The association between waist circumference and adult asthma attack using nationally representative samples.

AIMS: This study aims to explore the relationship between waist circumference and asthma attack in adults. METHODS: In this cross-sectional study, we analysed data from 5,530 U.S. adults diagnosed with asthma. Participants were categorized into two groups based on their experience of asthma attacks: with or without asthma attacks. We employed adjusted weighted logistic regression models, weighted restricted cubic splines, subgroup and sensitivity analyses to assess the association between waist circumference and asthma attack. RESULTS: The median age of all participants was 43 years, and the median waist circumference was 98.9 cm, with a median BMI was 28.50 kg/m2. Participants in the asthma attack group had significantly higher waist circumferences than those in the non-attack group (P < 0.001). After full adjustment for body mass index-defined obesity, age, gender, race, education levels, poverty income ratio levels, smoking status, and metabolic syndrome, every 5 cm increase in waist circumference exhibited a 1.06 times higher likelihood of asthma attack probability. The weighted restricted cubic spline analysis demonstrated an increased risk of asthma attacks with rising waist circumference. Subgroup analyses confirmed this relationship across various groups differentiated by gender, age, and smoking status. When applying a stricter definition of asthma attack, the weighted logistic regression models showed robust association between waist circumference and asthma attack. CONCLUSION: Waist circumference is an independent predictor of asthma attacks. Our findings underscore the importance of waist circumference measurement in evaluating the risk of asthma attacks.

The Effect of Oral Montelukast in Controlling Asthma Attacks in Children: A Randomized Double-blind Placebo Control Study.

Oral Montelukast is recommended as maintenance therapy for persistent asthma, but there is controversy regarding its effectiveness in controlling asthma attacks. The present study was conducted to investigate the clinical efficacy of oral Montelukast for asthma attacks in children. This study was conducted as a double-blind placebo-controlled clinical trial on 80 children aged 1-14 years with asthma who were admitted to the emergency department of Bahrami Children's Hospital (Tehran, Iran) during one year. Patients were randomly divided into case and control groups. In addition to the standard asthma attack treatment, Montelukast was prescribed in the case group and placebo in the control group for one week. Patients were evaluated in terms of asthma attack severity score and oxygen saturation percentage (SpO2) in room air as primary outcomes 1, 4, 8, 24 and 48 hours after admission. In the first 48 hours, there was no significant difference in the score of asthma attack severity and SpO2 between the case and control groups. There was no significant difference between the groups in terms of length of hospitalization or number of admissions to the intensive care unit. None of the patients were re-hospitalized after discharge. The results of this study showed that the use of Montelukast along with the standard treatment of asthma attacks in children has no added benefit.

The relationship between PM2.5 and the onset and exacerbation of childhood asthma: a short communication.

Much is known about the link between air pollution and asthma in adults, particularly fine particulate matter (PM2.5). Studies have found that certain levels of fine PM2.5 can increase airway responsiveness and worsen asthma. PM2.5 may play a role in the onset and exacerbation of childhood asthma. However, there is little in the literature on how PM2.5 affects asthma attacks and exacerbations in children. Asthma is a common chronic disease in children, and air pollution can aggravate it. The effect of PM2.5 on childhood asthma needs further research. By evaluating, reviewing, and collating existing results in this area, this paper aims to explore the relationship between PM2.5 and asthma onset and exacerbation in children.

Association between asthma and headache: Findings from the NHANES 2001-2004.

INTRODUCTION: With the adjustment of sociodemographic factors, our study aimed to explore the association between asthma control and headache using a representative sample in the United States. METHODS: A total of participants aged >20 years from the National Health and Nutrition Examination Survey (NHANES) cycles 2001-2004 were included. The presence of asthma and headache was determined by questionnaires. Multivariate logistic regression was performed. RESULTS: Participants with asthma had higher odds of suffering headaches (odds ratio = 1.62, 95% confidence interval: 1.30-2.02, p < 0.001). Those who had an asthma attack in the past year had higher odds of experiencing headaches than those who did not (odds ratio = 1.94, 95% confidence interval: 1.11-3.39, p = 0.022). No statistically significant association was found between participants who had emergency care visit for asthma in the past year and those who had not. CONCLUSION: Patients with asthma attack in the past year were more likely to have a headache than those who without.

