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J Appl Physiol (1985). 2016 Jul 1;121(1):324-32. doi: 10.1152/japplphysiol.00086.2016. Epub 2016 May 26.

Physiological phenotyping of pediatric chronic obstructive airway diseases.

Author information

1
Division of Respiratory Medicine, Department of Paediatrics, University Children's Hospital of Bern, University of Bern, Bern, Switzerland; Department of Paediatric Pulmonology, University Children's Hospital Basel (UKBB), Basel, Switzerland; sylvia.nyilas@ukbb.ch.
2
Division of Respiratory Medicine, Department of Paediatrics, University Children's Hospital of Bern, University of Bern, Bern, Switzerland; Division of Respiratory Medicine, University Children's Hospital Zurich, Zurich, Switzerland; and.
3
Department of Paediatric Pulmonology, University Children's Hospital Basel (UKBB), Basel, Switzerland;
4
Division of Respiratory Medicine, Department of Paediatrics, University Children's Hospital of Bern, University of Bern, Bern, Switzerland; Department of Paediatric Pulmonology, University Children's Hospital Basel (UKBB), Basel, Switzerland;
5
Department of Paediatric Pulmonology, University Children's Hospital of Ruhr University Bochum at St. Josef-Hospital, Bochum, Germany.
6
Division of Respiratory Medicine, Department of Paediatrics, University Children's Hospital of Bern, University of Bern, Bern, Switzerland;

Abstract

Inert tracer gas washout (IGW) measurements detect increased ventilation inhomogeneity (VI) in chronic lung diseases. Their suitability for different diseases, such as cystic fibrosis (CF) and primary ciliary dyskinesia (PCD), has already been shown. However, it is still unclear if physiological phenotypes based on different IGW variables can be defined independently of underlying disease. Eighty school-age children, 20 with CF, 20 with PCD, 20 former preterm children, and 20 healthy children, performed nitrogen multiple-breath washout, double-tracer gas (DTG) single-breath washout, and spirometry. Our primary outcome was the definition of physiological phenotypes based on IGW variables. We applied principal component analysis, hierarchical Ward's clustering, and enrichment analysis to compare clinical characteristics between the clusters. IGW variables used for clustering were lung clearance index (LCI) and convection-dependent [conductive ventilation heterogeneity index (Scond)] and diffusion-convection-dependent variables [acinar ventilation heterogeneity index (Sacin) and carbon dioxide and DTG phase III slopes]. Three main phenotypes were identified. Phenotype I (n = 38) showed normal values in all IGW outcome variables. Phenotype II (n = 21) was characterized by pronounced global and convection-dependent VI while diffusion-dependent VI was normal. Phenotype III (n = 21) was characterized by increased global and diffusion- and convection-dependent VI. Enrichment analysis revealed an overrepresentation of healthy children and former preterm children in phenotype I and of CF and PCD in phenotypes II and III. Patients in phenotype III showed the highest proportion and frequency of exacerbations and hospitalization in the year prior to the measurement. IGW techniques allow identification of clinically meaningful, disease-independent physiological clusters. Their predictive value of future disease outcomes remains to be determined.

KEYWORDS:

clustering; gas washout; lung disease; phenotypes; spirometry

PMID:
27231309
DOI:
10.1152/japplphysiol.00086.2016
[Indexed for MEDLINE]
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