Prediction of asthma exacerbations in children by innovative exhaled inflammatory markers: results of a longitudinal study

Prediction of Asthma Exacerbations in Children

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

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

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

Understanding the Updates in the Asthma Guidelines

Asthma is a chronic inflammatory lung disease that affects millions of Americans, with variable symptoms of bronchospasm and obstruction among individuals over time. The National Heart, Lung, and Blood Institute (NHLBI) published the 2020 Focused Updates to the Asthma Management Guidelines based on the latest research since the 2007 Expert Panel Report-3 (EPR-3). The following article reviews the 21 new recommendations on the six core topics in asthma: use of intermittent inhaled corticosteroids, long-acting muscarinic antagonist therapy, use of the fractional exhaled nitric oxide test in asthma diagnosis and monitoring, indoor allergen mitigation, immunotherapy, and bronchial thermoplasty. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to rate recommendations as strong or conditional based on the evidence. The recommendations were based on systematic reviews of the literature and focused on patient-centered critical outcomes of asthma exacerbations, asthma control, and asthma-related quality of life. Understanding the recommendations with consideration of individual values through shared decision-making may improve asthma outcomes.

Defining pediatric asthma: phenotypes to endotypes and beyond

Asthma is the most common chronic pediatric lung disease that has traditionally been defined as a syndrome of airway inflammation characterized by clinical symptoms of cough and wheeze. Highlighting the complex and heterogeneous nature of asthma, this review summarizes recent advances in asthma classification that are based on pathobiology, and thereby directly addresses limitations of existent definitions of asthma. By reviewing and contrasting clinical and mechanistic features of adult and childhood asthma, the review summarizes key biomarkers that distinguish childhood asthma subtypes. While atopy and its severity are important features of childhood asthma, there is evidence to support the existence of a childhood asthma endotype distinct from the atopic endotype. Although biomarkers of non-atopic asthma are an area of future research, we summarize a clinical approach that includes existing measures of airway-specific and systemic measures of atopy, co-existing morbidities, and disease severity and control, in the definition of childhood asthma, to empower health care providers to better characterize asthma disease burden in children. Identification of biomarkers of non-atopic asthma and the contribution of genetics and epigenetics to pediatric asthma burden remains a research need, which can potentially allow delivery of precision medicine to pediatric asthma. IMPACT: This review highlights asthma as a complex and heterogeneous disease and discusses recent advances in the understanding of the pathobiology of asthma to demonstrate the need for a more nuanced definitions of asthma. We review current knowledge of asthma phenotypes and endotypes and put forth an approach to endotyping asthma that may be useful for defining asthma for clinical care as well as for future research studies in the realm of personalized medicine for asthma.

Exhaled Breath Condensate-A Non-Invasive Approach for Diagnostic Methods in Asthma

The pathophysiology of asthma has been intensively studied, but its underlying mechanisms such as airway inflammation, control of airway tone, and bronchial reactivity are still not completely explained. There is an urgent need to implement novel, non-invasive diagnostic tools that can help to investigate local airway inflammation and connect the molecular pathways with the broad spectrum of clinical manifestations of asthma. The new biomarkers of different asthma endotypes could be used to confirm diagnosis, predict asthma exacerbations, or evaluate treatment response. In this paper, we briefly describe the characteristics of exhaled breath condensate (EBC) that is considered to be an interesting source of biomarkers of lung disorders. We look at the composition of EBC, some aspects of the collection procedure, the proposed biomarkers for asthma, and its clinical implications. We also indicate the limitations of the method and potential strategies to standardize the procedure of EBC collection and analytical methods.