Recovery of Breakthrough Asthma Attacks Treated With Oral Steroids While on Monoclonal Antibody Therapy: Protocol for a Prospective Observational Study (BOOST).

BACKGROUND: Asthma attacks are a common and important problem. Someone experiences an asthma attack in the United Kingdom every 10 seconds. Asthma attacks cause coughing, wheezing, breathlessness, and chest tightness and are highly stressful for patients. They result in reduced quality of life, with days lost from work or school. Asthma attacks are treated with oral corticosteroids (OCSs), but these have many short- and long-term side effects. Asthma monoclonal antibodies (mAbs) have revolutionized the treatment of severe asthma by reducing asthma attacks and OCS burden by over 50%, but some people still experience attacks while on mAbs. The MEX study showed that residual asthma attacks are broadly eosinophilic (high fractional exhaled nitric oxide [FeNO]) or noneosinophilic (low FeNO), but it did not measure response to OCS treatment. There is an evidence gap in understanding the clinical and inflammatory responses that occur when using OCSs to treat residual asthma attacks in patients taking asthma mAbs. OBJECTIVE: The primary objective is to compare the clinical recovery between high-FeNO and low-FeNO attacks after acute treatment with oral prednisolone among people established on long-term asthma mAb treatment. The exploratory objective is to compare the inflammatory response to acute treatment with oral prednisolone between high-FeNO and low-FeNO attacks. METHODS: BOOST (Breakthrough Asthma Attacks Treated With Oral Steroids) is a single-center, prospective observational study of 60 adults established on long-term asthma mAb treatment who receive acute treatment with oral prednisolone (usual care) for an asthma attack. The primary outcome will be the proportion of treatment failure (the need to start oral prednisolone or antibiotics or an unscheduled health care visit for asthma, following an attack) at day 28. The secondary outcomes will be the change in forced expiratory volume in 1 second and the change in visual analogue scale symptom score between the stable state, attack, day 7, and day 28 visits. The exploratory outcomes include the changes in sputum, nasal, and blood inflammometry between the stable state, attack, day 7, and day 28 visits. RESULTS: The last asthma attack visit is anticipated to occur in December 2023. Data analysis and publication will take place in 2024. CONCLUSIONS: We will test the hypothesis that there is a difference in the rate of recovery of clinical and inflammatory measures between high-FeNO and low-FeNO asthma attacks that occur in patients on mAb therapy. The study data will help power a future randomized placebo-controlled trial of prednisolone treatment for nonsevere attacks in patients treated with asthma mAbs and will provide important information on whether corticosteroid treatment should be FeNO-directed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46741.

Monitoring the Resolution of Acute Exacerbation of Airway Bronchoconstriction in an Asthma Attack Using Capnogram Waveforms.

Patients with acute bronchospasm can show a distinct slope of the capnogram ("shark fin") as a result of asynchronous alveolar excretion. Although the slope of the upward alveolar plateau (phase III) in the capnogram waveforms of non-intubated patients is known to help monitor the therapeutic response to acute bronchospasm, little is known about the significance of its slope among intubated patients. Therefore, we quantified the phase III slope of an intubated patient with acute asthma to investigate whether capnogram waveforms could be useful for identifying the response to antibronchospasm treatment in real time. CASE SUMMARY: The patient was a 53-year-old man who had a history of asthma. He presented to the emergency department with the primary complaint of respiratory distress. He was diagnosed with severe asthma attack and required invasive mechanical ventilation for 10 days, during which we quantified the phase III slope of the capnogram. The phase III slope decreased during treatment, with a significant reduction from the third to the fourth day; however, a significant decrease in end-tidal carbon dioxide (EtCO2) was observed from the fifth to the sixth day. We found that the slope values decreased earlier than EtCO2 reduction, although the absolute EtCO2 values eventually decreased in response to antibronchospasm treatment. CONCLUSION: There were several reports that evaluated the phase III slope in non-intubated patients with asthma, but this is the first report measuring the phase III slope in an intubated patient over several days. Capnogram waveforms may serve as useful real-time indicators to monitor acute bronchospasm among mechanically ventilated patients.

