Restrictive spirometry versus restrictive lung function using the GLI reference values.

epidemiology respiratory function tests restrictive lung function restrictive spirometry pattern spirometry total lung capacity

Journal

Clinical physiology and functional imaging
ISSN: 1475-097X
Titre abrégé: Clin Physiol Funct Imaging
Pays: England
ID NLM: 101137604

Informations de publication

Date de publication:
May 2022
Historique:
revised: 03 02 2022
received: 26 10 2021
accepted: 07 02 2022
pubmed: 1 3 2022
medline: 15 4 2022
entrez: 28 2 2022
Statut: ppublish

Résumé

Restrictive lung function may indicate various underlying diseases. The aim of this study was to evaluate the accuracy of different restrictive spirometry patterns (RSPs) to identify restrictive lung function (total lung capacity [TLC] < lower limit of normal [LLN]) according to reference values by the Global Lung Function Initiative (GLI) in a wide age-ranged, general population sample. A general population sample (n = 607, age 23-72 years, smokers 18.8%) with proper dynamic spirometry and TLC measurements, was included. Accuracy of two main categories of RSP to identify TLC < LLN were evaluated: traditional RSPs (definition 1: FVC < 80% of predicted and FEV The prevalence of restrictive lung function (TLC < LLN) was 5.3%. The most accurate cut-offs for FVC to identify TLC < LLN were 85.5% for FVC% of predicted, and -1.0 for FVC Z-score. The traditional RSP definitions 1 and 2 had higher specificity (95.0% and 96.9%) but substantially lower sensitivity compared to RSP definitions 3 and 4. Based on the GLI reference values, the RSP definition FVC < LLN and FEV

Sections du résumé

BACKGROUND BACKGROUND
Restrictive lung function may indicate various underlying diseases. The aim of this study was to evaluate the accuracy of different restrictive spirometry patterns (RSPs) to identify restrictive lung function (total lung capacity [TLC] < lower limit of normal [LLN]) according to reference values by the Global Lung Function Initiative (GLI) in a wide age-ranged, general population sample.
METHODS METHODS
A general population sample (n = 607, age 23-72 years, smokers 18.8%) with proper dynamic spirometry and TLC measurements, was included. Accuracy of two main categories of RSP to identify TLC < LLN were evaluated: traditional RSPs (definition 1: FVC < 80% of predicted and FEV
RESULTS RESULTS
The prevalence of restrictive lung function (TLC < LLN) was 5.3%. The most accurate cut-offs for FVC to identify TLC < LLN were 85.5% for FVC% of predicted, and -1.0 for FVC Z-score. The traditional RSP definitions 1 and 2 had higher specificity (95.0% and 96.9%) but substantially lower sensitivity compared to RSP definitions 3 and 4.
CONCLUSION CONCLUSIONS
Based on the GLI reference values, the RSP definition FVC < LLN and FEV

Identifiants

pubmed: 35225428
doi: 10.1111/cpf.12745
pmc: PMC9311670
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

181-189

Informations de copyright

© 2022 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine.

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Auteurs

Tomi Myrberg (T)

Department of Surgical and Perioperative Sciences, Anesthesiology and Intensive Care Medicine, Umeå University, Umeå, Sweden.

Anne Lindberg (A)

Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.

Berne Eriksson (B)

Department of Krefting Research Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
Department of Research and Development, Region Halland, Halmstad, Sweden.

Linnea Hedman (L)

Department of Public Health and Clinical Medicine, Section of Sustainable Health/the OLIN Unit, Umeå University, Umeå, Sweden.

Caroline Stridsman (C)

Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University, Umeå, Sweden.

Bo Lundbäck (B)

Department of Research and Development, Region Halland, Halmstad, Sweden.

Eva Rönmark (E)

Department of Public Health and Clinical Medicine, Section of Sustainable Health/the OLIN Unit, Umeå University, Umeå, Sweden.

Helena Backman (H)

Department of Public Health and Clinical Medicine, Section of Sustainable Health/the OLIN Unit, Umeå University, Umeå, Sweden.

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Classifications MeSH