HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Permissions Request Permissions Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (23) Citing Articles via Google Scholar Google Scholar Articles by Combescure, C. Articles by Godard, P. Search for Related Content PubMed PubMed Citation Articles by Combescure, C. Articles by Godard, P.

Assessment of variations in control of asthma over time

¹ Laboratoire de Biostatistique, Institut Universitaire de Recherche Clinique, and ² Service de Pneumologie, CHU Hôpital Arnaud de Villeneuve, Montpellier, and ³ Laboratoire de Biostatistique, Hôpital La Timone, Marseille, France

CORRESPONDENCE: C. Combescure, Laboratoire de Biostatistique, Institut Universitaire de Recherche Clinique, 641 Av. G. Giraud, 34093 Montpellier, France. Fax: 33 467542731. E-mail: combesc@iurc1.iurc.montp.inserm.fr

Keywords: asthma, control states, Markov model, severity

Received: September 2, 2002
Accepted February 21, 2003

This study was supported by an unrestricted educational grant from GlaxoSmithKline, France.

Control and severity of asthma are two different but complementary concepts. The severity of asthma could influence the control over time. The aim of this study was to demonstrate this relationship.

A total 365 patients with persistent asthma (severity) were enrolled and followed-up prospectively. Data were analysed using a continuous time homogeneous Markov model of the natural history of asthma. Control of asthma was defined according to three health states which were qualified: optimal, suboptimal and unacceptable control (states 1, 2 and 3). Transition forces (denoted _ij from state i to state j) and transition probabilities between control states were assessed and the results stratified by asthma severity were compared.

Models were validated by comparing expected and observed numbers of patients in the different states. Transition probabilities stabilised between 100–250 days and more rapidly in patients with mild-to-moderate asthma. Patients with mild-to-moderate asthma in suboptimal or unacceptable control had a high probability of transition directly to optimal control. Patients with severe asthma had a tendency to remain in unacceptable control.

A Markov model is a useful tool to model the control of asthma over time. Severity modified clearly the health states. It could be used to compare the performance of different approaches to asthma management.

This article has been cited by other articles:

				H. K. Reddel, D. R. Taylor, E. D. Bateman, L.-P. Boulet, H. A. Boushey, W. W. Busse, T. B. Casale, P. Chanez, P. L. Enright, P. G. Gibson, et al. An Official American Thoracic Society/European Respiratory Society Statement: Asthma Control and Exacerbations: Standardizing Endpoints for Clinical Asthma Trials and Clinical Practice Am. J. Respir. Crit. Care Med., July 1, 2009; 180(1): 59 - 99. [Abstract] [Full Text] [PDF]

				D. R. Taylor, E. D. Bateman, L-P. Boulet, H. A. Boushey, W. W. Busse, T. B. Casale, P. Chanez, P. L. Enright, P. G. Gibson, J. C. de Jongste, et al. A new perspective on concepts of asthma severity and control Eur. Respir. J., September 1, 2008; 32(3): 545 - 554. [Abstract] [Full Text] [PDF]

				A. P. Greening, D. Stempel, E. D. Bateman, and J. C. Virchow Managing asthma patients: which outcomes matter? Eur. Respir. Rev., June 1, 2008; 17(108): 53 - 61. [Abstract] [Full Text] [PDF]