Next Article in Journal
Assessment of Some Risk Factors and Biological Predictors in the Post COVID-19 Syndrome in Asthmatic Patients
Previous Article in Journal
Experience in the Application of Augmented Reality Technology in the Surgical Treatment of Patients Suffering Primary and Recurrent Pelvic Tumors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 14
Issue 1
10.3390/jpm14010020
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Prediction of Asthma Exacerbations in Children

by
Evangelia Sarikloglou
1,
Sotirios Fouzas
2 and
Emmanouil Paraskakis
3,*
1
Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece
2
Department of Pediatrics, University of Patras Medical School, 26504 Patras, Greece
3
Paediatric Respiratory Unit, Paediatric Department, University of Crete, 71500 Heraklion, Greece
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2024, 14(1), 20; https://doi.org/10.3390/jpm14010020
Submission received: 26 November 2023 / Revised: 17 December 2023 / Accepted: 20 December 2023 / Published: 22 December 2023
(This article belongs to the Special Issue Novel Diagnostic and Therapeutical Opportunities for Asthma and Allergy)
Browse Figure
Versions Notes

Abstract

:
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.

1. Introduction

Asthma is the most common chronic disorder of childhood and represents a significant health burden. The disease is characterized by chronic airway inflammation and acute episodes (exacerbations) of reversible airway obstruction with respiratory symptoms, such as wheezing, dyspnea, chest tightness, and coughing. Currently, asthma monitoring relies solely on the regular assessment of respiratory symptoms and lung function. However, the lack of a direct measurement of inflammation may result in the inappropriate recognition of children at risk for a future asthma attack [1,2,3,4].
Asthma exacerbations are not rare in asthmatic children, even among those with apparently reasonable 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 [5,6,7,8,9,10,11,12]. 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 [13]. Current asthma treatment strategies have generally succeeded in controlling daily symptoms and provide to asthmatic children a good quality of life [8]. Nevertheless, it is estimated that in the United States, half of the children with asthma will experience at least one exacerbation per year, while in Europe, more than one in three will have an unplanned hospital visit due to an asthma attack [14].
Of note, children prone to asthma exacerbation are a heterogeneous group [15]; 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-type of inflammation, and environmental exposures (pollutants, stress, viral and bacterial pathogens) determine the risk of a future exacerbation and should be carefully considered [7,16,17,18,19].
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.

2. Level of Asthma Control

Poor asthma control is a standard risk indicator for asthma exacerbation [12,20,21,22,23]. For example, children with partially controlled asthma have a 2-fold increase in the exacerbation rates, as compared to those with controlled disease [23]. However, symptom-based tools used to assess asthma control, such as the Asthma Control Test (ACT) and the Asthma Control Questionnaire (ACQ), cannot offer precise predictions on the time of exacerbation. In the study by Schatz et al., a lower ACT score was associated with an increased risk of emergency department visits and oral corticosteroid and beta-agonist use in the following 12 months [22,24]. Conversely, in another study by Meltzer et al., each 1-point increase in the ACQ score was associated with a 50% increase in exacerbation risk for the following two weeks [25]. On the other hand, other studies have demonstrated that asthma exacerbations can also occur in the context of reasonable asthma control [10,11,26]. These reports have questioned the predictive utility of ACT and ACQ scores, demonstrating that they are not superior to the frequency of rescue inhaler use alone [10,11,26]. In a 4-year study by Wu et al., 14% of the participants never reported troublesome asthma symptoms, although they had presented at least one severe exacerbation [12]. In another study of 612 asthmatic children, 54% of those who reported good asthma control had abnormal spirometry and or raised fractional exhaled nitric oxide (FeNO) [27]. Clearly, the factors that are associated with poor asthma control are not the same as those associated with asthma exacerbations. Moreover, the loss of disease control may be hard to identify by the patients and their parents [28,29]. Socioeconomic status also plays a crucial role in the way patients perceive and report their symptoms; in a cross-sectional study (N = 307) by Ganti et al., there was a significant positive correlation (p < 0.001) between the ACT score and the education and socioeconomic status of the family [30]. Nevertheless, it is generally accepted that ACT and ACQ scores can be used as part of the routine evaluation of asthmatic children and for assessing the risk of future exacerbations [31]. The combination of asthma symptom scores and medication scores could improve our ability to identify children at risk of an asthma attack in the future.
Several studies have shown that an asthma attack is by itself a strong predictor of an exacerbation of the disease in the future. The use of oral corticosteroids and emergency department visits or hospitalizations for symptoms related to asthma in the previous 12 months are strong and independent predictors of a future attack [9,12,18,23,32,33,34,35,36,37,38]. A study by Engelkes et al. demonstrated that patients with an asthma exacerbation have a 25% possibility of repeating the episode within the following year [32]. In a recent systematic review, Lowden et al. confirmed that a past exacerbation is the best predictor of a future exacerbation, regardless of the severity of the disease and the level of control [38]. The number of previous exacerbations is also important [17,37]; in the metropolitan area of St. Louis, the probability of hospital readmission for an asthma exacerbation over ten years increased by 30% after the first admission, 46% after a second admission, and 59% after a third admission [17]. On the other hand, Lowden et al. concluded that the severity of an asthma exacerbation does not necessarily relate to the severity of the previous exacerbations [38]. In any case, the asthma management plan for a given patient should be carefully reviewed when an exacerbation occurs. Various factors, such as female sex, higher FeNO levels, and escalating treatment, are associated with a higher exacerbation risk and, thus, may highlight the need for a more frequent follow-up [39].

3. Lung Function Testing

Spirometry is widely used for assessing the lung function of asthmatic patients. However, the test is notoriously unable to detect abnormalities at the level of small airways, as in the case of asthmatic children, where small airways are affected early in the course of the disease [40]. Thus, the existing evidence on the usefulness of spirometry in detecting children at increased risk for asthma exacerbations is conflicting [11,33,41,42]. In a retrospective study of 13,842 children (100,292 observations) seen annually over 15 years, a strong association was noted between FEV1% predicted and risk of asthma exacerbation in the subsequent year [41]. Repeated measurements of FEV1, even if they are within the normal range, could add to the clinical risk assessment; a 10% reduction in FEV1% predicted within three months is associated with 28% increased odds for an asthma exacerbation [43]. Reversibility to bronchodilators may reveal specific obstruction phenotypes, also related to the risk of an asthma attack [44]. In other studies, mid-expiratory flows presented a good predictive value for a future exacerbation, even when the baseline FEV1 was normal [45,46].
Specific peak expiratory flow (PEF) patterns may also be related to loss of asthma control and risk of exacerbation [47]. Wide diurnal PEF variations signify loss of disease control, while a steep PEF decline without changes in variability is observed during exacerbations. Studies using complex statistical methods have suggested that PEF variability could help predict future asthma attacks in adults [48], but similar data in children are lacking. In a study by Kim et al., PEF was lower in asthmatic children in autumn than in winter, suggesting that seasonal variations should also be considered [49].

4. Adherence to Treatment

Poor adherence to treatment, including improper inhaled medication and/or breathing chamber use techniques, is associated with an increased risk of exacerbations, hospital admissions, and asthma-related deaths [50,51,52]. Children with asthma who have regular follow-up visits present a reduced risk of asthma attacks, while inadequate follow-up adherence relates to increased morbidity and more frequent exacerbations [53]. Additionally, incorporating patient preferences into treatment decisions (e.g., type of inhaler device, medication dosage) seems to result in longer exacerbation-free periods, especially for children with poor asthma control [54,55]. Interestingly, a recent study showed that during the COVID-19 pandemic, asthma exacerbations were reduced due to decreased exposure to environmental triggers and increased patient adherence [56]. It should be mentioned, however, that other studies failed to confirm a significant impact of adherence to treatment on impending asthma exacerbation in children [57,58].

5. Other Patient-Related Factors

A study conducted in the United States showed that race and ethnicity play an important role in adverse asthma outcomes since non-Hispanic black children had a greater risk for emergency department visits and deaths due to asthma compared to their non-Hispanic white counterparts [59,60]. Others have shown that Asian ethnicity is associated with a lower likelihood of future asthma attacks [61], while African American race and low socioeconomic status may increase the risk of asthma exacerbations [62,63]. However, further studies are required to explore the exact role of the genetic background in such populations [64]. Various socioeconomic factors determining the ease of accessing healthcare resources may contribute equally to an increased risk of asthma exacerbations [8,65,66]. Moreover, all these factors may vary and, thus, play different roles according to the child’s age [13].
Overweight or obesity reduces the response to inhaled corticosteroids and predisposes one to asthma attacks. The role of chronic stress and anxiety is more complex and poorly understood, although an increased Th2 cytokine response has been reported [67,68]. In addition, chronic stress may lead asthmatic patients to poor adherence [69,70,71,72,73,74]. Interestingly, maternal depression is also associated with an increased risk of asthma exacerbation in children [75].

6. Salbutamol Overuse

A higher number of days of salbutamol use (>two days in two weeks) and a higher number of salbutamol doses per day are strong and independent predictors of severe asthma exacerbation in the future [76]. Short-acting beta-agonist (SABA) overuse has also been associated with an increased risk of death due to asthma [77]. Patients who have learned to “control” their disease only by SABAs need special attention because SABA overuse seems to increase bronchial hyperreactivity and induce pro-inflammatory pathways [76,78,79]. In this regard, the monitoring of SABA use could offer better disease control and prevent future exacerbations. In a study from Sweden, one-third of asthmatic patients (12–45 years old) used three or more SABA canisters per year, while the risk of asthma exacerbation was directly related to the amount of SABA used [80]. In another study, Frey et al. have suggested that the frequent administration of SABAs (>4 times per day) may increase the risk of asthma attacks due to the loss of beta-agonist effectiveness. The prescription of more than three SABA canisters per year should alert healthcare professionals to the risk of an imminent asthma exacerbation [48,77].
Long-acting beta-agonists (LABAs) are more effective in stabilizing airway tone in the long term [48]. However, LABA monotherapy may also be associated with severe asthma exacerbations and asthma-related death, especially in younger children [81]. Nevertheless, the concurrent administration of LABAs with inhaled corticosteroids (ICSs), usually as a fixed LABA-ICS combination, has been associated with the reduced rate and severity of exacerbations and better clinical outcomes than using ICSs alone [81].

