, MicroAIR U-100 might be a valuable second choice, when the delivery of medication with an MDI plus Aerochamber® is not accepted, or wrongly used by the parents.

Special Considerations in Preschool Age

The diagnosis of asthma can be particularly difficult in young children, in whom wheezing is not always synonym with asthma. It is also difficult to predict which preschool children with wheeze will go on to be true asthmatics. In this chapter, we will characterize preschool wheezing and asthma and discuss early risk factors for the development of severe asthma. We will also review risk factors for severe acute wheezing in young children. Finally, we will describe the natural history and prognosis of wheezing and some of the attempts at early identification of children who will develop severe asthma.

Priorities for future research into asthma diagnostic tools: A PAN-EU consensus exercise from the European asthma research innovation partnership (EARIP)

The diagnosis of asthma is currently based on clinical history, physical examination and lung function, and to date, there are no accurate objective tests either to confirm the diagnosis or to discriminate between different types of asthma. This consensus exercise reviews the state of the art in asthma diagnosis to identify opportunities for future investment based on the likelihood of their successful development, potential for widespread adoption and their perceived impact on asthma patients. Using a two-stage e-Delphi process and a summarizing workshop, a group of European asthma experts including health professionals, researchers, people with asthma and industry representatives ranked the potential impact of research investment in each technique or tool for asthma diagnosis and monitoring. After a systematic review of the literature, 21 statements were extracted and were subject of the two-stage Delphi process. Eleven statements were scored 3 or more and were further discussed and ranked in a face-to-face workshop. The three most important diagnostic/predictive tools ranked were as follows: "New biological markers of asthma (eg genomics, proteomics and metabolomics) as a tool for diagnosis and/or monitoring," "Prediction of future asthma in preschool children with reasonable accuracy" and "Tools to measure volatile organic compounds (VOCs) in exhaled breath."

Development of allergic sensitization and its relevance to paediatric asthma

PURPOSE OF REVIEW

The purpose of this review is to summarize the recent evidence on the distinct atopic phenotypes and their relationship with childhood asthma. We start by considering definitions and phenotypic classification of atopy and then review evidence on its association with asthma in children.

RECENT FINDINGS

It is now well recognized that both asthma and atopy are complex entities encompassing various different sub-groups that also differ in the way they interconnect. The lack of gold standards for diagnostic markers of atopy and asthma further adds to the existing complexity over diagnostic accuracy and definitions. Although recent statistical phenotyping studies contributed significantly to our understanding of these heterogeneous disorders, translating these findings into meaningful information and effective therapies requires further work on understanding underpinning biological mechanisms.

SUMMARY

The disaggregation of allergic sensitization may help predict how the allergic disease is likely to progress. One of the important questions is how best to incorporate tests for the assessment of allergic sensitization into diagnostic algorithms for asthma, both in terms of confirming asthma diagnosis, and the assessment of future risk.

Validation of childhood asthma predictive tools: A systematic review

BACKGROUND

There is uncertainty about the clinical usefulness of currently available asthma predictive tools. Validation of predictive tools in different populations and clinical settings is an essential requirement for the assessment of their predictive performance, reproducibility and generalizability. We aimed to critically appraise asthma predictive tools which have been validated in external studies.

METHODS

We searched MEDLINE and EMBASE (1946-2017) for all available childhood asthma prediction models and focused on externally validated predictive tools alongside the studies in which they were originally developed. We excluded non-English and non-original studies. PROSPERO registration number is CRD42016035727.

RESULTS

From 946 screened papers, eight were included in the review. Statistical approaches for creation of prediction tools included chi-square tests, logistic regression models and the least absolute shrinkage and selection operator. Predictive models were developed and validated in general and high-risk populations. Only three prediction tools were externally validated: the Asthma Predictive Index, the PIAMA and the Leicester asthma prediction tool. A variety of predictors has been tested, but no studies examined the same combination. There was heterogeneity in definition of the primary outcome among development and validation studies, and no objective measurements were used for asthma diagnosis. The performance of tools varied at different ages of outcome assessment. We observed a discrepancy between the development and validation studies in the tools' predictive performance in terms of sensitivity and positive predictive values.

