Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

Frey, Urs (2007). Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model. Current opinion in immunology, 7(3), pp. 223-30. Kidlington, UK: Elsevier 10.1097/ACI.0b013e32810fd771

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PURPOSE OF REVIEW: Predicting asthma episodes is notoriously difficult but has potentially significant consequences for the individual, as well as for healthcare services. The purpose of this review is to describe recent insights into the prediction of acute asthma episodes in relation to classical clinical, functional or inflammatory variables, as well as present a new concept for evaluating asthma as a dynamically regulated homeokinetic system. RECENT FINDINGS: Risk prediction for asthma episodes or relapse has been attempted using clinical scoring systems, considerations of environmental factors and lung function, as well as inflammatory and immunological markers in induced sputum or exhaled air, and these are summarized here. We have recently proposed that newer mathematical methods derived from statistical physics may be used to understand the complexity of asthma as a homeokinetic, dynamic system consisting of a network comprising multiple components, and also to assess the risk for future asthma episodes based on fluctuation analysis of long time series of lung function. SUMMARY: Apart from the classical analysis of risk factor and functional parameters, this new approach may be used to assess asthma control and treatment effects in the individual as well as in future research trials.

Item Type:

Journal Article (Further Contribution)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Paediatric Medicine

UniBE Contributor:

Frey, Urs Peter

ISSN:

0952-7915

ISBN:

17489039

Publisher:

Elsevier

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:52

Last Modified:

04 May 2014 23:15

Publisher DOI:

10.1097/ACI.0b013e32810fd771

PubMed ID:

17489039

Web of Science ID:

000247086500001

URI:

https://boris.unibe.ch/id/eprint/22048 (FactScience: 29518)

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