7. Biomarkers

Airway inflammation biomarkers are constantly evaluated concerning their ability to identify Th2 inflammation. The essential role of the Th2 type of inflammation in asthma exacerbation emerges from clinical trials of “biological” agents, such as IgE, IL-4, IL-5, and IL-13 inhibitors [82]. The administration of these novel drugs has consistently been associated with a significant reduction in asthma exacerbations, thus highlighting the pivotal role of Th2 inflammation in the susceptibility to asthma attacks [82]. Novel technologies that can be applied to multiple biological samples, such as metabolomics, proteomics, transcriptomics, and genomics, hold particular promise for identifying patients with poor disease control and are at risk for asthma exacerbations [82,83]. Among these techniques, “breathomics” is of particular interest due to its non-invasive nature that offers the possibility of frequent and repeated sampling.
Evidence on the utility of FeNO as a predictor of asthma exacerbations in children and adolescents remains conflicting. FeNO, alone or in combination with other biomarkers, is an essential tool for monitoring adherence and response to treatment [84,85,86]. In a recent observational study, Lo et al. correlated FeNO measurements with future asthma exacerbations and showed that higher FeNO levels could predict future asthma attacks [61]. A FeNO of ≥80 ppb has been proven useful in identifying poorly controlled asthma in children [87]. In a small study of adults, those who experienced an asthma exacerbation had significantly higher FeNO levels within two weeks before the event [88]. Moreover, the investigators showed that FeNO was the only significant and independent predictor of exacerbations compared to spirometric indices, quality of life scores, and medication usage [88]. On the other hand, similar studies in asthmatic children found that a single FeNO measurement is not useful in assessing the risk of an upcoming exacerbation [89,90]. In the Reducing Asthma Attacks in Children using Exhaled Nitric Oxide trial, a combined approach based on symptom-guided asthma treatment and FeNO levels did not reduce the asthma attacks [91,92]. Even FeNO measurements two weeks before an exacerbation in children with severe asthma may have poor positive predictive value [93]. In another cohort study from Ecuador, 283 children with asthma were followed for six months or until their next asthma attack; a previous severe exacerbation was the most reliable predictor of a future asthma attack, while the predictive ability of FeNO measurements was limited [94]. FeNO levels in children aged 0–4 years correlate well with the Asthma Predictive Index but cannot reliably predict a future asthma diagnosis or disease exacerbations [95]. A recent study confirmed the low predictive value of FeNO measurements even when combined with clinical characteristics [96], while Fielding et al. demonstrated that a significant increase in FeNO levels between subsequent visits was associated with poor asthma outcomes but not a higher exacerbation risk [43]. On the other hand, two recent meta-analyses concluded that when FeNO is used to guide asthma management strategies, the frequency of asthma exacerbations can be reduced [97,98]. The significant intrasubject variability in FeNO values in children may have accounted for the above controversial findings [99].
FeNO partitioning, i.e., the measurement of FeNO at multiple exhalation flow rates, offers valuable information on the NO concentration in the most distal airways, the so-called alveolar NO (CalvNO) [100]. A recent study from our group explored the role of CalvNO as a predictor for asthma exacerbations in 68 asthmatic children [101]. We found that CalvNO levels > 7 ppb could predict asthma exacerbations in the subsequent four months with 90.9% specificity, while a CalvNO of <4 ppb could exclude a future exacerbation with 71.4% sensitivity. Moreover, an increase in CalvNO by 0.5 ppb between subsequent visits could predict future exacerbations with 92% sensitivity and 92% specificity, while the performance of ACT scores and spirometric indices (including reversibility testing) was significantly lower [101]. Therefore, distal inflammation plays a pivotal role in asthma exacerbations in children and should be further considered in future studies [101].
Sputum eosinophils is a cost-effective biomarker for assessing disease control in asthmatic patients [102]. However, sputum collection may be challenging in young and uncooperative children, while sputum eosinophils do not seem reliable in predicting future asthma attacks [90,93,103]. Novel saliva biomarkers, such as eotaxin, IL-5, and IL-8, are easier to collect and have shown a strong correlation with the level of asthma control [104], but their role is still to be determined.
Measurements of volatile organic compounds (VOCs) in the exhaled breath seems also promising, as specific VOC patterns are closely related to disease exacerbations in asthmatic children [105,106,107].
Generally, blood eosinophil counts (EOSs) of >300 cells/µL have been related to troublesome asthma in adults. In the Severe Asthma Research Program, EOSs > 400 cells/mL were associated with an increased risk of exacerbation [108,109]. However, in asthmatic children, the evidence is conflicting [110,111,112]. EOSs, combined with FeNO, have been used as markers of the Th2 inflammation pathway to predict the response to treatment in asthmatic children, with reasonable results [113,114,115].
Serum IL-6 was also associated with the risk of asthma exacerbation in children, but further studies are required [116]. Plasma eosinophilic cationic protein (ECP) concentration is a useful marker of Th2 inflammation and may help identify children at risk for recurrent asthma attacks who could benefit from corticosteroid treatment [117]. Other biomarkers of atopy, such as skin prick or specific IgE testing for sensitization to aeroallergens and total serum IgE, have been utilized to assess the risk of seasonal exacerbations [118]. Mucosa-associated lymphoid tissue translocation protein 1 (MALT1) is another novel biomarker [119].
Urinary leukotriene E4 (ULTE4) levels reflect systemic cysteinyl leukotriene production [120,121], and, when elevated, may predict asthma exacerbations in children exposed to tobacco smoke [122]. Also, urinary phthalate metabolites and urinary organic acids seem to be significantly associated with imminent asthma attacks [123,124]. More inflammatory mediators, including cytokines, chemokines, IL-5, and acidity levels, can be measured in the exhaled breath condensate and serve as metabolomic biomarkers of asthma exacerbation in the future [36,96].
Studies based on genome-wide association have revealed the existence of susceptibility variants that are specifically related to exacerbations and differ from those generally related to asthma. A cadherin-related family member gene variant (CDHR-3) has been linked to recurrent severe asthma exacerbations in preschool children of European descent [125], while the 17q21 locus and the ADRB2 gene (especially its Glu27 variant) are consistently associated with asthma attacks in asthmatic children and adults [5,126]. A recent meta-analysis demonstrated a significant association between a single-nucleotide polymorphism in FLJ22447 (rs2253681) and severe asthma exacerbations [127]. Furthermore, three microRNA models (miR-146b, miR-206, and miR-720) that could predict exacerbations in asthmatic patients receiving inhaled corticosteroids have been detected [128]. Reduced responsiveness to SABAs, especially in those using long-acting beta-agonists (LABAs), has been associated with polymorphisms in the beta-2 adrenoceptor gene [129]. Finally, nasal airway transcriptomic analysis demonstrated that higher baseline Th2/Th1-interferon ratios can predict asthma attacks [130]. Future studies should explore the full spectrum of such genetic variabilities, with larger sample sizes, better representation of racial/ethnic diversity, and a more precise definition of asthma exacerbation.

8. Environmental Exposures

Environmental exposures, including aeroallergens, viral and bacterial pathogens, environmental pollutants, and stress, largely drive asthma exacerbations [131]. Atopic individuals, in particular, have the most significant risk when they are exposed to the aeroallergen to which they are sensitized [132]. The association between viral respiratory tract infection and asthma exacerbations is well established in childhood [133]. For example, in a study by Murray et al., this association tremendously increased the likelihood of an asthma exacerbation [134,135,136]. Such patients remain vulnerable to asthma attacks during respiratory infections even if the level of disease control is good [47]. Human rhinovirus (HRV) infection seems to be the most significant trigger of asthma exacerbations in children, and as such, it might be used as a “biomarker” for imminent asthma attacks. A study from Germany before the COVID-19 pandemic demonstrated that 41% of the children who experienced an exacerbation had a positive test result for HRV, while 14% were positive for the respiratory syncytial virus (RSV) [137,138]. Interestingly, HRV was particularly prevalent among asthmatic and atopic patients (56% and 66%, respectively) [137,138]. Respiratory microbiota and specific bacteria–host interactions may also determine the risk of asthma exacerbations. Several Moraxella and Haemophilus members may enrich viral respiratory illnesses during the fall season, leading to subsequent exacerbations [139]. These episodes seem to have a regular peak after returning to school from their summer holidays, i.e., in September for the Northern hemisphere and in January for the Southern [135,140,141,142].
Asthma exacerbations also present a second peak around the end of the hay fever season [143]. A 10-year-long study from Italy demonstrated that asthma exacerbations had seasonal peaks during autumn and spring. Pollens; wind speed; rainfall; and SO2, NO, O3, and NO2 levels were strongly associated with asthma exacerbations in those children [144,145,146]. Meteorological factors are important modulators in asthmatic children and adults [147,148]. During the COVID-19 pandemic, the most important factors that reduced asthma attacks were the decreased exposure to environmental triggers (e.g., the time spent at home) and the increased adherence to treatment [56,149]. Thus, identifying environmental factors associated with asthma exacerbations could lead to prompt pharmacological interventions [143,150] and offer the possibility of reducing exposure to specific triggers [151,152].
Air pollution is another crucial risk factor for children living in urban areas [153,154,155,156,157,158,159]. In the study by Zhang et al., who examined 17,227 pediatric asthma admissions during the 2015–2016 period in Chinese urban areas, a strong relationship emerged between hospital visits and nitrogen dioxide (NO2) ozone (O3), and particulate matter of at least 2.5 mm (PM2.5) levels [160]. A similar study, also from China, confirmed that PM2.5, sulfur dioxide (SO2), and NO2 atmospheric concentrations were significantly associated with asthma attacks [161]. The effects of SO2 were more potent in the cold season and those of NO2 during the warm months, while preschool children were more susceptible to increased SO2 levels [161]. In the same line, a relevant meta-analysis concluded that NO2, SO2, and PM2.5 levels predispose to future asthma attacks in both children and adults [162]. Interestingly, even short-term exposure to high concentrations of air pollutants may significantly increase the risk of asthma exacerbations [163]. Short-term exposure is associated with reduced interferon beta (IFN-b) expression in the airway epithelium, facilitating viral replication [164,165,166]. Tobacco smoke exposure, either first- or second-hand, has similar effects and may also trigger severe exacerbations [167]. In a prospective study of asthmatic Thai children, daily PM2.5 exposure to levels above 12 mcg/m3 was associated with asthma exacerbation within the next three days [168]. Accumulating evidence suggests that long-term exposure to air pollution, especially traffic-related air pollution (TRAP), can contribute to new-onset asthma in children and adults [169,170,171]. Four main mechanisms have been described: oxidative stress damage, airway remodeling, the activation of inflammatory pathways and immunological responses, and the enhancement of respiratory sensitization to aeroallergens [171].
Improving the air quality to prevent future asthma exacerbations and new cases of asthma in children would require solid governmental efforts. Until then, the continuous monitoring and online availability of air pollutant concentration and relevant meteorological data should be considered [172,173,174]. Informatics and wearable sensor technologies may further assist in collecting biometric data to understand pediatric asthma triggers and design appropriate and personalized monitoring and prevention strategies [163,175].

9. Risk Scores

Admittedly, a single marker for assessing the risk of asthma exacerbations is challenging to identify. Thus, current research focuses on combining risk factors into composite scores using advanced analytic methods, such as machine learning, to improve the risk stratification and recognition of the most vulnerable children [176,177]. These approaches are based on the systematic monitoring of known clinical and lung function exacerbation predictors, also offering the possibility of including widely available biomarkers (e.g., EOS, FeNO) or even air pollutant concentrations and relevant meteorological data [177].
A multidisciplinary, multi-factorial, and personalized approach is mandatory when managing pediatric asthma [13,178,179]. Current guidelines focus on the stepwise escalation/de-escalation of drug therapy to achieve improved control and reduce the risk of exacerbations. Therefore, the prompt identification of symptoms and the longitudinal monitoring of physiologic parameters (including lung function) are important. Huffaker et al. applied the passive nocturnal monitoring of heart rate, respiratory rate, and body movements by using a contactless bed sensor in a small cohort of asthmatic children (n = 16). Asthma symptoms and ACT scores were reported every two weeks. The investigators reported that nocturnal physiologic changes correlated well with asthma symptoms, suggesting that nocturnal physiologic monitoring could represent an objective tool for assessing disease control and predicting asthma exacerbations [180]. In a big cohort of 28,196 patients, Hatoun et al. recognized ten potential predictors that were subsequently included in an asthma exacerbation risk (AER) score [181]. The AER score is calculated monthly by healthcare professionals to identify children at risk for asthma exacerbation within the following year [181]. Another score, the test for respiratory and asthma control in kids (TRACK), has been designed to apply in preschoolers with acute wheezing episodes within the first five days of the event [182]. It has been reported that TRACK predicts a subsequent severe exacerbation (emergency department visit and/or need of systemic corticosteroids) within the next three months; for each 10-unit decrease in TRACK, the probability of a future exacerbation increases by 38% [182].
An advanced monitoring tool, the myAirCoach system, which includes an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a personal FeNO device, and a dedicated smartphone app, has been shown to improve asthma control and the quality of life of asthmatic patients [183,184]. The Biomedical Real-Time Health Evaluation (BREATH) platform is a similar tool that focuses on pediatric patients [185]. Although much work remains to be carried out about measurement collection and standardization, analyzing these data series using machine learning algorithms holds promise for developing reliable personalized predictive tools [186,187,188,189].