CONCLUSIONS

Validated asthma predictive tools, reviewed in this paper, provided poor predictive accuracy with performance variation in sensitivity and positive predictive value.

The Simple 10-Item Predicting Asthma Risk in Children Tool to Predict Childhood Asthma-An External Validation

BACKGROUND

External validation of prediction models is important to assess generalizability to other populations than the one used for model development. The Predicting Asthma Risk in Children (PARC) tool, developed in the Leicestershire Respiratory Cohort (LRC), uses information on preschool respiratory symptoms to predict asthma at school age.

OBJECTIVE

We performed an external validation of PARC using the Avon Longitudinal Study of Parents and Children (ALSPAC).

METHODS

We defined inclusion criteria, prediction score items at baseline and asthma at follow-up in ALSPAC to match those used in LRC using information from parent-reported questionnaires. We assessed performance of PARC by calculating sensitivity, specificity, predictive values, likelihood ratios, area under the curve (AUC), Brier score and Nagelkerke's R2. Sensitivity analyses varied inclusion criteria, scoring items, and outcomes.

RESULTS

The validation population included 2690 children with preschool respiratory symptoms of whom 373 (14%) had asthma at school age. Discriminative performance of PARC was similar in ALSPAC (AUC = 0.77, Brier score 0.13) as in LRC (0.78, 0.22). The score cutoff of 4 showed the highest sum of sensitivity (69%) and specificity (76%) and positive and negative likelihood ratios of 2.87 and 0.41, respectively. Changes to inclusion criteria, scoring items, or outcome definitions barely altered the prediction performance.

CONCLUSIONS

Performing equally well in the validation cohort as in the development cohort, PARC is a valid tool for predicting asthma in population-based cohorts. Its use in clinical practice is ready to be tested.

Individual risk assessment tool for school-age asthma prediction in UK birth cohort

Background

Current published asthma predictive tools have moderate positive likelihood ratios (+LR) but high negative likelihood ratios (−LR) based on their recommended cut‐offs, which limit their clinical usefulness.

Objective

To develop a simple clinically applicable asthma prediction tool within a population‐based birth cohort.

Method

Children from the Manchester Asthma and Allergy Study (MAAS) attended follow‐up at ages 3, 8 and 11 years. Data on preschool wheeze were extracted from primary‐care records. Parents completed validated respiratory questionnaires. Children were skin prick tested (SPT). Asthma at 8/11 years (school‐age) was defined as parentally reported (a) physician‐diagnosed asthma and wheeze in the previous 12 months or (b) ≥3 wheeze attacks in the previous 12 months. An asthma prediction tool (MAAS APT) was developed using logistic regression of characteristics at age 3 years to predict school‐age asthma.

Results

Of 336 children with physician‐confirmed wheeze by age 3 years, 117(35%) had school‐age asthma. Logistic regression selected 5 significant risk factors which formed the basis of the MAAS APT: wheeze after exercise; wheeze causing breathlessness; cough on exertion; current eczema and SPT sensitisation(maximum score 5). A total of 281(84%) children had complete data at age 3 years and were used to test the MAAS APT. Children scoring ≥3 were at high risk of having asthma at school‐age (PPV > 75%; +LR 6.3, −LR 0.6), whereas children who had a score of 0 had very low risk(PPV 9.3%; LR 0.2).

Conclusion

MAAS APT is a simple asthma prediction tool which could easily be applied in clinical and research settings.

