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PLoS One. 2014 Dec 31;9(12):e116238. doi: 10.1371/journal.pone.0116238. eCollection 2014.

Algorithm for automatic forced spirometry quality assessment: technological developments.

Author information

1
Dept. d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial (ESAII), Centre for Biomedical Engineering Research (CREB), Universitat Politècnica de Catalunya, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain.
2
Department of Pulmonary Medicine. Hospital Clínic de Barcelona (ICT). IDIBAPS, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación en Red de Enfermedades Respiratorias (CibeRes), Palma de Mallorca, Spain.
3
Barcelona Digital Technology Centre, Barcelona, Spain; Dept. d'Enginyeria Telemàtica (ENTEL), Universitat Politècnica de Catalunya, Barcelona, Spain; ViCOROB, Universitat de Girona, Girona, Spain.
4
Barcelona Digital Technology Centre, Barcelona, Spain; Dept. d'Enginyeria Telemàtica (ENTEL), Universitat Politècnica de Catalunya, Barcelona, Spain.

Abstract

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.

PMID:
25551213
PMCID:
PMC4281176
DOI:
10.1371/journal.pone.0116238
[Indexed for MEDLINE]
Free PMC Article

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