10. Conclusions

Pediatric asthma is a multifactorial, complex, and dynamic disease, and as such, it cannot be monitored using classical clinical tools or simple biomarkers. The ideal method for predicting the loss of disease control and imminent asthma exacerbations should be based on the combination of patient data (e.g., demographics, symptom-based scores, etc.), lung function measurements, various Th2 inflammation biomarkers (e.g., EOS, FeNO, “omics”, etc.), and environmental exposures (e.g., aeroallergen and air pollutant concentrations, meteorological data, etc.). Machine or deep learning techniques should be used to analyze these big-data series further and ensure reliable and personalized predictions in the context of different disease subtypes. The above approach is summarized in Figure 1. Standardizing the criteria to diagnose asthma exacerbation is equally critical; both loose and stringent definitions of asthma attacks may lead to false associations, thus impeding the generalizability of the prediction models. Finally, an important future aim should be establishing an international pediatric exacerbation network that would significantly facilitate data collection and comparison, as well as assessing innovative technologies and applying relevant predictive strategies in clinical practice.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Simoneau, T.; Cloutier, M.M. Controversies in Pediatric Asthma. Pediatr. Ann. 2019, 48, e128–e134. [Google Scholar] [CrossRef] [PubMed]
  2. Masoli, M.; Fabian, D.; Holt, S.; Beasley, R.; Global Initiative for Asthma, P. The global burden of asthma: Executive summary of the GINA Dissemination Committee report. Allergy 2004, 59, 469–478. [Google Scholar] [CrossRef] [PubMed]
  3. Plaza, V.; Serrano, J.; Picado, C.; Sanchis, J. Frequency and clinical characteristics of rapid-onset fatal and near-fatal asthma. Eur. Respir. J. 2002, 19, 846–852. [Google Scholar] [CrossRef] [PubMed]
  4. Zhang, J.; Yu, C.; Holgate, S.T.; Reiss, T.F. Variability and lack of predictive ability of asthma end-points in clinical trials. Eur. Respir. J. 2002, 20, 1102–1109. [Google Scholar] [CrossRef] [PubMed]
  5. Herrera-Luis, E.; Hernandez-Pacheco, N.; Vijverberg, S.J.; Flores, C.; Pino-Yanes, M. Role of genomics in asthma exacerbations. Curr. Opin. Pulm. Med. 2019, 25, 101–112. [Google Scholar] [CrossRef]
  6. Pardue Jones, B.; Fleming, G.M.; Otillio, J.K.; Asokan, I.; Arnold, D.H. Pediatric acute asthma exacerbations: Evaluation and management from emergency department to intensive care unit. J. Asthma 2016, 53, 607–617. [Google Scholar] [CrossRef]
  7. Denlinger, L.C.; Heymann, P.; Lutter, R.; Gern, J.E. Exacerbation-Prone Asthma. J. Allergy Clin. Immunol. Pract. 2020, 8, 474–482. [Google Scholar] [CrossRef]
  8. Moral, L.; Asensi Monzo, M.; Julia Benito, J.C.; Ortega Casanueva, C.; Paniagua Calzon, N.M.; Perez Garcia, M.I.; Rodriguez Fernandez-Oliva, C.R.; Sanz Ortega, J.; Valdesoiro Navarrete, L.; Valverde-Molina, J. Pediatric asthma: The REGAP consensus. An. Pediatr. (Engl. Ed.) 2021, 95, 125.e1–125.e11. [Google Scholar] [CrossRef]
  9. Lieu, T.A.; Quesenberry, C.P.; Sorel, M.E.; Mendoza, G.R.; Leong, A.B. Computer-based models to identify high-risk children with asthma. Am. J. Respir. Crit. Care Med. 1998, 157, 1173–1180. [Google Scholar] [CrossRef]
  10. Reddel, H.K.; Busse, W.W.; Pedersen, S.; Tan, W.C.; Chen, Y.Z.; Jorup, C.; Lythgoe, D.; O’Byrne, P.M. Should recommendations about starting inhaled corticosteroid treatment for mild asthma be based on symptom frequency: A post-hoc efficacy analysis of the START study. Lancet 2017, 389, 157–166. [Google Scholar] [CrossRef]
  11. Romagnoli, M.; Caramori, G.; Braccioni, F.; Ravenna, F.; Barreiro, E.; Siafakas, N.M.; Vignola, A.M.; Chanez, P.; Fabbri, L.M.; Papi, A.; et al. Near-fatal asthma phenotype in the ENFUMOSA Cohort. Clin. Exp. Allergy 2007, 37, 552–557. [Google Scholar] [CrossRef] [PubMed]
  12. Wu, A.C.; Tantisira, K.; Li, L.; Schuemann, B.; Weiss, S.T.; Fuhlbrigge, A.L. Predictors of symptoms are different from predictors of severe exacerbations from asthma in children. Chest 2011, 140, 100–107. [Google Scholar] [CrossRef] [PubMed]
  13. Niu, C.; Xu, Y.; Schuler, C.L.; Gu, L.; Arora, K.; Huang, Y.; Naren, A.P.; Durrani, S.R.; Hossain, M.M.; Guilbert, T.W. Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations. J. Allergy Clin. Immunol. Pract. 2021, 9, 4393–4401.e8. [Google Scholar] [CrossRef] [PubMed]
  14. Fleming, L. Asthma exacerbation prediction: Recent insights. Curr. Opin. Allergy Clin. Immunol. 2018, 18, 117–123. [Google Scholar] [CrossRef] [PubMed]
  15. Grunwell, J.R.; Gillespie, S.; Morris, C.R.; Fitzpatrick, A.M. Latent Class Analysis of School-Age Children at Risk for Asthma Exacerbation. J. Allergy Clin. Immunol. Pract. 2020, 8, 2275–2284.e2. [Google Scholar] [CrossRef] [PubMed]
  16. Bacharier, L.B.; Phillips, B.R.; Bloomberg, G.R.; Zeiger, R.S.; Paul, I.M.; Krawiec, M.; Guilbert, T.; Chinchilli, V.M.; Strunk, R.C.; Childhood Asthma, R.; et al. Severe intermittent wheezing in preschool children: A distinct phenotype. J. Allergy Clin. Immunol. 2007, 119, 604–610. [Google Scholar] [CrossRef] [PubMed]
  17. Bloomberg, G.R.; Trinkaus, K.M.; Fisher, E.B., Jr.; Musick, J.R.; Strunk, R.C. Hospital readmissions for childhood asthma: A 10-year metropolitan study. Am. J. Respir. Crit. Care Med. 2003, 167, 1068–1076. [Google Scholar] [CrossRef] [PubMed]
  18. Haselkorn, T.; Zeiger, R.S.; Chipps, B.E.; Mink, D.R.; Szefler, S.J.; Simons, F.E.; Massanari, M.; Fish, J.E. Recent asthma exacerbations predict future exacerbations in children with severe or difficult-to-treat asthma. J. Allergy Clin. Immunol. 2009, 124, 921–927. [Google Scholar] [CrossRef]
  19. Akinbami, L.J.; Simon, A.E.; Rossen, L.M. Changing Trends in Asthma Prevalence Among Children. Pediatrics 2016, 137, 1–7. [Google Scholar] [CrossRef]
  20. Chipps, B.E.; Szefler, S.J.; Simons, F.E.; Haselkorn, T.; Mink, D.R.; Deniz, Y.; Lee, J.H.; Group, T.S. Demographic and clinical characteristics of children and adolescents with severe or difficult-to-treat asthma. J. Allergy Clin. Immunol. 2007, 119, 1156–1163. [Google Scholar] [CrossRef]
  21. Bateman, E.D.; Buhl, R.; O’Byrne, P.M.; Humbert, M.; Reddel, H.K.; Sears, M.R.; Jenkins, C.; Harrison, T.W.; Quirce, S.; Peterson, S.; et al. Development and validation of a novel risk score for asthma exacerbations: The risk score for exacerbations. J. Allergy Clin. Immunol. 2015, 135, 1457–1464.e4. [Google Scholar] [CrossRef] [PubMed]
  22. Schatz, M.; Zeiger, R.S.; Vollmer, W.M.; Mosen, D.; Apter, A.J.; Stibolt, T.B.; Leong, A.; Johnson, M.S.; Mendoza, G.; Cook, E.F. Validation of a beta-agonist long-term asthma control scale derived from computerized pharmacy data. J. Allergy Clin. Immunol. 2006, 117, 995–1000. [Google Scholar] [CrossRef] [PubMed]
  23. Zeiger, R.S.; Yegin, A.; Simons, F.E.; Haselkorn, T.; Rasouliyan, L.; Szefler, S.J.; Chipps, B.E.; Group, T.S. Evaluation of the National Heart, Lung, and Blood Institute guidelines impairment domain for classifying asthma control and predicting asthma exacerbations. Ann. Allergy Asthma Immunol. 2012, 108, 81–87. [Google Scholar] [CrossRef] [PubMed]
  24. Forno, E.; Fuhlbrigge, A.; Soto-Quiros, M.E.; Avila, L.; Raby, B.A.; Brehm, J.; Sylvia, J.M.; Weiss, S.T.; Celedon, J.C. Risk factors and predictive clinical scores for asthma exacerbations in childhood. Chest 2010, 138, 1156–1165. [Google Scholar] [CrossRef] [PubMed]
  25. Meltzer, E.O.; Busse, W.W.; Wenzel, S.E.; Belozeroff, V.; Weng, H.H.; Feng, J.; Chon, Y.; Chiou, C.F.; Globe, D.; Lin, S.L. Use of the Asthma Control Questionnaire to predict future risk of asthma exacerbation. J. Allergy Clin. Immunol. 2011, 127, 167–172. [Google Scholar] [CrossRef] [PubMed]
  26. Cajigal, S.; Wells, K.E.; Peterson, E.L.; Ahmedani, B.K.; Yang, J.J.; Kumar, R.; Burchard, E.G.; Williams, L.K. Predictive Properties of the Asthma Control Test and Its Component Questions for Severe Asthma Exacerbations. J. Allergy Clin. Immunol. Pract. 2017, 5, 121–127.e2. [Google Scholar] [CrossRef] [PubMed]
  27. Lo, D.K.; Beardsmore, C.S.; Roland, D.; Richardson, M.; Yang, Y.; Danvers, L.; Wilson, A.; Gaillard, E.A. Lung function and asthma control in school-age children managed in UK primary care: A cohort study. Thorax 2020, 75, 101–107. [Google Scholar] [CrossRef] [PubMed]
  28. Partridge, M.R.; van der Molen, T.; Myrseth, S.E.; Busse, W.W. Attitudes and actions of asthma patients on regular maintenance therapy: The INSPIRE study. BMC Pulm. Med. 2006, 6, 13. [Google Scholar] [CrossRef]
