Predictors of asthma exacerbation among patients with poorly controlled asthma despite inhaled corticosteroid treatment

Quezada, Wilson; Kwak, Ellie; Reibman, Joan; Rogers, Linda; Mastronarde, John G.; Teague, W. Gerald; Wei, Christine; Holbrook, Janet T.; DiMango, Emily

doi:10.1016/j.anai.2015.11.011

Cited by 38 publications

(34 citation statements)

References 34 publications

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“…This might be a consequence of an overestimation of asthma control that does not match symptom severity. However, the number of patients who had visited the ED during the preceding year was lower in our sample than in other published observational studies [23], and, interestingly, did not significantly predict a new exacerbation during the period analyzed.…”

Section: Discussioncontrasting

confidence: 52%

“…Moreover, exacerbations affect patients with poorly controlled asthma irrespective of severity, even in those treated with inhaled corticosteroids [23]. Loss of asthma control usually leads to unscheduled clinical visits; in one study, 70% of uncontrolled asthmatics had an unscheduled visit to a physician, 36% had an ED visit, and 14% had been hospitalized in the previous year [24].…”

Section: Discussionmentioning

confidence: 99%

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Asthma exacerbations in a tertiary hospital: clinical features, triggers, and risk factors for hospitalization

Pola-Bibian¹,

Domínguez‐Ortega²,

Vilà-Nadal³

et al. 2017

J Investig Allergol Clin Immunol

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Background: The risk factors for asthma exacerbations are not fully understood. The aim of this study was to determine the epidemiological and clinical characteristics of patients who experience asthma exacerbations. We also assessed potential triggers of exacerbations and possible predictors of hospitalization. Methods: A retrospective, noninterventional cohort study was conducted in adult patients who attended the emergency department of a tertiary hospital with an asthma exacerbation during 2014.

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Section: Discussioncontrasting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Asthma exacerbations in a tertiary hospital: clinical features, triggers, and risk factors for hospitalization

Pola-Bibian¹,

Domínguez‐Ortega²,

Vilà-Nadal³

et al. 2017

J Investig Allergol Clin Immunol

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“…72,73 In addition, various sociodemographic factors, such as age, sex, race, ethnicity, income, social support, and proximity and access to quality medical care, were reported to be associated with frequency of asthma exacerbations. 74,75 Behavioral factors, such as self-care skills, health literacy, self-efficacy, medication adherence, depression, anxiety, and stress levels, were shown to contribute to the development of asthma exacerbations, 76,77 and environmental factors, such as indoor and outdoor pollution, exposure to allergens, and weather conditions, have been demonstrated to play an important role in asthma exacerbation prediction. 40 Future algorithms may benefit from predictive modeling that combines all relevant factors in one predictive framework for asthma exacerbations (Fig.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

147

110

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Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training data sets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

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“…Asthma symthoms are poor predictors for future asthma exacerbation [30]. In another study, symptom scores on validated questionnaires were not significantly worse in children who developed an exacerbation [31]. Recently, Robroeks et al reported that prediction of asthma exacerbations in children is possible by profiles of exhaled volatile organic compounds (VOCs), a new non-invasive inflammatory biomarkers [32].…”

Section: Discussionmentioning

confidence: 99%

Association of Asthma Symptoms and Exacerbation with Inflammatory Biomarkers

Navratil¹,

Jureković²,

Topalušić³

et al. 2017

Clin Pediatr OA

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Objective: To investigate a relationship of asthma symptoms and exacerbations with systemic (high-sensitivity Creactive protein (hs-CRP), eosinophilic cationic protein (ECP), leukocytes) and local (exhaled NO (FENO), pH and urates in exhaled breath condensate (EBC) and exhaled breath temperature (EBT)) inflammatory biomarkers (BMs) in asthmatic children. Methods:This cross-sectional study comprised 93 consecutive asthmatic patients (age 6-18 years, 22 girls) with mild intermittent asthma ([IA], N=44) and mild to moderate persistent asthma ([PA], N=49). Medical history (asthma symptoms and exacerbation), pulmonary function, FENO, EBT, samples of exhaled breath and peripheral blood were collected.Results: Local BMs (EBC urates and EBT) showed stronger correlation with asthma symptoms then systemic BMs (hs-CRP, blood count with differencials) (r=0.26-0.68, r=0.06-0.32, p<0.05; respectively). Single measurements of inflammatory BMs are not good predictors for future asthma exacerbation (binary logistic regression; χ²=13.9; df=11; p=0.238). Conclusion:Study of combination of various exhaled breath and exhaled breath condensate BMs should continue, especially in longitudinal studies with repeated measurements of BMs.

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Predictors of asthma exacerbation among patients with poorly controlled asthma despite inhaled corticosteroid treatment

Cited by 38 publications

References 34 publications

Asthma exacerbations in a tertiary hospital: clinical features, triggers, and risk factors for hospitalization

Asthma exacerbations in a tertiary hospital: clinical features, triggers, and risk factors for hospitalization

Machine learning approaches to personalize early prediction of asthma exacerbations

Association of Asthma Symptoms and Exacerbation with Inflammatory Biomarkers

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