Diagnosis Of Asthma In Children Research Articles

Impulse oscillometry in the diagnosis of cough variant asthma in children

BackgroundCough variant asthma (CVA) is one of the most common causes of chronic cough in children worldwide. The diagnosis of CVA in children remains challenging. This study aimed to assess the diagnostic utility of impulse oscillometry (IOS) pulmonary function in children with CVA.MethodsThis study included children aged 4 to 12 years diagnosed with CVA who underwent IOS pulmonary function and bronchodilation (BD) tests. A control group of healthy children was matched. Pre- and post-BD IOS parameters were recorded and presented as mean ± standard deviation or median. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the discriminatory potential of the IOS parameters for diagnosing CVA.ResultsA total of 180 patients with CVA and 65 control subjects were included. The baseline IOS parameters in the CVA group, except X5%pred, were significantly greater compared to the control group. After inhalation of salbutamol sulfate, all IOS parameters improved significantly in the CVA group. However, Z5%pred, R5%pred, and R20%pred remained greater in the CVA group compared to the control group. The improvement rates of IOS parameters in the CVA group significantly surpassed those in the control group. The ROC curve results for pre-BD IOS parameters and the improvement rate during the BD test showed that the combinations of pre-Z5%pred+△Z5% and pre-R5%pred+△R5% achieved the highest AUC value of 0.920 and 0.898, respectively. The AUC values of these combined parameters surpassed those of individual ones.ConclusionsThis study highlights that children with CVA exhibit greater IOS parameters compared to healthy children. The changes in IOS parameters during the BD test provided valuable diagnostic information for CVA, and the combination of various parameters can help pediatricians accurately identify CVA in children.

COVID-19 and Asthma Onset in Children.

Respiratory viral infections increase risk of asthma in infants and children. Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus can cause severe lung inflammation and prolonged respiratory symptoms. We sought to determine whether SARS-CoV-2 infection modified pediatric incident asthma risk. This retrospective cohort study examined children ages 1 to 16 within the Children's Hospital of Philadelphia Care Network who received polymerase chain reaction (PCR) testing for SARS-CoV-2 between March 1, 2020 and February 28, 2021. Multivariable Cox regression models assessed the hazard ratio of new asthma diagnosis between SARS-CoV-2 PCR positive and SARS-CoV-2 PCR negative groups within an 18-month observation window. Models were adjusted for demographic characteristics, socioeconomic variables, and atopic comorbidities. There were 27 423 subjects included in the study. In adjusted analyses, SARS-CoV-2 PCR positivity had no significant effect on the hazard of new asthma diagnosis (hazard ratio [HR]: 0.96; P = .79). Black race (HR: 1.49; P = .004), food allergies (HR: 1.26; P = .025), and allergic rhinitis (HR: 2.30; P < .001) significantly increased the hazard of new asthma diagnosis. Preterm birth (HR: 1.48; P = .005) and BMI (HR: 1.13; P < .001) significantly increased the hazard of new asthma diagnosis for children <5 years old. SARS-CoV-2 PCR positivity was not associated with new asthma diagnosis in children within the observation period, although known risk factors for pediatric asthma were confirmed. This study informs the prognosis and care of children with a history of SARS-CoV-2 infection.

Serum proteomics identifies novel diagnostic biomarkers for asthma in preschool children.