Assessing asthma self-management education among US children with current asthma, Asthma Call-back Survey (ACBS) 2015-2017.

OBJECTIVE: Asthma self-management education (AS-ME) is an effective strategy to help children with asthma achieve better asthma control and outcome. The objective of this study is to assess the association between the prevalence of receiving AS-ME curriculum components and sociodemographic characteristics among children with current asthma. METHODS: Behavioral Risk Factor Surveillance System, child Asthma Call-back Survey 2015-2017 aggregated data were used. Multivariable logistic regression models were used to assess associations of each AS-ME component question and sociodemographic characteristic, adjusting for sample weighting. RESULTS: Among 3,213 children with current asthma, 52% of children reported ever being given an asthma action plan by a doctor or other healthcare professional. After adjusting for other variables, boys and Non-Hispanic Black children were more likely to report being given an action plan (APR= 1.15[95% CI 1.00-1.32] and APR= 1.28[95% CI 1.07-1.54] respectively). Non-Hispanic Black (APR = 2.15 [95% CI 1.30-3.55]), non-Hispanic, other race (APR = 1.95 [95% CI1.04-3.66]), and Hispanic children (APR = 1.84 [95% CI 1.18-2.89]) were more likely to report taking a course to learn how to manage asthma than non-Hispanic White children. Hispanic children (40.8%) were more likely to report being advised to change home environment compared to non-Hispanic Whites (31.5%) (APR =1.28 [95% CI 1.01-1.63). CONCLUSION: The prevalence of some elements of asthma-self management education was relatively low and there were differences observed in the prevalence of receiving AS-ME by race/ethnicity, parental education, and income. Targeted implementation of asthma self-management components and interventions may improve asthma control and reduce asthma morbidity.

The central glial cell line-derived neurotrophic factor (GDNF) regulates pulmonary function in asthmatic rats.

Background: Asthma is a common chronic inflammatory disease of the airway, but the mechanism is still not fully understood. This study aimed to investigate the effect of glial cell line-derived neurotrophic factor (GDNF) on asthma attacks. Methods: An asthmatic rat model was established. GDNF expression in the airway and brain was observed by immunohistochemistry (IHC), and the concentration of GDNF in bronchoalveolar lavage fluid (BALF) was detected by enzyme-linked immunosorbent assay (ELISA). After injection of GDNF and its antibody into the lateral ventricle of asthmatic rats, the pulmonary function was recorded, and the levels of interferon-γ (IFN-γ) and interleukin-4 (IL-4) in BALF were tested. Results: GDNF expressions were increased significantly in the lung tissues of asthmatic rats. In the central nervous system (CNS), GDNF-positive immunoreactive substances were observed in multiple brain regions, including the medial amygdala (MeA), paraventricular nucleus (PVN), cortex, and nucleus of solitary tract (NTS). After injection of GDNF into the lateral ventricles of asthmatic rats, the symptoms of asthma and airway inflammation were significantly aggravated, which could be improved by injection of GDNF antibody into the lateral ventricles. Conclusions: GDNF expression is increased in the lung and brain in asthmatic rats. During an asthma attack, the increased GDNF expressions in the rat brain remarkably aggravate the asthmatic symptoms.

Analysis of risk factors for acute attacks complicated by respiratory failure in children with asthma.

Objective: To describe the proportion and clinical characteristics of hospitalized children with acute asthma attacks complicated by respiratory failure and to analyze the risk factors. Methods: This retrospective study analyzed hospital admissions of children and adolescents with acute asthma attacks between January 2016 and December 2021. Inclusion criteria were used to identify eligible cases, and demographic information and disease characteristics were collected. Patients were categorized into respiratory failure group and the other group based on the result of artery blood gas analysis. Multivariate logistic regression was utilized to investigate the risk factors associated with respiratory failure resulting from acute asthma attacks. The data were analyzed using SPSS 22.0, and significance was considered at P < 0.05. Results: Our research involved 225 participants, with 18.7% diagnosed with respiratory failure. The respiratory failure group was found to be younger and have higher percentage of male, while birth weight, nationality, and type of residence did not differ between the two groups. In the respiratory failure group, a significant difference was observed in emergency hospitalization, ICU treatment, severe to critical attack, dyspnea and allergy history. The two groups did not differ in admission season, first asthma diagnosis, respiratory infection and comorbidity. The respiratory failure group exhibited a higher proportion of atopy-only asthma and a lower proportion of T2-high asthma. The eosinophil count, and eosinophil percentage were lower in the respiratory failure group, while neutrophil count was higher. Having a history of allergies (OR = 2.46, 95% CI: 1.08-5.59) and neutrophil count (OR = 1.10, 95% CI: 1.00-1.21) were the risk factors for respiratory failure in children with asthma. There also existed that the risk of respiratory failure increases with decreasing age of the children (OR = 0.85, 95% CI: 0.73-0.99). Conclusion: Notably, risk factors for respiratory failure in hospitalized asthma children include age, having a history of allergies, and neutrophil count. The identification of the above factors and the implementation of timely intervention can optimize the treatment of asthma in children.

Prediction of Asthma Exacerbations in Children.

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

Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review.

BACKGROUND: An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE: This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS: We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS: Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS: Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

Determinants of Asthma Attack Among Adult Asthmatic Patients Attending at Public Hospitals of West Shoa Zone, Oromia Regional State, Ethiopia, 2021: Case-Control Study.

Background: Asthma attacks are life-threatening episodes that place a costly burden on the individual and the community in both high- and low-income countries including Ethiopia. There is scant information on the determinant of it in the study area. Objective: To identify determinants of asthma attack among adult asthmatic patients attending at public hospitals of West Shoa Zone, Oromia Regional State, Ethiopia. Methods: An institutional-based unmatched case-control study design was conducted. In this study, 300 participants (100 cases and 200 controls) were included. A pre-tested structured questionnaire was used to collect data. After the data were entered into Epidata version 3.1, it was exported to SPSS version 25 for analysis. First, bivariable logistic regression was performed. Independent variables with a p-value < 0.25 in binary logistic regression were entered into a multivariable logistic regression model. In the multivariable logistic regression model, independent variables with a p-value < 0.05 were considered to be significant determinants of the outcome variable of the study. Results: This finding identified that upper respiratory tract infection (AOR = 5.89, 95% CI: 2.72, 12.79), obstructive sleep apnea (AOR = 3.48, 95% CI: 1.58, 7.66), passive smoker (AOR = 5.93, 95% CI: 2.07, 16.96), spring season (AOR = 2.49, 95% CI: 1.27, 4.89), pet ownership (AOR = 3.63, 95% CI: 1.82, 7.22), kitchen smoke (AOR = 2.31, 95% CI: 1.6, 4.6), rhinitis (AOR = 4.49, 95% CI: 2.25, 8.93) and being jobless (AOR = 5.68, 95% CI: 1.94, 16.68) were significant determinants of asthma attack. Conclusion: In this study, upper respiratory tract infection, obstructive sleep apnea, passive smoker, spring season, kitchen smoke, pet ownership, rhinitis, and being jobless were identified as significant determinants of an asthma attack. Because asthma attacks are life-threatening events, effective methods and interventions on determinants of asthma attack incidence should be implemented.

Prediction of airborne pollen concentrations by artificial neural network and their relationship with meteorological parameters and air pollutants.

After the early rainfall in the autumn of 2013, respiratory syndromes spread in the Khuzestan province of Iran with the most severity in Ahvaz. There have been recurring outbreaks in recent years. Considering that pollen-derived airborne allergens are regarded as key aeroallergens and the main cause of allergic rhinitis and asthma, this work aimed to forecast total pollen concentration in Ahvaz through an artificial neural network (ANN), followed by evaluating the pollen spatial distribution across the city and the association between pollen concentrations and environmental parameters. The utilized ANN in this work included an input layer with 13 parameters, a hidden layer of five neurons, and an output layer. Data were classified into training, validation, and testing sets. The ANN was implemented with 70% and 80% of data for training. The value of the correlation coefficient for the data validation of these two networks was 0.89 and 0.92, respectively. The results also indicated that despite the difference in the mean concentration of the pollens in various areas of Ahvaz, this difference was not statistically significant (P > 0.05). Furthermore, there was a negative correlation between the concentration of total pollen and relative humidity, precipitation, and air pressure. However, it had a positive correlation with temperature. Consequently, considering the logistical challenges of monitoring bioaerosols in the air, the ANN approach could predict total pollen concentrations. Therefore, in addition to measurements, the ANN technique can be a good tool to enable authorities to mitigate the impact of airborne pollen on people.