  29. Davis, K.J.; Disantostefano, R.; Peden, D.B. Is Johnny wheezing? Parent-child agreement in the Childhood Asthma in America survey. Pediatr. Allergy Immunol. 2011, 22, 31–35. [Google Scholar] [CrossRef]
  30. Ganti, P.; Suman, A.; Chaudhary, S.; Sangha, B.; David, L.; Sekhsaria, S. The effect of the socioeconomic status on the measurement of asthma control. Allergy Asthma Proc. 2022, 43, e11–e16. [Google Scholar] [CrossRef]
  31. Lee, W.Y.; Suh, D.I.; Song, D.J.; Baek, H.S.; Shin, M.; Yoo, Y.; Kwon, J.W.; Jang, G.C.; Yang, H.J.; Lee, E.; et al. Asthma control test reflects not only lung function but also airway inflammation in children with stable asthma. J. Asthma 2020, 57, 648–653. [Google Scholar] [CrossRef] [PubMed]
  32. Engelkes, M.; Janssens, H.M.; de Ridder, M.A.; Sturkenboom, M.C.; de Jongste, J.C.; Verhamme, K.M. Real life data on incidence and risk factors of severe asthma exacerbations in children in primary care. Respir. Med. 2016, 119, 48–54. [Google Scholar] [CrossRef] [PubMed]
  33. Covar, R.A.; Szefler, S.J.; Zeiger, R.S.; Sorkness, C.A.; Moss, M.; Mauger, D.T.; Boehmer, S.J.; Strunk, R.C.; Martinez, F.D.; Taussig, L.M.; et al. Factors associated with asthma exacerbations during a long-term clinical trial of controller medications in children. J. Allergy Clin. Immunol. 2008, 122, 741–747.e4. [Google Scholar] [CrossRef] [PubMed]
  34. Miller, M.K.; Lee, J.H.; Miller, D.P.; Wenzel, S.E.; Group, T.S. Recent asthma exacerbations: A key predictor of future exacerbations. Respir. Med. 2007, 101, 481–489. [Google Scholar] [CrossRef] [PubMed]
  35. Price, D.B.; Rigazio, A.; Campbell, J.D.; Bleecker, E.R.; Corrigan, C.J.; Thomas, M.; Wenzel, S.E.; Wilson, A.M.; Small, M.B.; Gopalan, G.; et al. Blood eosinophil count and prospective annual asthma disease burden: A UK cohort study. Lancet Respir. Med. 2015, 3, 849–858. [Google Scholar] [CrossRef] [PubMed]
  36. Robroeks, C.M.H.H.T.; van Vliet, D.; Jöbsis, Q.; Braekers, R.; Rijkers, G.T.; Wodzig, W.K.W.H.; Bast, A.; Zimmermann, L.J.I.; Dompeling, E. Prediction of asthma exacerbations in children: Results of a one-year prospective study. Clin. Exp. Allergy 2012, 42, 792–798. [Google Scholar] [CrossRef] [PubMed]
  37. Peters, M.C.; Mauger, D.; Ross, K.R.; Phillips, B.; Gaston, B.; Cardet, J.C.; Israel, E.; Levy, B.D.; Phipatanakul, W.; Jarjour, N.N.; et al. Evidence for Exacerbation-Prone Asthma and Predictive Biomarkers of Exacerbation Frequency. Am. J. Respir. Crit. Care Med. 2020, 202, 973–982. [Google Scholar] [CrossRef] [PubMed]
  38. Lowden, R.; Turner, S. Past asthma exacerbation in children predicting future exacerbation: A systematic review. ERJ Open Res. 2022, 8, 00174-2022. [Google Scholar] [CrossRef]
  39. Hauerslev, M.; Garpvall, K.; Marckmann, M.; Hermansen, M.N.; Hansen, K.S.; Chawes, B.L. Long-term predictors of loss of asthma control in school-aged well-controlled children with mild to moderate asthma: A 5-year follow-up. Pediatr. Pulmonol. 2022, 57, 81–89. [Google Scholar] [CrossRef]
  40. Cottini, M.; Lombardi, C.; Berti, A.; Comberiati, P. Small-airway dysfunction in paediatric asthma. Curr. Opin. Allergy Clin. Immunol. 2021, 21, 128–134. [Google Scholar] [CrossRef]
  41. Fuhlbrigge, A.L.; Kitch, B.T.; Paltiel, A.D.; Kuntz, K.M.; Neumann, P.J.; Dockery, D.W.; Weiss, S.T. FEV(1) is associated with risk of asthma attacks in a pediatric population. J. Allergy Clin. Immunol. 2001, 107, 61–67. [Google Scholar] [CrossRef] [PubMed]
  42. Kitch, B.T.; Paltiel, A.D.; Kuntz, K.M.; Dockery, D.W.; Schouten, J.P.; Weiss, S.T.; Fuhlbrigge, A.L. A Single Measure of FEV 1 Is Associated With Risk of Asthma Attacks in Long-term Follow-up. Chest 2004, 126, 1875–1882. [Google Scholar] [CrossRef] [PubMed]
  43. Fielding, S.; Pijnenburg, M.; de Jongste, J.C.; Pike, K.C.; Roberts, G.; Petsky, H.; Chang, A.B.; Fritsch, M.; Frischer, T.; Szefler, S.; et al. Change in FEV(1) and Feno Measurements as Predictors of Future Asthma Outcomes in Children. Chest 2019, 155, 331–341. [Google Scholar] [CrossRef] [PubMed]
  44. Sorkness, R.L.; Zoratti, E.M.; Kattan, M.; Gergen, P.J.; Evans, M.D.; Visness, C.M.; Gill, M.; Khurana Hershey, G.K.; Kercsmar, C.M.; Liu, A.H.; et al. Obstruction phenotype as a predictor of asthma severity and instability in children. J. Allergy Clin. Immunol. 2018, 142, 1090–1099.e4. [Google Scholar] [CrossRef] [PubMed]
  45. Kang, M.G.; Yoon, S.A.; Sim, J.H.; Woo, S.I. Fractional exhaled nitric oxide and forced expiratory volume in 1 second/forced vital capacity have predictive value of asthma exacerbation in Korean school children. Asia Pac. Allergy 2020, 10, e7. [Google Scholar] [CrossRef] [PubMed]
  46. Lazova, S.; Priftis, S.; Petrova, G.; Naseva, E.; Velikova, T. MMEF(25-75) may predict significant BDR and future risk of exacerbations in asthmatic children with normal baseline FEV(1). Int. J. Physiol. Pathophysiol. Pharmacol. 2022, 14, 33–47. [Google Scholar] [PubMed]
  47. Reddel, H.; Ware, S.; Marks, G.; Salome, C.; Jenkins, C.; Woolcock, A. Differences between asthma exacerbations and poor asthma control. Lancet 1999, 353, 364–369. [Google Scholar] [CrossRef] [PubMed]
  48. Frey, U.; Brodbeck, T.; Majumdar, A.; Taylor, D.R.; Town, G.I.; Silverman, M.; Suki, B. Risk of severe asthma episodes predicted from fluctuation analysis of airway function. Nature 2005, 438, 667–670. [Google Scholar] [CrossRef]
  49. Kim, M.; Kim, Y.M.; Lee, J.Y.; Yang, H.K.; Kim, H.; Ahn, S.; Baek, S.Y.; Kim, J.; Ahn, K. Seasonal and monthly variation in peak expiratory flow rate in children with asthma. Asia Pac. Allergy 2021, 11, e19. [Google Scholar] [CrossRef]
  50. Klok, T.; Kaptein, A.A.; Duiverman, E.J.; Brand, P.L. Long-term adherence to inhaled corticosteroids in children with asthma: Observational study. Respir. Med. 2015, 109, 1114–1119. [Google Scholar] [CrossRef]
  51. Vasbinder, E.C.; Belitser, S.V.; Souverein, P.C.; van Dijk, L.; Vulto, A.G.; van den Bemt, P.M. Non-adherence to inhaled corticosteroids and the risk of asthma exacerbations in children. Patient Prefer. Adherence 2016, 10, 531–538. [Google Scholar] [CrossRef] [PubMed]
  52. Engelkes, M.; Janssens, H.M.; de Jongste, J.C.; Sturkenboom, M.C.; Verhamme, K.M. Medication adherence and the risk of severe asthma exacerbations: A systematic review. Eur. Respir. J. 2014, 45, 396–407. [Google Scholar] [CrossRef] [PubMed]
  53. Lang, J.E.; Tang, M.; Zhao, C.; Hurst, J.; Wu, A.; Goldstein, B.A. Well-Child Care Attendance and Risk of Asthma Exacerbations. Pediatrics 2020, 146. [Google Scholar] [CrossRef] [PubMed]
  54. Liu, T.L.; Taylor, Y.J.; Mahabaleshwarkar, R.; Blanchette, C.M.; Tapp, H.; Dulin, M.F. Shared decision making and time to exacerbation in children with asthma. J. Asthma 2018, 55, 949–955. [Google Scholar] [CrossRef] [PubMed]
  55. Duncan, C.L.; Hogan, M.B.; Tien, K.J.; Graves, M.M.; Chorney, J.M.; Zettler, M.D.; Koven, L.; Wilson, N.W.; Dinakar, C.; Portnoy, J. Efficacy of a parent-youth teamwork intervention to promote adherence in pediatric asthma. J. Pediatr. Psychol. 2013, 38, 617–628. [Google Scholar] [CrossRef] [PubMed]
  56. Papadopoulos, N.G.; Mathioudakis, A.G.; Custovic, A.; Deschildre, A.; Phipatanakul, W.; Wong, G.; Xepapadaki, P.; Abou-Taam, R.; Agache, I.; Castro-Rodriguez, J.A.; et al. Childhood asthma outcomes during the COVID-19 pandemic: Findings from the PeARL multi-national cohort. Allergy 2021, 76, 1765–1775. [Google Scholar] [CrossRef] [PubMed]
  57. Normansell, R.; Kew, K.M.; Stovold, E. Interventions to improve adherence to inhaled steroids for asthma. Cochrane Database Syst. Rev. 2017, 4, CD012226. [Google Scholar] [CrossRef]
  58. Jochmann, A.; Artusio, L.; Jamalzadeh, A.; Nagakumar, P.; Delgado-Eckert, E.; Saglani, S.; Bush, A.; Frey, U.; Fleming, L.J. Electronic monitoring of adherence to inhaled corticosteroids: An essential tool in identifying severe asthma in children. Eur. Respir. J. 2017, 50, 1700910. [Google Scholar] [CrossRef]
  59. Akinbami, L.J.; Moorman, J.E.; Garbe, P.L.; Sondik, E.J. Status of childhood asthma in the United States, 1980-2007. Pediatrics 2009, 123 (Suppl. S3), S131–S145. [Google Scholar] [CrossRef]
  60. Leong, A.B.; Ramsey, C.D.; Celedon, J.C. The challenge of asthma in minority populations. Clin. Rev. Allergy Immunol. 2012, 43, 156–183. [Google Scholar] [CrossRef]
  61. Lo, D.; Beardsmore, C.; Roland, D.; Richardson, M.; Yang, Y.; Danvers, L.; Wilson, A.; Gaillard, E.A. Risk factors for asthma attacks and poor control in children: A prospective observational study in UK primary care. Arch. Dis. Child. 2022, 107, 26–31. [Google Scholar] [CrossRef] [PubMed]
  62. Kopel, L.S.; Phipatanakul, W.; Gaffin, J.M. Social Disadvantage and Asthma Control in Children. Paediatr. Respir. Rev. 2014, 15, 256–263. [Google Scholar] [CrossRef] [PubMed]