Predicting Asthma Using Clinical Indexes

Asthma is no longer considered a single disease, but a common label for a set of heterogeneous conditions with shared clinical symptoms but associated with different cellular and molecular mechanisms. Several wheezing phenotypes coexist at preschool age but not all preschoolers with recurrent wheezing develop asthma at school-age; and since at the present no accurate single screening test using genetic or biochemical markers has been developed to determine which preschooler with recurrent wheezing will have asthma at school age, the asthma diagnosis still needs to be based on clinical predicted models or scores. The purpose of this review is to summarize the existing and most frequently used asthma predicting models, to discuss their advantages/disadvantages, and their accomplishment on all the necessary consecutive steps for any predictive model. Seven most popular asthma predictive models were reviewed (original API, Isle of Wight, PIAMA, modified API, ucAPI, APT Leicestersher, and ademAPI). Among these, the original API has a good positive LR~7.4 (increases the probability of a prediction of asthma by 2-7 times), and it is also simple: it only requires four clinical parameters and a peripheral blood sample for eosinophil count. It is thus an easy model to use in any rural or urban health care system. However, because its negative LR is not good, it cannot be used to rule out the development of asthma.

Life Cycle of Childhood Asthma: Prenatal, Infancy and Preschool, Childhood, and Adolescence

Asthma is a heterogeneous developmental disorder influenced by complex interactions between genetic susceptibility and exposures. Wheezing in infancy and early childhood is highly prevalent, with a substantial minority of children progressing to established asthma by school age, most of whom are atopic. Adolescence is a time of remission of symptoms with persistent lung function deficits. The transition to asthma in adulthood is not well understood.

Paths of causal influence from prenatal inflammation and preterm gestation to childhood asthma symptoms

Introduction: Long-lasting respiratory symptoms have a huge impact on the quality of life in prematurely born children. The aim was to investigate paths of assumed causality leading from foetal inflammatory response syndrome (FIRS) to asthma symptoms in preterms. Methods: Demographic, antenatal, delivery and outcome data were collected from 262 infants with less than 32 completed weeks of gestational age over a 10-year period in a prospective cohort study. The presence of symptoms of asthma beyond the age of 5 years was the primary outcome measure. The causal effect of FIRS on childhood asthma was tested with three different logistic regression models and two structural equation models (SEM). Results: FIRS (OR = 4.7) and subsequent chronic lung disease of prematurity (OR = 7.7) and early childhood wheezing (OR = 9.5) are the most important risk factors for development of asthma symptoms in children born with less than 32 weeks of gestational age. The path analysis showed that FIRS has a large direct (0.59), medium indirect (0.11) and large overall (0.70) effect on CLD; large negative direct effect on ECW (-0.34) and a large positive indirect effect (0.74), mediated by CLD. On the occurrence of asthma symptoms, FIRS has a medium negative direct effect (-0.18) and a medium positive indirect effect (0.26), mediated by CLD and ECW. Conclusion: Prenatal inflammation plays an important role in the development of chronic respiratory disturbances in preterm infants. This influence is mainly related to structural and developmental lung abnormalities initiated in utero as consequences of FIRS, resulting in CLD of prematurity, and overcoming the protective mechanisms of chorioamnionitis.

Abnormal lung function at preschool age asthma in adolescence?

BACKGROUND

Asthma often begins early in childhood. However, the risk for persistence is challenging to evaluate.

OBJECTIVE

This longitudinal study relates lung function assessed with impulse oscillometry (IOS) in preschool children to asthma in adolescence.

METHODS

Lung function was measured with IOS in 255 children with asthma-like symptoms aged 4-7 years. Baseline measurements were followed by exercise challenge and bronchodilation tests. At age 12-16 years, 121 children participated in the follow-up visit, when lung function was assessed with spirometry, followed by a bronchodilation test. Asthma symptoms and medication were recorded by a questionnaire and atopy defined by skin prick tests.

RESULTS

Abnormal baseline values in preschool IOS were significantly associated with low lung function, the need for asthma medication, and asthma symptoms in adolescence. Preschool abnormal R5 at baseline (z-score ≥1.645 SD) showed 9.2 odds ratio (95%CI 2.7;31.7) for abnormal FEV1/FVC, use of asthma medication in adolescence, and 9.9 odds ratio (95%CI 2.9;34.4) for asthma symptoms. Positive exercise challenge and modified asthma-predictive index at preschool age predicted asthma symptoms and the need for asthma medication, but not abnormal lung function at teenage.

CONCLUSION

Abnormal preschool IOS is associated with asthma and poor lung function in adolescence and might be utilised for identification of asthma persistence.

Prevalence of infant sneezing without colds and prediction of childhood allergy diseases in a prospective cohort study

Background

Allergy sensitization may begin during the perinatal period, but predicting allergic diseases in infancy remains difficult. This study attempted to identify early predictors of childhood allergy diseases in a prospective cohort study.

Materials and Methods

In a prospective birth cohort study at southern Taiwan locating in a subtropical region, questionnaire surveys of sneezing or cough without colds at 6 and 18 months of age were recorded, and the correlation with allergy diseases was assessed at 3 and 6 years of age.

Results

A total of 1812 pregnant women and 1848 newborn infants were prenatally enrolled, and 1543, 1344, 1236, and 756 children completed the follow-up at ages 6 months, 18 months, 3 years and 6 years, respectively. The prevalence of infant sneezing without colds at 6 and 18 months of age was 30.3% and 19.2%, respectively. The prevalence of infant cough without colds at 6 and 18 months of age was 10.6% and 5.7%, respectively. Infant sneezing without colds at 18 months of age was significantly correlated with atopic dermatitis, allergic rhinitis and asthma at 6 years of age. Infant cough without colds at 18 months of age significantly predicted asthma but not atopic dermatitis or allergic rhinitis at 6 years of age.

Conclusions

Infant sneezing without colds predicted all allergy diseases at 6 years of age in a subtropical country. This highlights a potential non-invasive clue in a subtropical region for the early prediction, treatment and prevention of childhood allergy diseases in infancy.

Temporal stability of multitrigger and episodic viral wheeze in early childhood

The distinction between episodic viral wheeze (EVW) and multitrigger wheeze (MTW) is used to guide management of preschool wheeze. It has been questioned whether these phenotypes are stable over time. We examined the temporal stability of MTW and EVW in two large population-based cohorts.We classified children from the Avon Longitudinal Study of Parents and Children (n=10 970) and the Leicester Respiratory Cohorts ((LRCs), n=3263) into EVW, MTW and no wheeze at ages 2, 4 and 6 years based on parent-reported symptoms. Using multinomial regression, we estimated relative risk ratios for EVW and MTW at follow-up (no wheeze as reference category) with and without adjusting for wheeze severity.Although large proportions of children with EVW and MTW became asymptomatic, those that continued to wheeze showed a tendency to remain in the same phenotype: among children with MTW at 4 years in the LRCs, the adjusted relative risk ratio was 15.6 (95% CI 8.3-29.2) for MTW (stable phenotype) compared to 7.0 (95% CI 2.6-18.9) for EVW (phenotype switching) at 6 years. The tendency to persist was weaker for EVW and from 2-4 years. Results were similar across cohorts.This suggests that MTW, and to a lesser extent EVW, tend to persist regardless of wheeze severity.

Can we predict fall asthma exacerbations? Validation of the seasonal asthma exacerbation index

BACKGROUND

A Seasonal Asthma Exacerbation Predictive Index (saEPI) was previously reported based on 2 prior National Institute of Allergy and Infectious Diseases Inner City Asthma Consortium trials.

OBJECTIVE

This study sought to validate the saEPI in a separate trial designed to prevent fall exacerbations with omalizumab therapy.

METHODS

The saEPI and its components were analyzed to characterize those who had an asthma exacerbation during the Preventative Omalizumab or Step-Up Therapy for Fall Exacerbations (PROSE) study. We characterized those inner-city children with and without asthma exacerbations in the fall period treated with guidelines-based therapy (GBT) in the absence and presence of omalizumab.