Asthma is characterized by airway hyperresponsiveness, reversible airway obstruction, and chronic airway inflammation. It is the most common chronic disease in childhood. However, the diagnosis of childhood asthma remains challenging, and there is an urgent need to develop new diagnostic methods. To identify biomarkers of asthma in children, we adopted the Orbitrap-based data-independent acquisition (DIA) mass spectrometry proteomics method to analyze the serum proteomic signatures of children with acute asthma and convalescent children. We identified 747 proteins in 46 serum samples and 50 differentially expressed proteins (DEPs) that distinguished between asthmatic and healthy children. Next, functional enrichment analysis of the DEPs was conducted, it was indicated that the DEPs were significantly enriched in immune-related and function terms and pathways. Furthermore, we performed statistical analysis and identified MMP14, ABHD12B, PCYOX1, LTBP1, CFHR4, APOA1, IGHG4, ANG and IGFALS proteins as the diagnostic biomarker candidates. Ultimately, a promising asthma diagnostic model for preschool children based on IGFALS was built and evaluated. The area under the curve (AUC) of the IGFALS model was 0.959. In this study, the DIA proteome strategy was used and the largest number of proteins of asthmatic children serum proteomics was identified. The proteomics results showed that the DEPs play the central role of the inflammation-immune mechanism in asthma pathogenesis, suggesting that these proteins may be used in asthma diagnosis, prognosis, or therapy, and suggested biomarkers for asthma of preschool children. In conclusion, our results provide insight into the pathophysiology of asthma. We believe that the diagnostic model will facilitate clinical decision-making regarding asthma in preschool children.

Maternal obesity and childhood asthma risk: Exploring mediating pathways.

Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways. To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk. We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system. Children were followed from their birth (2005-2014) until at least age 4 (n = 95,723), age 6 (n = 59,230) or age 8 (n = 25,261). Childhood asthma diagnosis at each age was determined using ICD-9/10 codes and medication dispensings. Prepregnancy BMI (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese [≥30] kg/m2 ) were defined using height and weight measurements close to the last menstrual period date. Child's BMI (Centers for Disease Control and Prevention BMI-for-age percentiles: underweight [<5th], normal [5th-85th], overweight [85th-95th], obese [>95th]) were obtained using anthropometric measurements taken the year preceding each follow-up age. GWG (delivery weight-prepregnancy weight) was categorised based on Institutes of Medicine recommendations (inadequate, adequate, excessive). Implementing first causal inference test (CIT) then causal mediator models (to decompose the natural direct and indirect effects), we examined the potential mediating effect of childhood BMI, GWG, and preterm birth on the association between prepregnancy BMI (continuous and categorical) and childhood asthma. Overall, risk of childhood asthma increased as prepregnancy BMI increased (age 4 risk ratio: 1.07, 95% confidence interval: 1.04, 1.09, per 5 kg/m2 increase in BMI; similar for age 6 and 8). CIT identified childhood BMI and preterm birth, but not GWG as potential mediators. Causal mediation models confirmed childhood BMI, but not preterm birth, as having a partial mediating effect. Results were similar for age 6 and 8, and when continuous mediators (instead of binary) were assessed. Childhood overweight/obesity has a modest mediating effect on the association between prepregnancy BMI and childhood asthma.

Acute Bronchiolitis and Other Predictors for Early Diagnosis of Childhood Asthma

BACKGROUND: Early onset of acute bronchiolitis is associated with early onset of childhood asthma. Unfortunately, childhood asthma tends to be frequently overlooked and not optimally managed. Family pediatricians, however, possess an exceptional opportunity to recognize the connection of these conditions and promptly identify, address, and oversee children with asthma. It is important to note that childhood asthma typically begins in the early years of life, often triggered by viral infections. AIM: We aimed to analyze the early predictors linked to childhood asthma, such as previous episodes of acute bronchiolitis and the presence of atopy, to facilitate the early identification of children with childhood asthma within primary care settings. MATERIALS AND METHODS: Retrospectively analyzed data from 1652 children from health records at the Primary Health Care Polyclinic - Pediatric Primary Health Care Practice -“Vitalino” in Veles, North Macedonia. Within this cohort, 108 children had experienced acute bronchiolitis in 2016, at an age range of 0–2 years. In 2020 and 2023, the data were analyzed to determine the existence of a connection between acute bronchiolitis and other predictors with the early onset of childhood asthma. RESULTS: This retrospective research showed that from the total number of children, 108 children aged up to 2 years suffered from acute bronchiolitis, of which the analysis in 2020 showed that 50 children had atopy, 19 allergic rhinitis, 33 children had recurrent laryngitis, 24 children recurrent otitis, 68 children with recurrent bronchial obstructions (≤3), and 39 children developed early onset of bronchial asthma. The analysis in 2023 showed that 51 children have asthma. Based on the obtained results, the prevalence of acute bronchiolitis in the Republic of North Macedonia is 6.54%. If acute bronchiolitis is considered an early manifestation of childhood asthma, then the prevalence of childhood asthma in the Republic of North Macedonia is about 6.54%. CONCLUSION: Based on the obtained results, we can conclude that early onset of acute bronchiolitis is an early manifestation of childhood asthma. Additional predictors are early signs of atopy, frequent viral infections with broncho-obstruction, allergic rhinitis, and recurrent laryngitis.