The Predictive Role of Biomarkers and Genetics in Childhood Asthma Exacerbations

Asthma exacerbations are associated with significant childhood morbidity and mortality. Recurrent asthma attacks contribute to progressive loss of lung function and can sometimes be fatal or near-fatal, even in mild asthma. Exacerbation prevention becomes a primary target in the management of all asthmatic patients. Our work reviews current advances on exacerbation predictive factors, focusing on the role of non-invasive biomarkers and genetics in order to identify subjects at higher risk of asthma attacks. Easy-to-perform tests are necessary in children; therefore, interest has increased on samples like exhaled breath condensate, urine and saliva. The variability of biomarker levels suggests the use of seriate measurements and composite markers. Genetic predisposition to childhood asthma onset has been largely investigated. Recent studies highlighted the influence of single nucleotide polymorphisms even on exacerbation susceptibility, through involvement of both intrinsic mechanisms and gene-environment interaction. The role of molecular and genetic aspects in exacerbation prediction supports an individual-shaped approach, in which follow-up planning and therapy optimization take into account not only the severity degree, but also the risk of recurrent exacerbations. Further efforts should be made to improve and validate the application of biomarkers and genomics in clinical settings.

Lung function testing and inflammation markers for wheezing preschool children: A systematic review for the EAACI Clinical Practice Recommendations on Diagnostics of Preschool Wheeze

BACKGROUND

Preschool wheeze is highly prevalent; 30%-50% of children have wheezed at least once before age six. Wheezing is not a disorder; it is a symptom of obstruction in the airways, and it is essential to identify the correct diagnosis behind this symptom. An increasing number of studies provide evidence for novel diagnostic tools for monitoring and predicting asthma in the pediatric population. Several techniques are available to measure airway obstruction and airway inflammation, including spirometry, impulse oscillometry, whole-body plethysmography, bronchial hyperresponsiveness test, multiple breath washout test, measurements of exhaled NO, and analyses of various other biomarkers.

METHODS

We systematically reviewed all the existing techniques available for measuring lung function and airway inflammation in preschool children to assess their potential and clinical value in the routine diagnostics and monitoring of airway obstruction.

RESULTS

If applicable, measuring FEV1 using spirometry is considered useful. For those unable to perform spirometry, whole-body plethysmography and IOS may be useful. Bronchial reversibility to beta2-agonist and hyperresponsiveness test with running exercise challenge may improve the sensitivity of these tests.

CONCLUSIONS

The difficulty of measuring lung function and the lack of large randomized controlled trials makes it difficult to establish guidelines for monitoring asthma in preschool children.

2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group

The 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group was coordinated and supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. It is designed to improve patient care and support informed decision making about asthma management in the clinical setting. This update addresses six priority topic areas as determined by the state of the science at the time of a needs assessment, and input from multiple stakeholders:A rigorous process was undertaken to develop these evidence-based guidelines. The Agency for Healthcare Research and Quality's (AHRQ) Evidence-Based Practice Centers conducted systematic reviews on these topics, which were used by the Expert Panel Working Group as a basis for developing recommendations and guidance. The Expert Panel used GRADE (Grading of Recommendations, Assessment, Development and Evaluation), an internationally accepted framework, in consultation with an experienced methodology team for determining the certainty of evidence and the direction and strength of recommendations based on the evidence. Practical implementation guidance for each recommendation incorporates findings from NHLBI-led patient, caregiver, and clinician focus groups. To assist clincians in implementing these recommendations into patient care, the new recommendations have been integrated into the existing Expert Panel Report-3 (EPR-3) asthma management step diagram format.

Longitudinal Relationships between Asthma-Specific Quality of Life and Asthma Control in Children; The Influence of Chronic Rhinitis

Managing pediatric asthma includes optimizing both asthma control and asthma-specific quality of life (QoL). However, it is unclear to what extent asthma-specific QoL is related to asthma control or other clinical characteristics over time. The aims of this study were to assess in children longitudinally: (1) the association between asthma control and asthma-specific QoL and (2) the relationship between clinical characteristics and asthma-specific QoL. In a 12-month prospective study, asthma-specific QoL, asthma control, dynamic lung function indices, fractional exhaled nitric oxide, the occurrence of exacerbations, and the use of rescue medication were assessed every 2 months. Associations between the clinical characteristics and asthma-specific QoL were analyzed using linear mixed models. At baseline, the QoL symptom score was worse in children with asthma and concomitant chronic rhinitis compared to asthmatic children without chronic rhinitis. An improvement of asthma control was longitudinally associated with an increase in asthma-specific QoL (p-value < 0.01). An increased use of β₂-agonists, the occurrence of wheezing episodes in the year before the study, the occurrence of an asthma exacerbation in the 2 months prior to a clinical visit, and a deterioration of lung function correlated significantly with a decrease in the Pediatric Asthma Quality of Life Questionnaire (PAQLQ) total score (p-values ≤ 0.01). Chronic rhinitis did not correlate with changes in the PAQLQ score over 1 year. The conclusion was that asthma control and asthma-specific QoL were longitudinally associated, but were not mutually interchangeable. The presence of chronic rhinitis at baseline did influence QoL symptom scores. β₂-agonist use and exacerbations before and during the study were inversely related to the asthma-specific QoL over time.

