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A Disease Model for Wheezing Disorders in Preschool Children Based on Clinicians' Perceptions

By Ben Daniel Spycher, Michael Silverman, Juerg Barben, Ernst Eber, Stéphane Guinand, Mark L. Levy, Caroline Pao, Willem M. van Aalderen, Onno C. P. van Schayck and Claudia Elisabeth Kuehni


Background:\ud Wheezing disorders in childhood vary widely in clinical presentation and disease course. During the last years, several ways to classify wheezing children into different disease phenotypes have been proposed and are increasingly used for clinical guidance, but validation of these hypothetical entities is difficult.\ud \ud Methodology/Principal Findings:\ud The aim of this study was to develop a testable disease model which reflects the full spectrum of wheezing illness in preschool children. We performed a qualitative study among a panel of 7 experienced clinicians from 4 European countries working in primary, secondary and tertiary paediatric care. In a series of questionnaire surveys and structured discussions, we found a general consensus that preschool wheezing disorders consist of several phenotypes, with a great heterogeneity of specific disease concepts between clinicians. Initially, 24 disease entities were described among the 7 physicians. In structured discussions, these could be narrowed down to three entities which were linked to proposed mechanisms: a) allergic wheeze, b) non-allergic wheeze due to structural airway narrowing and c) non-allergic wheeze due to increased immune response to viral infections. This disease model will serve to create an artificial dataset that allows the validation of data-driven multidimensional methods, such as cluster analysis, which have been proposed for identification of wheezing phenotypes in children.\ud \ud Conclusions/Significance:\ud While there appears to be wide agreement among clinicians that wheezing disorders consist of several diseases, there is less agreement regarding their number and nature. A great diversity of disease concepts exist but a unified phenotype classification reflecting underlying disease mechanisms is lacking. We propose a disease model which may help guide future research so that proposed mechanisms are measured at the right time and their role in disease heterogeneity can be studied

Publisher: Public Library of Science
Year: 2009
DOI identifier: 10.1371/journal.pone.0008533
OAI identifier:

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