Spirometry in the diagnosis of cough variant asthma in children.

This study aimed to assess the diagnostic utility of spirometry, particularly focusing on small airway parameters, in children with cough variant asthma (CVA). This study included children aged 5-12 years with a diagnosis of CVA. Pre- and postbronchodilation spirometry parameters, including FEV1 %pred, FVC%pred, FEV1 /FVC%pred, PEF%pred, FEF25 %pred, FEF50 %pred, FEF75 %pred, MMEF%pred, were recorded. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was calculated to assess the discriminatory potential of these spirometry parameters for CVA. A prediction model based on logistic regression (LR) was performed. A total of 200 patients with CVA and 73 control subjects were included. Baseline spirometry parameters in the CVA group, except for FVC%pred, were significantly lower compared to the control group. After inhalation of salbutamol sulfate, all parameters showed significant improvement in the CVA group. However, these parameters, except for FEV1 %pred and FVC%pred, remained lower in the CVA group compared to the control group. The improvement rate of each parameter in the CVA group, except for ∆ FVC%, was significantly higher than that in the control group. △ MMEF% achieved the highest AUC of 0.797 with a threshold value of 16.09%, followed by △ FEF75 % (0.792), △ FEV1 % (0.756), and △ FEF50 % (0.747) with threshold values of 19.01%, 4.48%, and 19.4%, respectively. The clinical prediction model included four variables (age, △ FEF25 %, △ FEF75 %, and △ MMEF%) and demonstrated excellent performance distinguishing patients with and without CVA (AUC = 0.850). In the CVA group, the △ FEV1 % showed a positive correlation with small airway parameters. This study highlights that children with CVA exhibit lower pulmonary function parameters compared to healthy children. Changes in small airway parameters during bronchodilator tests can be valuable in diagnosing CVA, and the LR prediction model incorporating age and several pulmonary parameters can assist physicians in accurately identifying CVA in clinical practice.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care.

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

The quality of paediatric asthma guidelines: evidence underpinning diagnostic test recommendations from a meta-epidemiological study.

Asthma is one of the most frequent reasons children visit a general practitioner (GP). The diagnosis of childhood asthma is challenging, and a variety of diagnostic tests for asthma exist. GPs may refer to clinical practice guidelines when deciding which tests, if any, are appropriate, but the quality of these guidelines is unknown. To determine (i) the methodological quality and reporting of paediatric guidelines for the diagnosis of childhood asthma in primary care, and (ii) the strength of evidence supporting diagnostic test recommendations. Meta-epidemiological study of English-language guidelines from the United Kingdom and other high-income countries with comparable primary care systems including diagnostic testing recommendations for childhood asthma in primary care. The AGREE-II tool was used to assess the quality and reporting of the guidelines. The quality of the evidence was assessed using GRADE. Eleven guidelines met the eligibility criteria. The methodology and reporting quality varied across the AGREE II domains (median score 4.5 out of 7, range 2-6). The quality of evidence supporting diagnostic recommendations was generally of very low quality. All guidelines recommended the use of spirometry and reversibility testing for children aged ≥5 years, however, the recommended spirometry thresholds for diagnosis differed across guidelines. There were disagreements in testing recommendations for 3 of the 7 included tests. The variable quality of guidelines, lack of good quality evidence, and inconsistent recommendations for diagnostic tests may contribute to poor clinician adherence to guidelines and variation in testing for diagnosing childhood asthma.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged &lt;25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged &lt;25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Bacterial Microbiota of Asthmatic Children and Preschool Wheezers’ Airways—What Do We Know?

Asthma is the most chronic pulmonary disease in pediatric population, and its etiopathology still remains unclear. Both viruses and bacteria are suspected factors of disease development and are responsible for its exacerbation. Since the launch of The Human Microbiome Project, there has been an explosion of research on microbiota and its connection with various diseases. In our review, we have collected recent data about both upper- and lower-airway bacterial microbiota of asthmatic children. We have also included studies regarding preschool wheezers, since asthma diagnosis in children under 5 years of age remains challenging due to the lack of an objective tool. This paper indicates the need for further studies of microbiome and asthma, as in today’s knowledge, there is no particular bacterium that discriminates the asthmatics from the healthy peers and can be used as a potential biological factor in the disease prevalence and treatment.

Bronchodilator Response in FEF25-75 for the Diagnosis of Asthma in Children.

Spirometry tests with a bronchodilator response (BDR) in FEV1, a methacholine concentration that produces a 20% drop in FEV1 (PC20) ≤ 2 mg/mL, and a positive exercise test have high specificity for the diagnosis of asthma in children. However, the value of forced expiratory flow during the middle half of the FVC maneuver (FEF25-75) in spirometry has been questioned. The objective of this study was to relate the BDR in FEF25-75 of spirometry tests with normal FEV1 and FEV1/FVC to airway hyper-responsiveness (AHR) to methacholine or exercise in children age 5-15 y with clinical suspicion of asthma. This was a cross-sectional study of spirometry tests performed between January 2017-December 2019 in children age 5-15 y with diagnostic suspicion of asthma who had a methacholine and/or exercise testing within a period not exceeding 60 d between exams. The mean (± SD) age of the children was 9.04 ± 2.67 y, with a range of 5-15 y, and 56.17% were male. Of the 324 spirometry tests with normal FEV1 and FEV1/FVC, 66 (20.4%) tests showed BDR in FEF25-75. A total of 46.9% and 33.3% of the children with and without BDR in FEF25-75, respectively, had a PC20 value ≤ 2 mg/mL and/or a positive exercise testing (P = .039). Children with suspected asthma and normal spirometry, other than BDR in FEF25-75, had greater AHR than those without BDR in FEF25-75. BDR in FEF25-75 was not always accompanied by AHR to confirm the diagnosis of asthma, so this study suggests that assessment of FEF25-75 alone is not always reliable for ruling in or ruling out AHR in the setting of otherwise normal spirometry results in children with suspected asthma.

Development and implementation in outpatient practice of an electronic computer program “Diagnostics of bronchial asthma in children”

BACKGROUND: Currently, the criteria for the differential diagnosis of bronchial asthma and several respiratory diseases in children, accompanied by bronchial obstruction syndrome, remain insufficiently effective. Moreover, timely administration of anti-inflammatory therapy is important.&#x0D; AIM: To develop and introduce a computer program for the timely diagnosis of asthma in children and appointment of basic therapy into outpatient practice.&#x0D; MATERIALS AND METHODS: A comparative comprehensive analysis of risk factors for disease development in 155 children with bronchial asthma and 155 clinically healthy children who suffered episodes of bronchial obstruction at an early age was conducted, and a computer program that allows timely diagnosis of asthma was developed. The program was tested during the observation of 68 children with episodes of bronchial obstruction.&#x0D; RESULTS: Sixty-two most significant signs (disease predictors) were established, which formed the basis of the computer program Diagnostics of bronchial asthma in children. The application of the program in clinical conditions showed a high consistency of the result with the conclusion of an allergist-immunologist. The sensitivity and specificity of the method were 88.2% and 94.1%, respectively (p 0.05). The program was introduced into outpatient practice.&#x0D; CONCLUSION: The data obtained determine the possibility of using the proposed computer program as a tool for the timely diagnosis of bronchial asthma in children.

Neutrophil-lymphocyte ratios in blood to distinguish children with asthma exacerbation from healthy subjects.

Airway inflammation is a prominent feature of asthma and may play an important role in disease pathophysiology. Despite the increasing incidence of asthma worldwide, reliable diagnostic biomarkers are lacking and widely lead to asthma misdiagnosis. Neutrophil-lymphocyte ratio (NLR) is a biomarker of systemic inflammation, in addition to NLR-alanine aminotransferase ratio (NAR) and NLR-albumin ratio (NBR). The aim of this study was to evaluate associations of NLR, NAR, and NBR with diagnosis of childhood asthma to determine if they can aid clinical childhood asthma diagnosis. This retrospective case-control study included 89 children with asthma and 53 healthy children from the Wuxi Children's Hospital affiliated with Nanjing Medical University. We applied various statistical tests to the dataset: Mann-Whitney U test to compare characteristics of the case and control groups; chi-squared test to compare categorical variables; Kruskal-Wallis test to compare statistical differences of asthma indicators among groups; receiver operating characteristic (ROC) curves to assess the diagnostic value of indices; and Spearman correlation analysis to evaluate relationships between NLR and lactate dehydrogenase, albumin, aspartate transaminase, and alanine transaminase levels. Compared with controls, the asthma case group had significantly higher white blood cell (p < 0.01), neutrophil, lactate dehydrogenase, C-reactive protein, and NLR levels (p < 0.01) and significantly lower lymphocyte (p = 0.001), platelet (p = 0.039), and albumin levels (p = 0.04). We determined optimal cutoff levels for several metrics: 1.723 for NLR, with sensitivity of 0.73 and specificity of 0.906; 0.135 for NAR, with sensitivity of 0.685 and specificity of 0.887; and 0.045 for NBR, with sensitivity of 0.674 and specificity of 0.906. The areas under the curve (AUCs) were 0.824 for NLR, 0.788 for NAR, 0.818 for NBR, and 0.83 for the combination of NLR + NAR + NBR. The combination of NLR, NAR, and NBR biomarkers distinguished asthmatic ones suffering from exacerbation of the condition from healthy children. Thus, our results indicate NLR + NAR + NBR could be used as a clinical biomarker for asthma in children.

How Do Pediatricians Diagnose Asthma in Tertiary Care Hospitals?

There is a lot of disparity in the guidelines and the practice of pediatricians globally for diagnosing asthma in children. To find out if pediatricians are diagnosing asthma in children according to best standard practices. A cross-sectional study was conducted at tertiary care hospitals' emergency and outpatient departments (OPDs). All the parents accompanying the asthmatic children to the emergency or outpatient departments of the tertiary care hospitals were asked questions regarding the diagnosis of their children's asthma on a prescribed performa. This performa had all the components of the best standard practices for the diagnosis of asthma in children. The data were entered into SPSS version 27 (SPSS Inc., Chicago, IL) and analyzed. Among the 234 children, the diagnosis of asthma was based on only one component out of three, i.e., recurrence (100%) of symptoms or signs. The objective measurement of the second component, i.e., reversibility with a peak flow meter (PFM) or spirometry, was assessed in only 6% of children. The third component, i.e., the presence of inflammation, was not assessed at all (0.0%). The diagnosis of asthma in children lacks precision. This is far from the evidence-based best standard practices. There is a need to provide motivation, training, and equipment to the staff.

Cytokine profile in childhood asthma

Childhood asthma is a chronic airway disease, which pathogenesis is markedly heterogeneous–with multiple phenotypes defining visible characteristics and endotypes defining molecular mechanisms. Cytokines and chemokines released during inflammatory responses are key immune mediators. The cytokine response can largely determine the susceptibility to childhood asthma and its severity. The purpose of this study was to characterize the immune profile of childhood asthma. The study involved 26 children (3–18 years old), who were divided into 2 groups: study–with childhood asthma; control–without asthma. The innovative Bio-Plex method was used to determine the serum concentration of 37 inflammatory proteins in one experiment. The results were analyzed using univariate statistical tests. In the study group, the level of the 10 tested markers increased, while the level of the remaining 9 decreased compared to the control; a statistically significant reduction in concentration was obtained only for the MMP-1(p&lt;0.05). According to the ROC curve, MMP-1 can be considered an effective discriminator of childhood asthma (p&lt;0.05; AUC=0.752). Cytokines/chemokines may be useful in the diagnosis of childhood asthma and may also become a prognostic target in determining the phenotype/endotype of this condition. This study should be a prelude to and an incentive for more complex proteomic analyzes.

Identifying a novel serum microRNA biomarker panel for the diagnosis of childhood asthma.

The pathological mechanism of childhood asthma is complex, and timely diagnosis is the key to effective prevention and control of childhood asthma. We collected 170 serum samples from 95 children with asthma and 75 healthy children. Serum miRNA biomarkers were analyzed by Illumina sequencing for childhood asthma. Differentially serum miRNAs were confirmed with quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. The Illumina sequencing data showed the differential expression of 111 serum miRNAs among asthmatic and healthy children. After confirmation of miRNAs expression through qRT-PCR, four of them (namely hsa-miR-106a-5p, hsa-miR-18a-5p, hsa-miR-144-3p, and hsa-miR-375) manifested significant differential expression between asthmatic children and healthy controls. The biomarkers classification tree model created with these four miRNAs using the Biomarker Patterns Software could effectively separate childhood asthma and healthy children, with a specificity of 88.3%, a sensitivity of 95.0%, and an area under the curve (AUC) value of 0.942. The regulatory networks containing miRNAs and their gene targets suggested that the four miRNAs might have gene targets implicated in inflammation, immunity, and transcriptional efficiency. Taken together, this four-serum-miRNA panel is a promising biomarker to diagnose childhood asthma noninvasively.

Validation of screening tool for diagnosis of childhood asthma by exclusion method

Introduction: In the 21st century, with growing concerns over over-diagnosis of asthma in children; the diagnosis of asthma is left as diagnosis by exclusion. On the same lines, an attempt had been made to device a criteria to exclude diagnosis of asthma. The criteria are labeled as other than asthma (OTA) criteria. It is assumed that if a case does not satisfy OTA tool, then it is likely to be asthma. This study had objectives of validation of screening tool by expert and validation of OTA tool by direct application on patients. Materials and Methods: This was an observational and prospective study only. Total 120 cases were enrolled. Questionnaire applied on all cases and all of the 120 cases were followed up on day 30 with clinical diagnosis and were included in results estimation. Results: According to the screening tool, out of 120 cases, 83 cases (69.2%) were found to have OTA while 37 cases (30.8%) werefound to have non-OTA. However, among this study population, 94 cases (78.3%) had non-asthma and 26 cases (21.7%) had clinical diagnosis of asthma taken as gold standard. The association between clinical diagnosis and total score based diagnosis was found to bestatistically significant (p=1.96E-07). Sensitivity was 91.57% and specificity was 51.35% which helps detect OTA cases effectively. Predictive value of positive test is 80.85% and predictive value of negative test was 73.08%. OTA category has relative risk of 3.003 for diagnosis of non-asthma as compared to 0.262 for non-OTA category being almost 3 times in comparison to non-OTA category. Thus, OTA was consistent with non-asthma diagnosis. Conclusion: On direct application of patients in prospective study, this tool has proven its value as an effective screening tool. This was the first such study to diagnose asthma by exclusion, devising a criterion for other than asthma conditions.

Emerging Roles of Non-Coding RNAs in Childhood Asthma.

Asthma is a chronic airway inflammatory disease in children characterized by airway inflammation, airway hyperresponsiveness and airway remodeling. Childhood asthma is usually associated with allergy and atopy, unlike adult asthma, which is commonly associated with obesity, smoking, etc. The pathogenesis and diagnosis of childhood asthma also remains more challenging than adult asthma, such as many diseases showing similar symptoms may coexist and be confused with asthma. In terms of the treatment, although most childhood asthma can potentially be self-managed and controlled with drugs, approximately 5–10% of children suffer from severe uncontrolled asthma, which carries significant health and socioeconomic burdens. Therefore, it is necessary to explore the pathogenesis of childhood asthma from a new perspective. Studies have revealed that non-coding RNAs (ncRNAs) are involved in the regulation of respiratory diseases. In addition, altered expression of ncRNAs in blood, and in condensate of sputum or exhalation affects the progression of asthma via regulating immune response. In this review, we outline the regulation and pathogenesis of asthma and summarize the role of ncRNAs in childhood asthma. We also hold promise that ncRNAs may be used for the development of biomarkers and support a new therapeutic strategy for childhood asthma.

