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Research ArticleReviews

Variation Among Spirometry Interpretation Algorithms

Katrina A D’Urzo, Florence Mok and Anthony D D’Urzo
Respiratory Care October 2020, 65 (10) 1585-1590; DOI: https://doi.org/10.4187/respcare.07294
Katrina A D’Urzo
School of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland.
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Florence Mok
Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.
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Anthony D D’Urzo
Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.
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Abstract

Several algorithms exist to facilitate spirometric interpretation in clinical practice, yet there is a lack of consensus on how spirometric criteria for asthma, COPD, and restrictive disorders should be incorporated into spirometry interpretation algorithms suitable for use in day-to-day primary care management. The purpose of this review was to identify and describe the variability that exists among spirometry interpretation algorithms and how this might be relevant to the interpretation of spirometric data of common conditions encountered in primary care. MEDLINE, Embase, and mainstream search engines were used to identify all English-language spirometry interpretation algorithm–related material between January 1990 and December 2018. Eight variations in spirometry interpretation algorithms were identified via specific a priori assumptions that each spirometry interpretation algorithm should contain content consistent with national and international guidelines related to spirometry interpretation. Of the 26 spirometry interpretation algorithms identified, 5 were deemed impractical for day-to-day use in primary care (19%), 23 lacked a logic string leading to the postbronchodilator FEV1/FVC (88%), 4 relied on postbronchodilator change in FEV1 to distinguish between asthma and COPD (15%), 24 lacked a prompt for bronchodilator challenge when FEV1/FVC was considered to be at a normal level (92%), 12 did not indicate whether the data represented a prebronchodilator or postbronchodilator scenario (46%), 7 did not include a logic string that considers mixed obstructive/restrictive defect (27%), 23 did not contain a prompt to refer for methacholine challenge testing when spirometry appeared normal (88%), and 2 spirometry interpretation algorithms did not include a logic string leading to restrictive disorder (8%). Our review suggests that there is considerable variability among spirometry interpretation algorithms available as diagnostic aids and that there is a need for standardization of spirometry interpretation algorithms in primary care.

  • spirometry interpretation algorithms
  • variation
  • primary care
  • clinician tools
  • family medicine
  • respiratory illness

Footnotes

  • Correspondence: Anthony D D’Urzo MD MSc. E-mail: tonydurzo{at}sympatico.ca
  • This work was supported by the Comprehensive Research Experience for Medical Students, Faculty of Medicine, Department of Family and Community Medicine, University of Toronto. The authors have disclosed no conflicts of interest.

  • Supplementary material related to this paper is available at http://www.rcjournal.com.

  • Copyright © 2020 by Daedalus Enterprises
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Respiratory Care: 65 (10)
Respiratory Care
Vol. 65, Issue 10
1 Oct 2020
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Variation Among Spirometry Interpretation Algorithms
Katrina A D’Urzo, Florence Mok, Anthony D D’Urzo
Respiratory Care Oct 2020, 65 (10) 1585-1590; DOI: 10.4187/respcare.07294

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Variation Among Spirometry Interpretation Algorithms
Katrina A D’Urzo, Florence Mok, Anthony D D’Urzo
Respiratory Care Oct 2020, 65 (10) 1585-1590; DOI: 10.4187/respcare.07294
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Keywords

  • spirometry interpretation algorithms
  • variation
  • primary care
  • clinician tools
  • family medicine
  • respiratory illness

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