  63. Ardura-Garcia, C.; Stolbrink, M.; Zaidi, S.; Cooper, P.J.; Blakey, J.D. Predictors of repeated acute hospital attendance for asthma in children: A systematic review and meta-analysis. Pediatr. Pulmonol. 2018, 53, 1179–1192. [Google Scholar] [CrossRef] [PubMed]
  64. Forno, E.; Celedon, J.C. Asthma and ethnic minorities: Socioeconomic status and beyond. Curr. Opin. Allergy Clin. Immunol. 2009, 9, 154–160. [Google Scholar] [CrossRef] [PubMed]
  65. Ungar, W.J.; Paterson, J.M.; Gomes, T.; Bikangaga, P.; Gold, M.; To, T.; Kozyrskyj, A.L. Relationship of asthma management, socioeconomic status, and medication insurance characteristics to exacerbation frequency in children with asthma. Ann. Allergy Asthma Immunol. 2011, 106, 17–23. [Google Scholar] [CrossRef] [PubMed]
  66. Lieu, T.A.; Lozano, P.; Finkelstein, J.A.; Chi, F.W.; Jensvold, N.G.; Capra, A.M.; Quesenberry, C.P.; Selby, J.V.; Farber, H.J. Racial/ethnic variation in asthma status and management practices among children in managed medicaid. Pediatrics 2002, 109, 857–865. [Google Scholar] [CrossRef] [PubMed]
  67. Bender, B.G. Risk taking, depression, adherence, and symptom control in adolescents and young adults with asthma. Am. J. Respir. Crit. Care Med. 2006, 173, 953–957. [Google Scholar] [CrossRef] [PubMed]
  68. Chen, E.; Hanson, M.D.; Paterson, L.Q.; Griffin, M.J.; Walker, H.A.; Miller, G.E. Socioeconomic status and inflammatory processes in childhood asthma: The role of psychological stress. J. Allergy Clin. Immunol. 2006, 117, 1014–1020. [Google Scholar] [CrossRef]
  69. Brehm, J.M.; Ramratnam, S.K.; Tse, S.M.; Croteau-Chonka, D.C.; Pino-Yanes, M.; Rosas-Salazar, C.; Litonjua, A.A.; Raby, B.A.; Boutaoui, N.; Han, Y.Y.; et al. Stress and Bronchodilator Response in Children with Asthma. Am. J. Respir. Crit. Care Med. 2015, 192, 47–56. [Google Scholar] [CrossRef]
  70. Forno, E.; Lescher, R.; Strunk, R.; Weiss, S.; Fuhlbrigge, A.; Celedon, J.C. Decreased response to inhaled steroids in overweight and obese asthmatic children. J. Allergy Clin. Immunol. 2011, 127, 741–749. [Google Scholar] [CrossRef]
  71. Katon, W.J.; Richardson, L.; Lozano, P.; McCauley, E. The relationship of asthma and anxiety disorders. Psychosom. Med. 2004, 66, 349–355. [Google Scholar] [CrossRef] [PubMed]
  72. Vila, G.; Nollet-Clemencon, C.; de Blic, J.; Mouren-Simeoni, M.C.; Scheinmann, P. Prevalence of DSM IV anxiety and affective disorders in a pediatric population of asthmatic children and adolescents. J. Affect. Disord. 2000, 58, 223–231. [Google Scholar] [CrossRef] [PubMed]
  73. Sandberg, S.; Jarvenpaa, S.; Penttinen, A.; Paton, J.Y.; McCann, D.C. Asthma exacerbations in children immediately following stressful life events: A Cox’s hierarchical regression. Thorax 2004, 59, 1046–1051. [Google Scholar] [CrossRef] [PubMed]
  74. Sandberg, S.; Paton, J.Y.; Ahola, S.; McCann, D.C.; McGuinness, D.; Hillary, C.R.; Oja, H. The role of acute and chronic stress in asthma attacks in children. Lancet 2000, 356, 982–987. [Google Scholar] [CrossRef] [PubMed]
  75. Stevens, E.L.; Han, Y.Y.; Rosser, F.; Forno, E.; Acosta-Perez, E.; Miller, G.E.; Canino, G.; Celedon, J.C. Maternal Depressive Symptoms, Lung Function, and Severe Asthma Exacerbations in Puerto Rican Children. J. Allergy Clin. Immunol. Pract. 2021, 9, 1319–1326.e3. [Google Scholar] [CrossRef] [PubMed]
  76. Patel, M.; Pilcher, J.; Reddel, H.K.; Pritchard, A.; Corin, A.; Helm, C.; Tofield, C.; Shaw, D.; Black, P.; Weatherall, M.; et al. Metrics of salbutamol use as predictors of future adverse outcomes in asthma. Clin. Exp. Allergy 2013, 43, 1144–1151. [Google Scholar] [CrossRef] [PubMed]
  77. Fy, O.K.R. Why Asthma Still Kills. Ulster Med. J. 2017, 86, 44. [Google Scholar]
  78. Johnston, S.L.; Edwards, M.R. Mechanisms of adverse effects of beta-agonists in asthma. Thorax 2009, 64, 739–741. [Google Scholar] [CrossRef]
  79. Edwards, M.R.; Haas, J.; Panettieri, R.A., Jr.; Johnson, M.; Johnston, S.L. Corticosteroids and beta2 agonists differentially regulate rhinovirus-induced interleukin-6 via distinct Cis-acting elements. J. Biol. Chem. 2007, 282, 15366–15375. [Google Scholar] [CrossRef]
  80. Nwaru, B.I.; Ekstrom, M.; Hasvold, P.; Wiklund, F.; Telg, G.; Janson, C. Overuse of short-acting beta(2)-agonists in asthma is associated with increased risk of exacerbation and mortality: A nationwide cohort study of the global SABINA programme. Eur. Respir. J. 2020, 55, 1901872. [Google Scholar] [CrossRef]
  81. Xia, Y.; Kelton, C.M.L.; Xue, L.; Guo, J.J.; Bian, B.; Wigle, P.R. Safety of long-acting beta agonists and inhaled corticosteroids in children and adolescents with asthma. Ther. Adv. Drug Saf. 2013, 4, 254–263. [Google Scholar] [CrossRef] [PubMed]
  82. Dunican, E.M.; Fahy, J.V. The Role of Type 2 Inflammation in the Pathogenesis of Asthma Exacerbations. Ann. Am. Thorac. Soc. 2015, 12, S144–S149. [Google Scholar] [CrossRef] [PubMed]
  83. Bush, A. Translating Asthma: Dissecting the Role of Metabolomics, Genomics and Personalized Medicine. Indian J. Pediatr. 2018, 85, 643–650. [Google Scholar] [CrossRef] [PubMed]
  84. Bush, A. Pathophysiological Mechanisms of Asthma. Front. Pediatr. 2019, 7, 68. [Google Scholar] [CrossRef] [PubMed]
  85. Wojsyk-Banaszak, I.; Mikoś, M.; Szczepankiewicz, A.; Wielebska, A.; Sobkowiak, P.; Kamińska, A.; Bręborowicz, A. Evaluation of exhaled breath temperature (EBT) as a marker and predictor of asthma exacerbation in children and adolescents. J. Asthma 2017, 54, 699–705. [Google Scholar] [CrossRef] [PubMed]
  86. Ulrik, C.S.; Lange, P.; Hilberg, O. Fractional exhaled nitric oxide as a determinant for the clinical course of asthma: A systematic review. Eur. Clin. Respir. J. 2021, 8, 1891725. [Google Scholar] [CrossRef] [PubMed]
  87. Li, R.; Dong, X.Y.; Jiang, K.; Wang, C.; Sun, C.; Yuan, L.; Dong, N. Application of fractional exhaled nitric oxide and nasal nitric oxide in control evaluation of bronchial asthma and diagnosis of allergic rhinitis in children. Zhongguo Dang Dai Er Ke Za Zhi 2022, 24, 90–95. [Google Scholar] [CrossRef] [PubMed]
  88. Kharitonov, S.A.; Barnes, P.J. Exhaled biomarkers. Chest 2006, 130, 1541–1546. [Google Scholar] [CrossRef]
  89. Diamant, N.; Amirav, I.; Armoni-Domany, K.; Sadot, E.; Shapira, U.; Cahal, M.; Be’er, M.; Rochman, M.; Lavie, M. High fractional exhaled nitric oxide levels in asthma patients: Does size matter? Pediatr. Pulmonol. 2021, 56, 1449–1454. [Google Scholar] [CrossRef]
  90. Harkins, M.S.; Fiato, K.L.; Iwamoto, G.K. Exhaled nitric oxide predicts asthma exacerbation. J. Asthma 2004, 41, 471–476. [Google Scholar] [CrossRef]
  91. Visser, C.A.; Brand, P.L. Does a single measurement of exhaled nitric oxide predict asthma exacerbations? Arch. Dis. Child. 2011, 96, 781–782. [Google Scholar] [CrossRef] [PubMed]
  92. Zacharasiewicz, A.; Erin, E.M.; Bush, A. Noninvasive monitoring of airway inflammation and steroid reduction in children with asthma. Curr. Opin. Allergy Clin. Immunol. 2006, 6, 155–160. [Google Scholar] [CrossRef] [PubMed]
  93. Turner, S.; Cotton, S.; Wood, J.; Bell, V.; Raja, E.A.; Scott, N.W.; Morgan, H.; Lawrie, L.; Emele, D.; Kennedy, C.; et al. Reducing asthma attacks in children using exhaled nitric oxide (RAACENO) as a biomarker to inform treatment strategy: A multicentre, parallel, randomised, controlled, phase 3 trial. Lancet Respir. Med. 2022, 10, 584–592. [Google Scholar] [CrossRef] [PubMed]
  94. Turner, S.; Cotton, S.; Wood, J.; Bell, V.; Raja, E.A.; Scott, N.W.; Morgan, H.; Lawrie, L.; Emele, D.; Kennedy, C.; et al. Treatment Guided by Fractional Exhaled Nitric Oxide in Addition to Standard Care in 6- to 15-Year-Olds with Asthma: The RAACENO RCT; Efficacy and Mechanism Evaluation: Southampton, UK, 2022. [CrossRef]
  95. Fleming, L.; Tsartsali, L.; Wilson, N.; Regamey, N.; Bush, A. Non-invasive markers of inflammation as predictors of a severe exacerbation in children with problematic asthma. Thorax 2008, 63, A35–A37. [Google Scholar]
  96. Ardura-Garcia, C.; Arias, E.; Hurtado, P.; Bonnett, L.J.; Sandoval, C.; Maldonado, A.; Workman, L.J.; Platts-Mills, T.A.E.; Cooper, P.J.; Blakey, J.D. Predictors of severe asthma attack re-attendance in Ecuadorian children: A cohort study. Eur. Respir. J. 2019, 54, 1802419. [Google Scholar] [CrossRef] [PubMed]
  97. Wang, Z.; Pianosi, P.; Keogh, K.; Zaiem, F.; Alsawas, M.; Alahdab, F.; Almasri, J.; Mohammed, K.; Larrea-Mantilla, L.; Farah, W.; et al. The Clinical Utility of Fractional Exhaled Nitric Oxide (FeNO) in Asthma Management; US Agency for Healthcare Research and Quality: Rockville, MD, USA, 2017.
