Effects of Air Pollutant Exposure on Exacerbation Severity in Asthma Patients with or without Reversible Airflow Obstruction

Fang, Wei; Li, Sinian; Liu, Aiming; Yin, Jing; Chen, Dandan; Li, Binbin; Yao, Cheng‐Bao; Chen, Rongchang; Shi, Fei

doi:10.2147/jaa.s328652

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…Also, recording asthma triggers encountered, such as viral infections, passive smoke, and pets, could give a better understanding of a person's asthma and their symptoms. [64][65][66] Connecting data from pollution monitoring stations and meteorology stations with patient health records provides a wealth of information for analysis.…”

Section: Environment Monitoringmentioning

confidence: 99%

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

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Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on longterm management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

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Section: Environment Monitoringmentioning

confidence: 99%

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

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“…We discovered no statistically significant difference in arterial blood gas analyses between the three groups of With a sensitivity of 36% and a specificity of 91.2%, a different study of adult asthmatic patients found that a concentration of 57.659 μg/ m 3 of PM2.5 (AUC = 0.645, p = 0.027) offered the highest performance to predict severe asthma exacerbation. 10 Our findings suggested that asthmatic youngsters may be more sensitive to elevated PM2.5 levels than adult asthmatics. When the ambient PM2.5 concentration is excessively high, asthmatic children should limit their outside activities to lower their risk of having severe asthma attacks.…”

Section: Discussionmentioning

confidence: 56%

“…The frequency of severe asthma exacerbation in children increased when the PM2.5 concentration rose beyond 44.5 μg/m 3 . With a sensitivity of 36% and a specificity of 91.2%, a different study of adult asthmatic patients found that a concentration of 57.659 μg/m 3 of PM2.5 (AUC = 0.645, p = 0.027) offered the highest performance to predict severe asthma exacerbation 10 . Our findings suggested that asthmatic youngsters may be more sensitive to elevated PM2.5 levels than adult asthmatics.…”

Section: Discussionmentioning

confidence: 64%

“…These studies found that PM2.5 exposure is critical in inducing asthma exacerbation events leading to hospitalization. To our knowledge, only one study revealed that the concentration of PM2.5 was associated with the severity of adults' asthma exacerbations 10 . This research only focused on the asthma exacerbation severity of adult outpatients.…”

Section: Introductionmentioning

confidence: 99%

“…To our knowledge, only one study revealed that the concentration of PM2.5 was associated with the severity of adults' asthma exacerbations. 10 This research only focused on the asthma exacerbation severity of adult outpatients. However, the relationship between PM2.5's concentration and the severity of acute exacerbation in asthmatic children needs to be clarified.…”

Section: Introductionmentioning

confidence: 99%

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The association between PM2.5 concentration and the severity of acute asthmatic exacerbation in hospitalized children: A retrospective study in Chongqing, China

Zhang

et al. 2023

Pediatric Pulmonology

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BackgroundAmbient PM2.5 is associated with asthma exacerbation. The association between the concentration of PM2.5 and the severity of asthma exacerbation has yet to be thoroughly clarified. The study aims to explore the association between the piror 30 days average concentration of PM2.5 and the severity of acute asthma exacerbation in hospitalized children.MethodsA total of 269 children with acute exacerbation of asthma were enrolled and divided into three groups according to the PM2.5 exposure concentrations: group 1 (PM2.5: <37.5 μg/m3), group 2 (PM2.5: 37.5–75 μg/m3), group 3 (PM2.5: ≥75 μg/m3), respectively. The ordered logistic regression modeling was conducted to explore the influence of daily PM2.5 concentration on the clinical severity of children's asthma exacerbation. Multiple linear regression was conducted to explore the association between the concentration of PM2.5 and the length of stay in the hospital (LOS). We also conducted a receiver operating characteristic (ROC) curve analysis to explore the cutoff value of PM2.5 to predict the children's asthma exacerbation.ResultsThere was no statistical difference among the three groups of children in gender, age, body mass index, ethnicity, the first diagnosis of asthma, allergic history, passive smoke exposure, or family history of asthma. There was a statistically significant difference in many hospitalization characteristics (p < 0.05) among the three groups of children. Significant differences were found in terms of accessory muscles of respiration (p = 0.005), respiratory failure (p = 0.012), low respiratory tract infectious (p = 0.020), and the severity of asthma exacerbation (p < 0.001) among the three groups. PM2.5 concentration was primarily positively correlated to neutrophile inflammation. The ordered multivariate logistic regression model showed that higher PM2.5 concentrations were significantly associated with greater odds of more severe asthma exacerbation in one and two‐pollutant models. The adjusted odds ratio of severe asthma exacerbation was 1.029 (1.009, 1.049) in the one‐pollutant model. The most significant odds ratio of severe asthma exacerbation was 1.050 (1.027, 1.073) when controlling NO2 in the two‐pollutant models. Multiple linear regression showed that PM2.5 concentration was significantly associated with longer LOS in both one‐pollutant and two‐pollutant models. By performing ROC analysis, the average daily concentration of 44.5 µg/m3 of PM2.5 (AUC = 0.622, p = 0.002) provided the best performance to predict severe asthma of children exacerbation with a sensitivity of 59.2% and a specificity of 63.8%.ConclusionThe increased prior 30 days average concentration of PM2.5 was associated with greater asthma exacerbation severity and longer length of stay in the hospital of children with asthma exacerbation.

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Omega-3 reverses the metabolic and epigenetically regulated placental phenotype acquired from preconceptional and peri-conceptional exposure to air pollutants

Ganguly,

Ghosh,

Jin

et al. 2024

The Journal of Nutritional Biochemistry

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Effects of Air Pollutant Exposure on Exacerbation Severity in Asthma Patients with or without Reversible Airflow Obstruction

Cited by 3 publications

References 18 publications

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

The association between PM2.5 concentration and the severity of acute asthmatic exacerbation in hospitalized children: A retrospective study in Chongqing, China

Omega-3 reverses the metabolic and epigenetically regulated placental phenotype acquired from preconceptional and peri-conceptional exposure to air pollutants

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