RESULTS

A higher saEPI was associated with an exacerbation in both the GBT alone (P < .001; area under the curve, 0.76) and the GBT + omalizumab group (P < .01; area under the curve, 0.65). In the GBT group, younger age at recruitment, higher total IgE, higher blood eosinophil percentage and number, and higher treatment step were associated with those who had an exacerbation compared with those who did not. In the GBT + omalizumab group, younger age at recruitment, increased eosinophil number, recent exacerbation, and higher treatment step were also associated with those who had an exacerbation. The saEPI was associated with a high negative predictive value in both groups.

CONCLUSIONS

An exacerbation in children treated with GBT with or without omalizumab was associated with a higher saEPI along with higher markers of allergic inflammation, treatment step, and a recent exacerbation. Those that exacerbated on omalizumab had similar features with the exception of some markers of allergic sensitization, indicating a need to develop better markers to predict poor response to omalizumab therapy and alternative treatment strategies for children with these risk factors. The saEPI was able to reliably predict those children unlikely to have an asthma exacerbation in both groups.

Development and preliminary validation of a new screening questionnaire for identifying atopic children

Background

Allergic diseases represent a frequent and increasing condition affecting children. A screening questionnaire allowing an easy identification of children with symptoms of allergic diseases may improve management and clinical outcome. The aim of this study was to develop and validate an easy-to-use screening questionnaire to detect children requiring further allergological evaluations.

Methods

A 10-item questionnaire, evaluating the presence and the history of the most frequent allergic conditions affecting children, including allergic asthma, allergic rhinitis and conjunctivitis, food allergy, and atopic dermatitis, was developed and administered to 214 parents of children from 5 to 10 years of age (163 with allergic disease and 51 healthy, nonallergic children). Validation was performed by Pearson’s correlation between the clinical diagnosis and the responses to the questionnaire. Internal consistency was computed by Cronbach’s alpha correlation coefficient. Sensitivity and specificity of the novel questionnaire were assessed by the receiver operating characteristic (ROC) curve.

Results

Validation analysis of the new children atopy (ChAt) questionnaire showed good internal consistency with a Cronbach’s alpha of 0.757. Responses to the items evaluating the presence of individual allergic conditions significantly correlated with the clinical diagnosis (p<0.001). The ROC curve showed an area of 0.956 and identified a cutoff value >2 of the ChAt questionnaire total score for detection of allergy (sensitivity =0.92 and specificity =0.902).

Conclusion

The novel ChAt questionnaire represents a simple tool able to detect the presence of all major allergic diseases in a pediatric population allowing an early identification of allergic multimorbidity and potentially facilitating clinical management.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease's case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma's case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

Red cell distribution width as a novel predictor of postoperative respiratory adverse events after adenotonsillectomy

BACKGROUND

Respiratory adverse events are commonly observed after adenotonsillectomy in children with sleep-disordered breathing. Preoperative prediction of these events enhances quality of care and resource management in facilities while encouraging precautions against them. Red cell distribution width, a measure of erythrocyte size variability, has recently been linked to adverse outcomes in a variety of disorders. Red cell distribution width has also been found to be associated with severity of obstructive sleep apnea in adults due to hypoxia-mediated inflammation.

AIM

The objective of this study was to evaluate whether elevated red cell distribution width is associated with postoperative respiratory adverse events in children with symptoms of sleep-disordered breathing.

METHODS

A prospective, observational, assessor-blinded study was conducted with consecutive children undergoing elective adenotonsillectomy for treatment of sleep-disordered breathing. Under general anesthesia, adenoidectomy was performed by curettage, and tonsillectomy was carried out by dissection. The primary outcome was the occurrence of an adverse event during emergence or in the postanesthesia care unit (PACU).