Serum Eosinophilic Cationic Protein Is a Reliable Biomarker for Childhood Asthma

Background: Eosinophilic cationic protein (ECP) is associated with airway inflammation and asthma. However, the clinical value of measuring ECP in childhood asthma is not fully known. We aimed to study the diagnostic performance of serum ECP and other common asthma biomarkers, individually and in combinations. Methods: In a cross-sectional study, 5–16-year-old children with current asthma (CA) (n = 37), transient asthma (TA) (n = 43), (previous history of wheezing/asthma), and healthy children (HC) (n = 86) were investigated for ECP, blood eosinophil count (B-Eos), fractional exhaled nitric oxide (FeNO), and lung function, i.e., spirometry (forced expiratory volume during the first second [FEV1]/forced vital capacity [FVC] ratio). Results: Both ECP and B-Eos were higher in CA compared to TA (p < 0.01) and HC (p < 0.0001). ECP and B-Eos were also higher in TA compared to HC (p < 0.05 and p < 0.001, respectively). FeNO was higher in CA (p < 0.0001) and TA (p < 0.01) compared to HC but similar between the asthma groups. The FEV1/FVC ratio was lower in CA compared to TA and HC (both p < 0.01) but similar between TA and HC. The best diagnostic performance regarding CA was found for ECP and B-Eos with receiver operating characteristics area under curve (AUC) of 0.801 and 0.810, respectively. The optimal cutoff for ECP (29 μg/L) yielded a sensitivity and specificity of 70.3% and 81.4%. The corresponding AUCs for FeNO and FEV1/FVC were 0.732 and 0.670, respectively. ECP and B-Eos showed the highest AUCs (0.669 and 0.673) for differentiation between CA and TA. Combining ECP with FeNO and FEV1/FVC increased the odds ratio (OR) for having CA from OR 3.97–10.3 for the single biomarkers to OR 20.2 (95% confidence interval: 5.76–68.6). Conclusion: Our results show that serum ECP is a reliable biomarker in the diagnosis of childhood asthma, with additional value in combination with FeNO and FEV1/FVC, and that ECP can be an alternative to B-Eos.

Value of fractional exhaled nitric oxide combined with impulse oscillometry in the diagnosis of asthma in preschool children

To evaluate the value of fractional exhaled nitric oxide (FeNO) combined with impulse oscillometry (IOS) in the diagnosis of asthma in preschool children, and to establish the optimal predictive model. A retrospective analysis was performed on 156 children with wheezing, aged 3-5 years, who were admitted from September 2019 to December 2020. These children were divided into an asthma group with 52 children and a non-asthma group with 104 children. The two groups were compared in terms of IOS parameters, FeNO, and clinical data. The multivariate logistic regression analysis was used to establish the optimal predictive model. Compared with the non-asthma group, the asthma group had significantly higher total respiratory system impedance at 5 Hz (Z5), resistance of respiratory system at 5 Hz and 20 Hz (R5 and R20, respectively), resonance frequency, reactance area (AX), and FeNO and a significantly lower reactance difference at 5 Hz (P<0.05). The receiver operating characteristic (ROC) curve analysis showed that Z5, R5, R20, and FeNO had a certain value in the diagnosis of asthma (P<0.05). The multivariate logistic regression analysis established the optimal predictive model of R20+AX+FeNO, with an area under the ROC curve of 0.858 (P<0.05), a sensitivity of 78.8%, and a specificity of 76.9%. FeNO combined with IOS is helpful for the diagnosis of asthma in preschool children, and the model of R20+AX+FeNO has a certain value in the diagnosis of asthma in these children.