  98. van Vliet, D.; Alonso, A.; Rijkers, G.; Heynens, J.; Rosias, P.; Muris, J.; Jobsis, Q.; Dompeling, E. Prediction of asthma exacerbations in children by innovative exhaled inflammatory markers: Results of a longitudinal study. PLoS ONE 2015, 10, e0119434. [Google Scholar] [CrossRef] [PubMed]
  99. Wang, X.; Tan, X.; Li, Q. Effectiveness of fractional exhaled nitric oxide for asthma management in children: A systematic review and meta-analysis. Pediatr. Pulmonol. 2020, 55, 1936–1945. [Google Scholar] [CrossRef] [PubMed]
  100. Petsky, H.L.; Cates, C.J.; Kew, K.M.; Chang, A.B. Tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils): A systematic review and meta-analysis. Thorax 2018, 73, 1110–1119. [Google Scholar] [CrossRef]
  101. Turner, S. Exhaled nitric oxide and the management of childhood asthma--yet another promising biomarker "has been" or a misunderstood gem. Paediatr. Respir. Rev. 2015, 16, 88–96. [Google Scholar] [CrossRef]
  102. Paraskakis, E.; Zihlif, N.; Bush, A. Nitric oxide production in PCD: Possible evidence for differential nitric oxide synthase function. Pediatr. Pulmonol. 2007, 42, 876–880. [Google Scholar] [CrossRef]
  103. Paraskakis, E.; Sarikloglou, E.; Fouzas, S.; Steiropoulos, P.; Tsalkidis, A.; Bush, A. Improved prediction of asthma exacerbations by measuring distal airway inflammation. Eur. Respir. J. 2022, 60, 2101684. [Google Scholar] [CrossRef] [PubMed]
  104. Buendia, J.A.; Talamoni, H.L. Cost-utility of use of sputum eosinophil counts to guide management in children with asthma. J. Asthma 2022, 59, 31–37. [Google Scholar] [CrossRef] [PubMed]
  105. Green, R.H.; Brightling, C.E.; McKenna, S.; Hargadon, B.; Parker, D.; Bradding, P.; Wardlaw, A.J.; Pavord, I.D. Asthma exacerbations and sputum eosinophil counts: A randomised controlled trial. Lancet 2002, 360, 1715–1721. [Google Scholar] [CrossRef] [PubMed]
  106. Little, F.F.; Delgado, D.M.; Wexler, P.J.; Oppenheim, F.G.; Mitchell, P.; Feldman, J.A.; Walt, D.R.; Peng, R.D.; Matsui, E.C. Salivary inflammatory mediator profiling and correlation to clinical disease markers in asthma. PLoS ONE 2014, 9, e84449. [Google Scholar] [CrossRef] [PubMed]
  107. van Vliet, D.; Smolinska, A.; Jobsis, Q.; Rosias, P.; Muris, J.; Dallinga, J.; Dompeling, E.; van Schooten, F.J. Can exhaled volatile organic compounds predict asthma exacerbations in children? J. Breath. Res. 2017, 11, 016016. [Google Scholar] [CrossRef] [PubMed]
  108. Robroeks, C.M.; van Berkel, J.J.; Jobsis, Q.; van Schooten, F.J.; Dallinga, J.W.; Wouters, E.F.; Dompeling, E. Exhaled volatile organic compounds predict exacerbations of childhood asthma in a 1-year prospective study. Eur. Respir. J. 2013, 42, 98–106. [Google Scholar] [CrossRef] [PubMed]
  109. Neerincx, A.H.; Vijverberg, S.J.H.; Bos, L.D.J.; Brinkman, P.; Van Der Schee, M.P.; De Vries, R.; Sterk, P.J.; Maitland-van Der Zee, A.-H. Breathomics from exhaled volatile organic compounds in pediatric asthma. Pediatr. Pulmonol. 2017, 52, 1616–1627. [Google Scholar] [CrossRef]
  110. Mogensen, I.; Alving, K.; Jacinto, T.; Fonseca, J.; Janson, C.; Malinovschi, A. Simultaneously elevated FeNO and blood eosinophils relate to asthma morbidity in asthmatics from NHANES 2007-12. Clin. Exp. Allergy 2018, 48, 935–943. [Google Scholar] [CrossRef]
  111. Denlinger, L.C.; Phillips, B.R.; Ramratnam, S.; Ross, K.; Bhakta, N.R.; Cardet, J.C.; Castro, M.; Peters, S.P.; Phipatanakul, W.; Aujla, S.; et al. Inflammatory and Comorbid Features of Patients with Severe Asthma and Frequent Exacerbations. Am. J. Respir. Crit. Care Med. 2017, 195, 302–313. [Google Scholar] [CrossRef]
  112. Malinovschi, A.; Fonseca, J.A.; Jacinto, T.; Alving, K.; Janson, C. Exhaled nitric oxide levels and blood eosinophil counts independently associate with wheeze and asthma events in National Health and Nutrition Examination Survey subjects. J. Allergy Clin. Immunol. 2013, 132, 821–827.e5. [Google Scholar] [CrossRef]
  113. Malinovschi, A.; Janson, C.; Borres, M.; Alving, K. Simultaneously increased fraction of exhaled nitric oxide levels and blood eosinophil counts relate to increased asthma morbidity. J. Allergy Clin. Immunol. 2016, 138, 1301–1308.e2. [Google Scholar] [CrossRef] [PubMed]
  114. Buhl, R.; Korn, S.; Menzies-Gow, A.; Aubier, M.; Chapman, K.R.; Canonica, G.W.; Picado, C.; Martin, N.; Escobar, R.A.; Korom, S.; et al. Assessing biomarkers in a real-world severe asthma study (ARIETTA). Respir. Med. 2016, 115, 7–12. [Google Scholar] [CrossRef] [PubMed]
  115. Poole, A.; Urbanek, C.; Eng, C.; Schageman, J.; Jacobson, S.; O’Connor, B.P.; Galanter, J.M.; Gignoux, C.R.; Roth, L.A.; Kumar, R.; et al. Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease. J. Allergy Clin. Immunol. 2014, 133, 670–678.e2. [Google Scholar] [CrossRef] [PubMed]
  116. Woodruff, P.G.; Boushey, H.A.; Dolganov, G.M.; Barker, C.S.; Yang, Y.H.; Donnelly, S.; Ellwanger, A.; Sidhu, S.S.; Dao-Pick, T.P.; Pantoja, C.; et al. Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc. Natl. Acad. Sci. USA 2007, 104, 15858–15863. [Google Scholar] [CrossRef] [PubMed]
  117. Woodruff, P.G.; Modrek, B.; Choy, D.F.; Jia, G.; Abbas, A.R.; Ellwanger, A.; Koth, L.L.; Arron, J.R.; Fahy, J.V. T-helper type 2-driven inflammation defines major subphenotypes of asthma. Am. J. Respir. Crit. Care Med. 2009, 180, 388–395. [Google Scholar] [CrossRef] [PubMed]
  118. Jackson, D.J.; Bacharier, L.B.; Calatroni, A.; Gill, M.A.; Hu, J.; Liu, A.H.; Wheatley, L.M.; Gern, J.E.; Gruchalla, R.S.; Khurana Hershey, G.K.; et al. Serum IL-6: A biomarker in childhood asthma? J. Allergy Clin. Immunol. 2020, 145, 1701–1704.e3. [Google Scholar] [CrossRef] [PubMed]
  119. Shah, S.N.; Grunwell, J.R.; Mohammad, A.F.; Stephenson, S.T.; Lee, G.B.; Vickery, B.P.; Fitzpatrick, A.M. Performance of Eosinophil Cationic Protein as a Biomarker in Asthmatic Children. J. Allergy Clin. Immunol. Pract. 2021, 9, 2761–2769.e2. [Google Scholar] [CrossRef] [PubMed]
  120. Anderson, W.C., 3rd; Szefler, S.J. Controlling the Risk Domain in Pediatric Asthma through Personalized Care. Semin. Respir. Crit. Care Med. 2018, 39, 36–44. [Google Scholar] [CrossRef]
  121. Liu, L.; Gao, Y.; Si, Y.; Liu, B.; Liu, X.; Li, G.; Wang, R. MALT1 in asthma children: A potential biomarker for monitoring exacerbation risk and Th1/Th2 imbalance-mediated inflammation. J. Clin. Lab. Anal. 2022, 36, e24379. [Google Scholar] [CrossRef]
  122. Abd El-Motaleb, G.S.; Abou Amer, A.A.; Elawa, G.M.; Abo Alsood Abd Elfattah, M. Study of urinary leukotriene E4 levels in children with acute asthma. Int. J. Gen. Med. 2014, 7, 131–135. [Google Scholar] [CrossRef]
  123. Busse, W.; Kraft, M. Cysteinyl leukotrienes in allergic inflammation: Strategic target for therapy. Chest 2005, 127, 1312–1326. [Google Scholar] [CrossRef] [PubMed]
  124. Rabinovitch, N.; Reisdorph, N.; Silveira, L.; Gelfand, E.W. Urinary leukotriene E(4) levels identify children with tobacco smoke exposure at risk for asthma exacerbation. J. Allergy Clin. Immunol. 2011, 128, 323–327. [Google Scholar] [CrossRef] [PubMed]
  125. Babadi, R.S.; Riederer, A.M.; Sampson, P.D.; Sathyanarayana, S.; Kavanagh, T.J.; Krenz, J.E.; Andra, S.S.; Kim-Schulze, S.; Jansen, K.L.; Torres, E.; et al. Longitudinal measures of phthalate exposure and asthma exacerbation in a rural agricultural cohort of Latino children in Yakima Valley, Washington. Int. J. Hyg. Environ. Health 2022, 243, 113954. [Google Scholar] [CrossRef] [PubMed]
  126. Papamichael, M.M.; Katsardis, C.; Erbas, B.; Itsiopoulos, C.; Tsoukalas, D. Urinary organic acids as biomarkers in the assessment of pulmonary function in children with asthma. Nutr. Res. 2019, 61, 31–40. [Google Scholar] [CrossRef] [PubMed]
  127. Bonnelykke, K.; Sleiman, P.; Nielsen, K.; Kreiner-Moller, E.; Mercader, J.M.; Belgrave, D.; den Dekker, H.T.; Husby, A.; Sevelsted, A.; Faura-Tellez, G.; et al. A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations. Nat. Genet. 2014, 46, 51–55. [Google Scholar] [CrossRef] [PubMed]
  128. Sood, N.; Connolly, J.J.; Mentch, F.D.; Vazquez, L.; Sleiman, P.M.A.; Hysinger, E.B.; Hakonarson, H. Leveraging electronic health records to assess the role of ADRB2 single nucleotide polymorphisms in predicting exacerbation frequency in asthma patients. Pharmacogenet. Genom. 2018, 28, 256–259. [Google Scholar] [CrossRef] [PubMed]
  129. Yan, Q.; Forno, E.; Herrera-Luis, E.; Pino-Yanes, M.; Yang, G.; Oh, S.; Acosta-Perez, E.; Hu, D.; Eng, C.; Huntsman, S.; et al. A genome-wide association study of asthma hospitalizations in adults. J. Allergy Clin. Immunol. 2021, 147, 933–940. [Google Scholar] [CrossRef] [PubMed]