Comparison of fractional exhaled nitric oxide, spirometry, and Asthma Control Test, in predicting asthma exacerbations: A prospective cohort study

Context

Fractional exhaled nitric oxide (FeNO) is a noninvasive test for airway inflammation in asthma. The usefulness of FeNO in predicting exacerbations is uncertain.

Aims

The study aims to assess and compare the ability of FeNO, spirometry, and asthma control test (ACT) in predicting future exacerbations of asthma and their correlation with each other.

Settings and Design

This prospective, cohort study was conducted at the Department of Respiratory Medicine, Kasturba Medical College, Manipal.

Materials and Methods

Adult asthma patients of age 18-65 years were included. Patients with a smoking history of >10 pack-years and those in whom spirometry was contraindicated were excluded. Patients who consented underwent FeNO and spirometry. The control of asthma was assessed using the ACT questionnaire. We captured the number of exacerbations in the follow-up period of 4 months.

Statistical Analysis Used

Mann-Whitney test was used to compare the utility of FeNO, spirometry, ACT in predicting exacerbations and Spearman's correlation coefficient was used to ascertain the correlation among them.

Results

Of 154 study patients, 28% had exacerbations. We found that there was no significant difference in FeNO in patients with and without exacerbations. The median (interquartile range [IQR]) FEV1% in the patients with and without exacerbations were 68 (55-79) and 75 (65-88), respectively (P = 0.013). The median (IQR) ACT score in patients with exacerbations was 12 (10-16) which was significantly lower than in those without exacerbation in whom it was 16 (14-18) (P = 0.003). There was a negative correlation of ACT with FeNO (Correlation coefficient: -0.167, P = 0.038). The median (IQR) FeNO level (ppb) was lower in patients who were on inhaled corticosteroid (ICS) than in the other group values being 22 (14-38) and 30 (17-58), respectively (P = 0.05).

Conclusions

In our study, FEV1% and ACT score could predict exacerbations of asthma whereas FeNO could not. FeNO level correlated inversely with ACT score. FeNO level decreased with inhaled corticosteroid usage.

Acute Severe Asthma in Adolescent and Adult Patients: Current Perspectives on Assessment and Management

Asthma is a chronic airway inflammatory disease that is associated with variable expiratory flow, variable respiratory symptoms, and exacerbations which sometimes require hospitalization or may be fatal. It is not only patients with severe and poorly controlled asthma that are at risk for an acute severe exacerbation, but this has also been observed in patients with otherwise mild or moderate asthma. This review discusses current aspects on the pathogenesis and pathophysiology of acute severe asthma exacerbations and provides the current perspectives on the management of acute severe asthma attacks in the emergency department and the intensive care unit.

Asthma control in children: Body plethysmography in addition to spirometry

BACKGROUND

There is a lack of agreement among measures of asthma control in children. In Central Europe, body plethysmography is additionally used for asthma monitoring. However, its value is still unclear.

OBJECTIVES

We investigated the possible additional value of body plethysmographic measures (specific resistance, residual volume-total lung capacity ratio [RV/TLC]) compared with spirometric measures forced expiratory volume in 1 second (FEV ₁ ), forced vital capacity (FVC), FEV ₁ /FVC, forced expiratory flow at 25% to 75% of forced vital capacity (FEF _25-75 ), and fraction of exhaled nitric oxide (FeNO) for assessment of asthma control.

METHODS

One hundred and forty-five asthmatic children aged 5 to 17 were included. All children performed measurements of FeNO, spirometry, and body plethymography. Asthma control was assessed by the asthma control test (c-ACT/ACT) and a doctor's assessment of asthma control.

RESULTS

Investigating single lung function parameters, FEV₁ , FEV ₁ /FVC, FEF _25-75 and RV/TLC differed between controlled and partly controlled asthma. However, we found no differences between controlled and uncontrolled asthma with regard to single lung function parameters or for any parameter if investigated in a multivariable approach. This was also true if we combined obtained parameters from spirometry (comparing pathologic vs normal spirometry). Investigating the combination of body plethysmography and doctor's assessment of asthma control a significant association was found ( P = 0.02). Furthermore, combined spirometry and body plethysmography showed a significant association with both doctor's assessed asthma control ( P = 0.009) and the c-ACT/ACT ( P = 0.04). The addition of FeNO did not improve the results.

CONCLUSIONS

The combination of body plethysmography and spirometry shows best agreement with asthma control in children compared with spirometry or body plethysmography alone. Further studies are needed to find out whether additional measurements of body plethysmography improve the outcome of children in asthma monitoring.

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model

INTRODUCTION

Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.

METHODS AND ANALYSIS

We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 individuals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study.

ETHICS AND DISSEMINATION

Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516-0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands-Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website (https://github.com/hollytibble).

An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient's control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians' knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician's therapeutic actions would be based on control level.

Asthma exacerbation prediction: recent insights

PURPOSE OF REVIEW

Asthma attacks are frequent in children with asthma and can lead to significant adverse outcomes including time off school, hospital admission and death. Identifying children at risk of an asthma attack affords the opportunity to prevent attacks and improve outcomes.

RECENT FINDINGS

Clinical features, patient behaviours and characteristics, physiological factors, environmental data and biomarkers are all associated with asthma attacks and can be used in asthma exacerbation prediction models. Recent studies have better characterized children at risk of an attack: history of a severe exacerbation in the previous 12 months, poor adherence and current poor control are important features which should alert healthcare professionals to the need for remedial action. There is increasing interest in the use of biomarkers. A number of novel biomarkers, including patterns of volatile organic compounds in exhaled breath, show promise. Biomarkers are likely to be of greatest utility if measured frequently and combined with other measures. To date, most prediction models are based on epidemiological data and population-based risk. The use of digital technology affords the opportunity to collect large amounts of real-time data, including clinical and physiological measurements and combine these with environmental data to develop personal risk scores. These developments need to be matched by changes in clinical guidelines away from a focus on current asthma control and stepwise escalation in drug therapy towards inclusion of personal risk scores and tailored management strategies including nonpharmacological approaches.

SUMMARY

There have been significant steps towards personalized prediction models of asthma attacks. The utility of such models needs to be tested in the ability not only to predict attacks but also to reduce them.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Exhaled Breath Condensate in Childhood Asthma: A Review and Current Perspective

Exhaled breath condensate (EBC) was introduced more than two decades ago as a novel, non-invasive tool to assess airway inflammation. This review summarizes the latest literature on the various markers in EBC to predict asthma in children. Despite many recommendations and two comprehensive Task Force reports, there is still large heterogeneity in published data. The biggest issue remains a lack of standardization regarding EBC collection, preservation, processing, and analysis. As a result, published studies show mixed or conflicting results, questioning the reproducibility of findings. A joint, multicenter research study is urgently needed to address the necessary methodological standardization.

Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation

Objective

The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient’s parameters such as objective measurements and medical history data.

Results

In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3621-1) contains supplementary material, which is available to authorized users.

Circulating microRNAs and prediction of asthma exacerbation in childhood asthma

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00000575 .