Implementation of a Children's Safe Asthma Discharge Care Pathway Reduces the Risk of Future Asthma Attacks in Children-A Retrospective Quality Improvement Report.

Background: Many children attend Emergency Departments (ED) and Out of Hours (OoH) frequently for acute asthma. Follow up care is often suboptimal leaving these children at risk of a future attacks. We report on the development, implementation and evaluation of a safe asthma discharge care pathway (SADCP). Methods: This is a retrospective report on the development, implementation and evaluation of outcomes of a SADCP. The pathway was based on the Teach-to-goal educational methodology that supported the mastery correct inhaler technique and ability to action the personalized asthma action plan (PAAP). Children with frequent asthma attacks were entered as they were discharged from the Emergency Department or ward. The first training session occurred within 1-3 weeks of the index asthma attack with 2 further sessions in the following 8 weeks. Children exiting the pathway were discharged either back to primary care or to a hospital clinic. Results: 81 children entered the pathway (median age 5 years) with 72 discharged from the ED and 9 from the medical wards of the Royal Belfast Hospital for Sick Children. At pathway entry 13% had correct inhaler technique, 10% had a Personalized Asthma Action Plan (PAAP), and 5% had >80% (45% >50%) repeat refill evidence of adherence to inhaled corticosteroid over the previous 12 months. On pathway exit all children demonstrated correct inhaler technique and were able to action their PAAP. One year later 51% and 95% had refill evidence of >80% and >50% adherence. Comparisons of the 12 months before and 12 months after exit from the pathway the median number of emergency ED or OoH asthma attendances and courses of oral corticosteroids reduced to zero with >75% having no attacks requiring this level of attention. Similar findings resulted when the SADCP was implemented in a district general hospital pediatric unit. Conclusion: Implementing an asthma care pathway, using Teach-to-Goal skill training methods and frequent early reviews after an index asthma attack can reduce the future risk of asthma attacks in the next 6 to 12 months.

Ozone Exposure and Asthma Attack in Children.

Background: Increasing evidence indicated that ozone (O3) exposure could trigger asthma attacks in children. However, the effect of O3 at low concentrations is uncertain. Purpose: This study aimed to explore the effects of O3 exposure at low concentrations on asthma attacks in children. Methods: A total of 3,475 children with asthma attacks from the First Affiliated Hospital of Xiamen University were available for the analyses. Air pollution data and meteorological data in Xiamen during 2016-2019 were also collected. A case-crossover design and conditional logistic regression models were conducted to evaluate the association between asthma attacks and outdoor air pollution with lag structures (from lag 0 to lag 6) in both single and multi-pollutant models. Furthermore, we estimated the influence of various levels of O3 exposure on an asthma attack in three groups categorized by maximum daily 8-h sliding average ozone (O3-8 h) (O3-8 h ≥ 100 µg/m3, O3-8 h: 80-99 µg/m3, O3-8 h < 80 µg/m3). Results: For both single-pollutant models and multi-pollutant models, when O3-8 h was higher than 80 µg/m3, O3 exposure was increased the risk of acute asthma attacks on each day of lag. The effect of O3 on children with asthma was significant when O3 concentration was higher than 100 µg/m3. Conclusion: O3 concentration above 80 µg/m3 contributed to an increased risk of asthma attacks in children.

Management of Acute Asthma in Children.

Asthma is the most common disease of childhood globally and acute asthma is the most significant risk factor for asthma-related death and chronic complications. This article will aim to synthesize the most up-to-date research and translate it into a more practical guide to managing acute asthma on a more daily basis. The use of clinical severity score is reviewed alongside the use of history and clinical findings in making objective assessments of patients presenting with acute asthma. Practical evidence-based pathways and a stepwise approach are provided for management of acute asthma in the home, outpatient, emergency, and intensive care settings. In doing so, popular myths are dispelled and practices in relation to management of acute asthma and use of asthma related medications are clarified. Having a standardized approach to management of acute asthma will bring us one step closer to reducing exacerbations and achieving the goal of having zero tolerance towards exacerbations.

Asthma Attacks in Children-Challenges and Opportunities.

Asthma is the most common chronic disease of childhood worldwide, and is responsible for significant morbidity and mortality in children and young people (CYP). Given the inherent dangers of a child experiencing even a single asthma attack, it is essential to identify and manage modifiable risk factors at every clinical opportunity. Following an attack, there is an opportunity to prevent future attacks by assessing compliance and optimizing asthma control. Careful questioning will allow physicians to identify asthma triggers, barriers to good asthma control, and health beliefs or socioeconomic obstacles that may have contributed to this attack. The vast majority of children with asthma can achieve good symptom control with appropriate use of low-dose inhaled corticosteroids.

Evaluation of choroidal thickness in children with acute asthma attack by optical coherence tomography.

OBJECTIVE: To evaluate the change in central choroidal thickness in children with asthma attack before and after treatment with ß2 agonists. MATERIALS AND METHODS: About 100 eyes of 50 patients (5-17 years old) with visual acuity of 20/20 who had no retinal, choroidal, and systemic comorbidity were examined by enhanced depth optical coherence tomography (EDI-OCT) before and after asthma attack treatment. Sixty eyes of 30 healthy children of similar age and gender were evaluated as the control group. The central choroidal thickness, peak expiratory flow (PEF), forced expiratory volume 1(FEV1), oxygen saturation, and heart rate were evaluated. RESULTS: The mean age of the patients was 9.2 ± 3.1 years, and the mean saturation values of patients was 97.2 ± 1.3 before treatment, and it increased to 98.3 ± 0.9 after treatment with a statistically significant difference. The mean FEV1 values were 80.8 ± 15.2 before, and 92.7 ± 12.9 after the treatment and PEF values were 75.9 ± 18.6 before and 89.3 ± 18.9 after treatment. This differences were statistically significant (p < 0.001). The average choroidal thickness before the treatment were 310.4 ± 34.2 µm and decreased to 302.7 ± 34.4 µm after the treatment, this decrease was statistically significant (p < 0.001). The mean choroidal thickness of the control group was 303.0 ± 7.3 µm and compared to the pre-treatment and post-treatment values, it was more similar to the post-treatment values, although there was no statistically difference. CONCLUSION: In our study, it was shown that choroidal thickness was significantly reduced in children with asthma who received attack treatment with ß2 agonists, and it was similar to the control group after the treatment.

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.

BACKGROUND: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field. METHOD: A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review. RESULT: From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field. CONCLUSION: Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

Predicting asthma-related crisis events using routine electronic healthcare data: a quantitative database analysis study.

BACKGROUND: There is no published algorithm predicting asthma crisis events (accident and emergency [A&E] attendance, hospitalisation, or death) using routinely available electronic health record (EHR) data. AIM: To develop an algorithm to identify individuals at high risk of an asthma crisis event. DESIGN AND SETTING: Database analysis from primary care EHRs of people with asthma across England and Scotland. METHOD: Multivariable logistic regression was applied to a dataset of 61 861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage Databank of 174 240 patients from Wales. Outcomes were ≥1 hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance, or death (validation dataset) within a 12-month period. RESULTS: Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking, and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a receiver operating characteristic of 0.71 (95% confidence interval [CI] = 0.70 to 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI = 5.3% to 6.1%) and a negative predictive value of 98.9% (95% CI = 98.9% to 99.0%), with sensitivity of 28.5% (95% CI = 26.7% to 30.3%) and specificity of 93.3% (95% CI = 93.2% to 93.4%); those individuals had an event risk of 6.0% compared with 1.1% for the remaining population. In total, 18 people would need to be followed to identify one admission. CONCLUSION: This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding those not at high risk.