  130. Kho, A.T.; McGeachie, M.J.; Moore, K.G.; Sylvia, J.M.; Weiss, S.T.; Tantisira, K.G. Circulating microRNAs and prediction of asthma exacerbation in childhood asthma. Respir. Res. 2018, 19, 128. [Google Scholar] [CrossRef]
  131. Turner, S.; Francis, B.; Vijverberg, S.; Pino-Yanes, M.; Maitland-van der Zee, A.H.; Basu, K.; Bignell, L.; Mukhopadhyay, S.; Tavendale, R.; Palmer, C.; et al. Childhood asthma exacerbations and the Arg16 beta2-receptor polymorphism: A meta-analysis stratified by treatment. J. Allergy Clin. Immunol. 2016, 138, 107–113.e5. [Google Scholar] [CrossRef]
  132. Altman, M.C.; Gill, M.A.; Whalen, E.; Babineau, D.C.; Shao, B.; Liu, A.H.; Jepson, B.; Gruchalla, R.S.; O’Connor, G.T.; Pongracic, J.A.; et al. Transcriptome networks identify mechanisms of viral and nonviral asthma exacerbations in children. Nat. Immunol. 2019, 20, 637–651. [Google Scholar] [CrossRef]
  133. Hurst, J.H.; Zhao, C.; Hostetler, H.P.; Ghiasi Gorveh, M.; Lang, J.E.; Goldstein, B.A. Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. BMC Med. Inform. Decis. Mak. 2022, 22, 108. [Google Scholar] [CrossRef] [PubMed]
  134. Murray, C.S.; Poletti, G.; Kebadze, T.; Morris, J.; Woodcock, A.; Johnston, S.L.; Custovic, A. Study of modifiable risk factors for asthma exacerbations: Virus infection and allergen exposure increase the risk of asthma hospital admissions in children. Thorax 2006, 61, 376–382. [Google Scholar] [CrossRef] [PubMed]
  135. Message, S.D.; Johnston, S.L. Viruses in asthma. Br. Med. Bull. 2002, 61, 29–43. [Google Scholar] [CrossRef] [PubMed]
  136. Jackson, D.J.; Gangnon, R.E.; Evans, M.D.; Roberg, K.A.; Anderson, E.L.; Pappas, T.E.; Printz, M.C.; Lee, W.M.; Shult, P.A.; Reisdorf, E.; et al. Wheezing rhinovirus illnesses in early life predict asthma development in high-risk children. Am. J. Respir. Crit. Care Med. 2008, 178, 667–672. [Google Scholar] [CrossRef] [PubMed]
  137. Johnston, N.W.; Johnston, S.L.; Duncan, J.M.; Greene, J.M.; Kebadze, T.; Keith, P.K.; Roy, M.; Waserman, S.; Sears, M.R. The September epidemic of asthma exacerbations in children: A search for etiology. J. Allergy Clin. Immunol. 2005, 115, 132–138. [Google Scholar] [CrossRef] [PubMed]
  138. Zheng, S.Y.; Wang, L.L.; Ren, L.; Luo, J.; Liao, W.; Liu, E.M. Epidemiological analysis and follow-up of human rhinovirus infection in children with asthma exacerbation. J. Med. Virol. 2018, 90, 219–228. [Google Scholar] [CrossRef] [PubMed]
  139. Sallard, E.; Schult, F.; Baehren, C.; Buedding, E.; Mboma, O.; Ahmad-Nejad, P.; Ghebremedhin, B.; Ehrhardt, A.; Wirth, S.; Aydin, M. Viral Infection and Respiratory Exacerbation in Children: Results from a Local German Pediatric Exacerbation Cohort. Viruses 2022, 14, 491. [Google Scholar] [CrossRef] [PubMed]
  140. Sayed, S.; Diwadkar, A.R.; Dudley, J.W.; O’Brien, J.; Dvorin, D.; Kenyon, C.C.; Himes, B.E.; Hill, D.A.; Henrickson, S.E. COVID-19 Pandemic-Related Reductions in Pediatric Asthma Exacerbations Corresponded with an Overall Decrease in Respiratory Viral Infections. J. Allergy Clin. Immunol. Pract. 2022, 10, 91–99.e12. [Google Scholar] [CrossRef]
  141. McCauley, K.E.; Flynn, K.; Calatroni, A.; DiMassa, V.; LaMere, B.; Fadrosh, D.W.; Lynch, K.V.; Gill, M.A.; Pongracic, J.A.; Khurana Hershey, G.K.; et al. Seasonal airway microbiome and transcriptome interactions promote childhood asthma exacerbations. J. Allergy Clin. Immunol. 2022, 150, 204–213. [Google Scholar] [CrossRef]
  142. Johnston, N.W.; Sears, M.R. Asthma exacerbations 1: Epidemiology. Thorax 2006, 61, 722–728. [Google Scholar] [CrossRef]
  143. Lincoln, D.; Morgan, G.; Sheppeard, V.; Jalaludin, B.; Corbett, S.; Beard, J. Childhood asthma and return to school in Sydney, Australia. Public Health 2006, 120, 854–862. [Google Scholar] [CrossRef] [PubMed]
  144. Larsen, K.; Zhu, J.; Feldman, L.Y.; Simatovic, J.; Dell, S.; Gershon, A.S.; To, T. The Annual September Peak in Asthma Exacerbation Rates. Still a Reality? Ann. Am. Thorac. Soc. 2016, 13, 231–239. [Google Scholar] [CrossRef] [PubMed]
  145. Teach, S.J.; Gill, M.A.; Togias, A.; Sorkness, C.A.; Arbes, S.J., Jr.; Calatroni, A.; Wildfire, J.J.; Gergen, P.J.; Cohen, R.T.; Pongracic, J.A.; et al. Preseasonal treatment with either omalizumab or an inhaled corticosteroid boost to prevent fall asthma exacerbations. J. Allergy Clin. Immunol. 2015, 136, 1476–1485. [Google Scholar] [CrossRef] [PubMed]
  146. Tosca, M.A.; Ruffoni, S.; Canonica, G.W.; Ciprandi, G. Asthma exacerbation in children: Relationship among pollens, weather, and air pollution. Allergol. Immunopathol. (Madr.) 2014, 42, 362–368. [Google Scholar] [CrossRef] [PubMed]
  147. Teach, S.J.; Gergen, P.J.; Szefler, S.J.; Mitchell, H.E.; Calatroni, A.; Wildfire, J.; Bloomberg, G.R.; Kercsmar, C.M.; Liu, A.H.; Makhija, M.M.; et al. Seasonal risk factors for asthma exacerbations among inner-city children. J. Allergy Clin. Immunol. 2015, 135, 1465–1473.e5. [Google Scholar] [CrossRef] [PubMed]
  148. Chane-Si-Ken, N.; Allou, N.; Beneteau, S.; Verduyn, M.; Gazaille, V.; Raherison, C.; Andre, M. Asthma exacerbations in Reunion Island: Environmental factors. Respir. Med. Res. 2022, 81, 100779. [Google Scholar] [CrossRef] [PubMed]
  149. Yu, H.R.; Lin, C.R.; Tsai, J.H.; Hsieh, Y.T.; Tsai, T.A.; Tsai, C.K.; Lee, Y.C.; Liu, T.Y.; Tsai, C.M.; Chen, C.C.; et al. A Multifactorial Evaluation of the Effects of Air Pollution and Meteorological Factors on Asthma Exacerbation. Int. J. Environ. Res. Public Health 2020, 17, 4010. [Google Scholar] [CrossRef] [PubMed]
  150. D’Amato, G.; Vitale, C.; D’Amato, M.; Cecchi, L.; Liccardi, G.; Molino, A.; Vatrella, A.; Sanduzzi, A.; Maesano, C.; Annesi-Maesano, I. Thunderstorm-related asthma: What happens and why. Clin. Exp. Allergy 2016, 46, 390–396. [Google Scholar] [CrossRef]
  151. Kouis, P.; Michaelidou, E.; Kinni, P.; Michanikou, A.; Anagnostopoulou, P.; Dimitriou, H.; Karanicolas, K.; Matthaiou, A.M.; Achilleos, S.; Papatheodorou, S.I.; et al. Pediatric asthma symptom control during lockdown for the COVID-19 pandemic in Spring 2020: A prospective community-based study in Cyprus and Greece. Pediatr. Pulmonol. 2022, 57, 386–394. [Google Scholar] [CrossRef]
  152. Soto-Quiros, M.; Avila, L.; Platts-Mills, T.A.; Hunt, J.F.; Erdman, D.D.; Carper, H.; Murphy, D.D.; Odio, S.; James, H.R.; Patrie, J.T.; et al. High titers of IgE antibody to dust mite allergen and risk for wheezing among asthmatic children infected with rhinovirus. J. Allergy Clin. Immunol. 2012, 129, 1499–1505.e5. [Google Scholar] [CrossRef]
  153. Murray, C.S.; Foden, P.; Sumner, H.; Shepley, E.; Custovic, A.; Simpson, A. Preventing Severe Asthma Exacerbations in Children. A Randomized Trial of Mite-Impermeable Bedcovers. Am. J. Respir. Crit. Care Med. 2017, 196, 150–158. [Google Scholar] [CrossRef] [PubMed]
  154. Kanchongkittiphon, W.; Mendell, M.J.; Gaffin, J.M.; Wang, G.; Phipatanakul, W. Indoor environmental exposures and exacerbation of asthma: An update to the 2000 review by the Institute of Medicine. Environ. Health Perspect. 2015, 123, 6–20. [Google Scholar] [CrossRef] [PubMed]
  155. Romieu, I.; Meneses, F.; Sienra-Monge, J.J.; Huerta, J.; Ruiz Velasco, S.; White, M.C.; Etzel, R.A.; Hernandez-Avila, M. Effects of urban air pollutants on emergency visits for childhood asthma in Mexico City. Am. J. Epidemiol. 1995, 141, 546–553. [Google Scholar] [CrossRef] [PubMed]
  156. Schildcrout, J.S.; Sheppard, L.; Lumley, T.; Slaughter, J.C.; Koenig, J.Q.; Shapiro, G.G. Ambient air pollution and asthma exacerbations in children: An eight-city analysis. Am. J. Epidemiol. 2006, 164, 505–517. [Google Scholar] [CrossRef] [PubMed]
  157. Anderson, H.R.; Bremner, S.A.; Atkinson, R.W.; Harrison, R.M.; Walters, S. Particulate matter and daily mortality and hospital admissions in the west midlands conurbation of the United Kingdom: Associations with fine and coarse particles, black smoke and sulphate. Occup. Environ. Med. 2001, 58, 504–510. [Google Scholar] [CrossRef] [PubMed]
  158. Altman, M.C.; Kattan, M.; O’Connor, G.T.; Murphy, R.C.; Whalen, E.; LeBeau, P.; Calatroni, A.; Gill, M.A.; Gruchalla, R.S.; Liu, A.H.; et al. Associations between outdoor air pollutants and non-viral asthma exacerbations and airway inflammatory responses in children and adolescents living in urban areas in the USA: A retrospective secondary analysis. Lancet Planet. Health 2023, 7, e33–e44. [Google Scholar] [CrossRef] [PubMed]
  159. Huang, J.; Yang, X.; Fan, F.; Hu, Y.; Wang, X.; Zhu, S.; Ren, G.; Wang, G. Outdoor air pollution and the risk of asthma exacerbations in single lag0 and lag1 exposure patterns: A systematic review and meta-analysis. J. Asthma 2022, 59, 2322–2339. [Google Scholar] [CrossRef]