Biomarkers in inflammometry pediatric asthma: utility in daily clinical practice

Asthma is a common disease in both high and lower income countries that starts early and persists often for life. A correct and accurate diagnosis, treatment and follow-up during childhood are essential for a better understanding of adult asthma and avoiding over- or under-treatment. Th2 inflammation in children with asthma symptoms is usually assessed by measuring with serum total IgE, blood eosinophilia and FeNO levels that may help to predict asthma, particularly in those infants and young children in whom lung function tests are difficult to perform. FeNO measurement, compared to intra-individual levels, may be useful also for ascertaining treatment adherence. Nevertheless, an isolated measurement may be insufficient and only the combination of these markers improves the diagnosis, phenotyping and follow-up of an asthmatic child.

Use of the Control of Allergic Rhinitis and Asthma Test (CARATkids) in children and adolescents: Validation in Dutch

BACKGROUND

Allergic rhinitis and asthma are common and closely related diseases. Recently, a Portuguese questionnaire has been developed 'The Control of Allergic Rhinitis and Asthma Test' (CARATkids) that measures disease control of both diseases in children. This study aims to validate the CARATkids in Dutch children and for the first time in adolescents and, in addition, to calculate the minimal clinically important difference (MCID).

METHODS

A prospective observational study was conducted in an outpatient clinic. After translation of the CARATkids from Portuguese to Dutch, patients (6-18 years) with asthma or asthma and allergic rhinitis completed the CARATkids, Asthma Control Test, and visual analog scale questionnaire three times. Baseline characteristics, mean scores, internal consistency, test-retest reliability, cross-sectional and longitudinal validity, discriminative properties, responsiveness, and MCID of the CARATkids were assessed.

RESULTS

A total of 111 patients were included. In total, 86% and 79%, respectively, completed the questionnaires at the second and third visits. All children had asthma, and 85% had concomitant allergic rhinitis. The internal consistency was good with all expected a priori correlations met. CARATkids scores were higher in patients with uncontrolled asthma and patients with moderate-severe rhinitis compared to better controlled subjects. Patients with a variable asthma control had significantly higher scores during periods of uncontrolled asthma. Also the Guyatt's responsiveness index was good. The MCID was 2.8.

CONCLUSIONS

The CARATkids questionnaire is a reliable and valid tool to assess allergic rhinitis and asthma control among Dutch children. The tool can be used in adolescents.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

Biomarkers in Exhaled Breath Condensate Are Not Predictive for Pulmonary Exacerbations in Children with Cystic Fibrosis: Results of a One-Year Observational Study

Background

Cystic Fibrosis (CF) is characterized by chronically inflamed airways, and inflammation even increases during pulmonary exacerbations. These adverse events have an important influence on the well-being, quality of life, and lung function of patients with CF. Prediction of exacerbations by inflammatory markers in exhaled breath condensate (EBC) combined with early treatment may prevent these pulmonary exacerbations and may improve the prognosis.

Aim

To investigate the diagnostic accuracy of a set of inflammatory markers in EBC to predict pulmonary exacerbations in children with CF.

Methods

In this one-year prospective observational study, 49 children with CF were included. During study visits with an interval of 2 months, a symptom questionnaire was completed, EBC was collected, and lung function measurements were performed. The acidity of EBC was measured directly after collection. Inflammatory markers interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), and macrophage migration inhibitory factor (MIF) were measured using high sensitivity bead based flow immunoassays. Pulmonary exacerbations were recorded during the study and were defined in two ways. The predictive power of inflammatory markers and the other covariates was assessed using conditionally specified models and a receiver operating characteristic curve (SAS version 9.2). In addition, k-nearest neighbors (KNN) algorithm was applied (SAS version 9.2).

Results

Sixty-five percent of the children had one or more exacerbations during the study. The conditionally specified models showed an overall correct prediction rate of 55%. The area under the curve (AUC) was equal to 0.62. The results obtained with the KNN algorithm were very similar.

Conclusion

Although there is some evidence indicating that the predictors outperform random guessing, the general diagnostic accuracy of EBC acidity and the EBC inflammatory markers IL-6, IL-8, TNF-α and MIF is low. At present it is not possible to predict pulmonary exacerbations in children with CF with the chosen biomarkers and the method of EBC analysis. The biochemical measurements of EBC markers should be improved and other techniques should be considered.