RESULTS

Among 287 patients, with mean ± sd age 7.49 ± 3.21, the frequency of respiratory complications during emergence was 62 (22.30%) and in PACU was 56 (20.14%). Mean ± sd red cell distribution width was 14.36 ± 1.06 in patients with complications and higher than that in those without complications 13.53 ± 0.59. Red cell distribution width had an adjusted odds ratio 7.28 (95% CI: 4.30-13.28) and area under the curve value 0.74 (95% CI: 0.67-0.81) to predict postoperative complications. A cutoff value for red cell distribution width was found to be 14.7.

CONCLUSION

Our study showed that preoperative elevated red cell distribution width is associated with an increased risk of respiratory adverse events in children undergoing adenotonsillectomy for sleep-disordered breathing.

Sensitization predicts asthma development among wheezing toddlers in secondary healthcare

INTRODUCTION

Some wheezing toddlers develop asthma later in childhood. Sensitization is known to predict asthma in birth cohorts. However, its predictive value in secondary healthcare is uncertain.

AIM

This study examines the predictive value of sensitization to inhalant allergens among wheezing toddlers in secondary healthcare for the development of asthma at school age (≥6 years).

METHODS

Preschool children (1-3 years) who presented with wheezing in secondary healthcare were screened on asthma at school age with the International Study of Asthma and Allergies in Childhood questionnaire. The positive and negative predictive value (PPV and NPV) of specific IgE to inhalant allergens (cut-off concentration 0.35 kU/L) and several non-invasive variables from a child's history (such as hospitalization, eczema, and parental atopy) were calculated. The additional predictive value of sensitization when combined with non-invasive predictors was examined in multivariate analysis and by ROC curves.

RESULTS

Of 116 included children, 63% developed asthma at school age. Sensitization to inhalant allergens was a strong asthma predictor. The odds ratio (OR), PPV and NPV were 7.4%, 86%, and 55%, respectively. Eczema (OR 3.4) and hospital admission (OR 2.6) were significant non-invasive determinants. Adding sensitization to these non-invasive predictors in multivariate analysis resulted in a significantly better asthma prediction. The area under the ROC curve increased from 0.70 with only non-invasive predictors to 0.79 after adding sensitization.

CONCLUSION

Sensitization to inhalant allergens is a strong predictor of school age asthma in secondary healthcare and has added predictive value when combined with non-invasive determinants. Pediatr Pulmonol. 2017;52:729-736. © 2017 Wiley Periodicals, Inc.

Breastfeeding, maternal asthma and wheezing in the first year of life: a longitudinal birth cohort study

The impact of breastfeeding on respiratory health is uncertain, particularly when the mother has asthma. We examined the association of breastfeeding and wheezing in the first year of life.We studied 2773 infants from the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort. Caregivers reported on infant feeding and wheezing episodes at 3, 6 and 12 months. Breastfeeding was classified as exclusive, partial (supplemented with formula or complementary foods) or none.Overall, 21% of mothers had asthma, 46% breastfed for at least 12 months and 21% of infants experienced wheezing. Among mothers with asthma, breastfeeding was inversely associated with infant wheezing, independent of maternal smoking, education and other risk factors (adjusted rate ratio (aRR) 0.52; 95% CI 0.35-0.77 for ≥12 versus <6 months breastfeeding). Compared with no breastfeeding at 6 months, wheezing was reduced by 62% with exclusive breastfeeding (aRR 0.38; 95% CI 0.20-0.71) and by 37% with partial breastfeeding supplemented with complementary foods (aRR 0.63; 95% CI 0.43-0.93); however, breastfeeding was not significantly protective when supplemented with formula (aRR 0.89; 95% CI 0.61-1.30). Associations were not significant in the absence of maternal asthma (p-value for interaction <0.01).Breastfeeding appears to confer protection against wheezing in a dose-dependent manner among infants born to mothers with asthma.