  160. Solanki, N.; Bruckman, D.; Wang, X.; Tang, A.; Attaway, A.; Khatri, S. Nitrogen dioxide, an EPA parameter, may forecast the incidence of asthma exacerbations across urban areas: An observational study. Pediatr. Pulmonol. 2023, 58, 262–270. [Google Scholar] [CrossRef]
  161. Dosanjh, A. Complex pathways leading to future paediatric asthma exacerbations. ERJ Open Res. 2022, 8. [Google Scholar] [CrossRef]
  162. Zhang, Y.; Ni, H.; Bai, L.; Cheng, Q.; Zhang, H.; Wang, S.; Xie, M.; Zhao, D.; Su, H. The short-term association between air pollution and childhood asthma hospital admissions in urban areas of Hefei City in China: A time-series study. Environ. Res. 2019, 169, 510–516. [Google Scholar] [CrossRef]
  163. Zhao, Y.; Kong, D.; Fu, J.; Zhang, Y.; Chen, Y.; Liu, Y.; Chang, Z.; Liu, Y.; Liu, X.; Xu, K.; et al. Increased Risk of Hospital Admission for Asthma in Children From Short-Term Exposure to Air Pollution: Case-Crossover Evidence From Northern China. Front. Public Health 2021, 9, 798746. [Google Scholar] [CrossRef]
  164. Orellano, P.; Quaranta, N.; Reynoso, J.; Balbi, B.; Vasquez, J. Effect of outdoor air pollution on asthma exacerbations in children and adults: Systematic review and multilevel meta-analysis. PLoS ONE 2017, 12, e0174050. [Google Scholar] [CrossRef] [PubMed]
  165. Liu, L.; Liu, C.; Chen, R.; Zhou, Y.; Meng, X.; Hong, J.; Cao, L.; Lu, Y.; Dong, X.; Xia, M.; et al. Associations of short-term exposure to air pollution and emergency department visits for pediatric asthma in Shanghai, China. Chemosphere 2021, 263, 127856. [Google Scholar] [CrossRef] [PubMed]
  166. Bonato, M.; Gallo, E.; Turrin, M.; Bazzan, E.; Baraldi, F.; Saetta, M.; Gregori, D.; Papi, A.; Contoli, M.; Baraldo, S. Air Pollution Exposure Impairs Airway Epithelium IFN-beta Expression in Pre-School Children. Front. Immunol. 2021, 12, 731968. [Google Scholar] [CrossRef] [PubMed]
  167. Zheng, P.; Zhang, B.; Zhang, K.; Lv, X.; Wang, Q.; Bai, X. The Impact of Air Pollution on Intestinal Microbiome of Asthmatic Children: A Panel Study. Biomed. Res. Int. 2020, 2020, 5753427. [Google Scholar] [CrossRef] [PubMed]
  168. He, L.; Norris, C.; Cui, X.; Li, Z.; Barkjohn, K.K.; Teng, Y.; Fang, L.; Lin, L.; Wang, Q.; Zhou, X.; et al. Oral cavity response to air pollutant exposure and association with pulmonary inflammation and symptoms in asthmatic children. Environ. Res. 2022, 206, 112275. [Google Scholar] [CrossRef] [PubMed]
  169. Vargas, P.A.; Brenner, B.; Clark, S.; Boudreaux, E.D.; Camargo, C.A., Jr. Exposure to environmental tobacco smoke among children presenting to the emergency department with acute asthma: A multicenter study. Pediatr. Pulmonol. 2007, 42, 646–655. [Google Scholar] [CrossRef] [PubMed]
  170. Chankaew, K.; Sinitkul, R.; Manuyakorn, W.; Roekworachai, K.; Kamalaporn, H. Spatial Estimation of PM(2.5) Exposure and its Association with Asthma Exacerbation: A Prospective Study in Thai Children. Ann. Glob. Health 2022, 88, 15. [Google Scholar] [CrossRef] [PubMed]
  171. Brandt, S.J.; Perez, L.; Kunzli, N.; Lurmann, F.; McConnell, R. Costs of childhood asthma due to traffic-related pollution in two California communities. Eur. Respir. J. 2012, 40, 363–370. [Google Scholar] [CrossRef]
  172. Stevens, E.L.; Rosser, F.; Han, Y.Y.; Forno, E.; Acosta-Perez, E.; Canino, G.; Celedon, J.C. Traffic-related Air Pollution, Dust Mite Allergen, and Childhood Asthma in Puerto Ricans. Am. J. Respir. Crit. Care Med. 2020, 202, 144–146. [Google Scholar] [CrossRef]
  173. Guarnieri, M.; Balmes, J.R. Outdoor air pollution and asthma. Lancet 2014, 383, 1581–1592. [Google Scholar] [CrossRef] [PubMed]
  174. Jayawardene, W.P.; Youssefagha, A.H.; Lohrmann, D.K.; El Afandi, G.S. Prediction of asthma exacerbations among children through integrating air pollution, upper atmosphere, and school health surveillances. Allergy Asthma Proc. 2013, 34, e1–e8. [Google Scholar] [CrossRef] [PubMed]
  175. Bousquet, J.; O’Hehir, R.E.; Anto, J.M.; D’Amato, G.; Mosges, R.; Hellings, P.W.; Van Eerd, M.; Sheikh, A. Assessment of thunderstorm-induced asthma using Google Trends. J. Allergy Clin. Immunol. 2017, 140, 891–893.e7. [Google Scholar] [CrossRef] [PubMed]
  176. Ram, S.; Zhang, W.; Williams, M.; Pengetnze, Y. Predicting asthma-related emergency department visits using big data. IEEE J. Biomed. Health Inform. 2015, 19, 1216–1223. [Google Scholar] [CrossRef] [PubMed]
  177. Hao, H.; Eckel, S.P.; Hosseini, A.; Van Vliet, E.D.S.; Dzubur, E.; Dunton, G.; Chang, S.Y.; Craig, K.; Rocchio, R.; Bastain, T.; et al. Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit. Int. J. Environ. Res. Public Health 2022, 19, 3578. [Google Scholar] [CrossRef] [PubMed]
  178. Maeda, S.; Kobayashi, S.; Takahashi, K.; Miyata, S. Association of comorbidities and medications with risk of asthma exacerbation in pediatric patients: A retrospective study using Japanese claims data. Sci. Rep. 2022, 12, 5509. [Google Scholar] [CrossRef] [PubMed]
  179. Navanandan, N.; Hatoun, J.; Celedon, J.C.; Liu, A.H. Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow. J. Allergy Clin. Immunol. Pract. 2021, 9, 2619–2626. [Google Scholar] [CrossRef]
  180. Andrenacci, B.; Ferrante, G.; Roberto, G.; Piacentini, G.; La Grutta, S.; Marseglia, G.L.; Licari, A. Challenges in uncontrolled asthma in pediatrics: Important considerations for the clinician. Expert. Rev. Clin. Immunol. 2022, 18, 807–821. [Google Scholar] [CrossRef]
  181. Kaplan, A.; Hardjojo, A.; Yu, S.; Price, D. Asthma Across Age: Insights From Primary Care. Front. Pediatr. 2019, 7, 162. [Google Scholar] [CrossRef]
  182. Huffaker, M.F.; Carchia, M.; Harris, B.U.; Kethman, W.C.; Murphy, T.E.; Sakarovitch, C.C.D.; Qin, F.; Cornfield, D.N. Passive Nocturnal Physiologic Monitoring Enables Early Detection of Exacerbations in Children with Asthma. A Proof-of-Concept Study. Am. J. Respir. Crit. Care Med. 2018, 198, 320–328. [Google Scholar] [CrossRef]
  183. Hatoun, J.; Correa, E.T.; MacGinnitie, A.J.; Gaffin, J.M.; Vernacchio, L. Development and Validation of the Asthma Exacerbation Risk Score Using Claims Data. Acad. Pediatr. 2022, 22, 47–54. [Google Scholar] [CrossRef]
  184. Harel-Sterling, M.; Dai, R.; Moraes, T.J.; Boutis, K.; Eiwegger, T.; Narang, I.; Lepine, C.; Brydges, M.G.; Dubeau, A.; Subbarao, P.; et al. Test for respiratory and asthma control in preschool kids in the emergency department as a predictor of wheezing exacerbations. Pediatr. Pulmonol. 2020, 55, 338–345. [Google Scholar] [CrossRef] [PubMed]
  185. Honkoop, P.J.; Simpson, A.; Bonini, M.; Snoeck-Stroband, J.B.; Meah, S.; Fan Chung, K.; Usmani, O.S.; Fowler, S.; Sont, J.K. MyAirCoach: The use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study. BMJ Open 2017, 7, e013935. [Google Scholar] [CrossRef] [PubMed]
  186. Khusial, R.J.; Honkoop, P.J.; Usmani, O.; Soares, M.; Simpson, A.; Biddiscombe, M.; Meah, S.; Bonini, M.; Lalas, A.; Polychronidou, E.; et al. Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma. J. Allergy Clin. Immunol. Pract. 2020, 8, 1972–1979.e8. [Google Scholar] [CrossRef] [PubMed]
  187. Bui, A.A.T.; Hosseini, A.; Rocchio, R.; Jacobs, N.; Ross, M.K.; Okelo, S.; Lurmann, F.; Eckel, S.; Dzubur, E.; Dunton, G.; et al. Biomedical REAl-Time Health Evaluation (BREATHE): Toward an mHealth informatics platform. JAMIA Open 2020, 3, 190–200. [Google Scholar] [CrossRef] [PubMed]
  188. Finkelstein, J.; Jeong, I.C. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann. N. Y. Acad. Sci. 2017, 1387, 153–165. [Google Scholar] [CrossRef] [PubMed]
  189. Alharbi, E.T.; Nadeem, F.; Cherif, A. Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: A systematic review. BMC Med. Inform. Decis. Mak. 2021, 21, 345. [Google Scholar] [CrossRef] [PubMed]
Jpm 14 00020 g001
Figure 1. General approach for the prediction of asthma exacerbations in children.
Figure 1. General approach for the prediction of asthma exacerbations in children.
Jpm 14 00020 g001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sarikloglou, E.; Fouzas, S.; Paraskakis, E. Prediction of Asthma Exacerbations in Children. J. Pers. Med. 2024, 14, 20. https://doi.org/10.3390/jpm14010020

AMA Style

Sarikloglou E, Fouzas S, Paraskakis E. Prediction of Asthma Exacerbations in Children. Journal of Personalized Medicine. 2024; 14(1):20. https://doi.org/10.3390/jpm14010020

Chicago/Turabian Style

Sarikloglou, Evangelia, Sotirios Fouzas, and Emmanouil Paraskakis. 2024. "Prediction of Asthma Exacerbations in Children" Journal of Personalized Medicine 14, no. 1: 20. https://doi.org/10.3390/jpm14010020

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop