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Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians

Background: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies h...

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Published in:NPJ primary care respiratory medicine 2015-03, Vol.25 (1), p.15008-15008, Article 15008
Main Authors: He, Xiao-Ou, D’Urzo, Anthony, Jugovic, Pieter, Jhirad, Reuven, Sehgal, Prateek, Lilly, Evan
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description Background: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. Aims: We examined how two different SIAs may influence decision making among primary-care physicians. Methods: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. Results: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity) ratio was >0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV 1 that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV 1 /FVC ratio was 12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV 1 /FVC decision node in algorithm 1 did not permit consideration of possible COPD. Conclusions: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. Respiratory medicine: Interpreting asthma Variations among algorithms used to interpret ‘blow’ tests for diagnosis of asthma and lung disease may be skewing test results. The researchers, led by Anthony D'Urzo from the University of Toronto in Canada, had 113 primary care physicians make diagnoses from nine sets of blow test or spirogram results using two different spirogram interpretation algorithms (SIAs). They found for a particular case with impaired blow test results, one SIA resulted in a ‘normal’ diagnosis by 78% of physicians, while the other resulted in a diagnosis of ‘consistent with asthma’ by 94% of doctors. The findings suggest a need to standardise the algorithms in order to minimise differ
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Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. Aims: We examined how two different SIAs may influence decision making among primary-care physicians. Methods: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. Results: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity) ratio was &gt;0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV 1 that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV 1 /FVC ratio was &lt;0.70 after bronchodilator challenge but FEV 1 increased &gt;12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV 1 /FVC decision node in algorithm 1 did not permit consideration of possible COPD. Conclusions: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. Respiratory medicine: Interpreting asthma Variations among algorithms used to interpret ‘blow’ tests for diagnosis of asthma and lung disease may be skewing test results. The researchers, led by Anthony D'Urzo from the University of Toronto in Canada, had 113 primary care physicians make diagnoses from nine sets of blow test or spirogram results using two different spirogram interpretation algorithms (SIAs). They found for a particular case with impaired blow test results, one SIA resulted in a ‘normal’ diagnosis by 78% of physicians, while the other resulted in a diagnosis of ‘consistent with asthma’ by 94% of doctors. The findings suggest a need to standardise the algorithms in order to minimise differences in interpreting data, and underscore the importance of educating physicians about the pitfalls of using spirograms in isolation.</description><identifier>ISSN: 2055-1010</identifier><identifier>EISSN: 2055-1010</identifier><identifier>DOI: 10.1038/npjpcrm.2015.8</identifier><identifier>PMID: 25763716</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114 ; 631/378/2649/1409 ; 692/699/1785 ; 692/700/139 ; Algorithms ; Asthma - diagnosis ; Asthma - physiopathology ; Decision Making ; Diagnosis, Differential ; Forced Expiratory Volume ; Humans ; Internal Medicine ; Medicine ; Medicine &amp; Public Health ; Physicians, Primary Care ; Pneumology/Respiratory System ; Primary Care Medicine ; Pulmonary Disease, Chronic Obstructive - diagnosis ; Pulmonary Disease, Chronic Obstructive - physiopathology ; Spirometry ; Thoracic Surgery ; Vital Capacity</subject><ispartof>NPJ primary care respiratory medicine, 2015-03, Vol.25 (1), p.15008-15008, Article 15008</ispartof><rights>The Author(s) 2015</rights><rights>Copyright Nature Publishing Group Mar 2015</rights><rights>Copyright © 2015 Primary Care Respiratory Society UK/Macmillan Publishers Limited 2015 Primary Care Respiratory Society UK/Macmillan Publishers Limited</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-cf9c4ede887a46ba5fb47448c8de458d845c13da9990b84ed09352103125d78b3</citedby><cites>FETCH-LOGICAL-c458t-cf9c4ede887a46ba5fb47448c8de458d845c13da9990b84ed09352103125d78b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1787855393/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1787855393?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25763716$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>He, Xiao-Ou</creatorcontrib><creatorcontrib>D’Urzo, Anthony</creatorcontrib><creatorcontrib>Jugovic, Pieter</creatorcontrib><creatorcontrib>Jhirad, Reuven</creatorcontrib><creatorcontrib>Sehgal, Prateek</creatorcontrib><creatorcontrib>Lilly, Evan</creatorcontrib><title>Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians</title><title>NPJ primary care respiratory medicine</title><addtitle>npj Prim Care Resp Med</addtitle><addtitle>NPJ Prim Care Respir Med</addtitle><description>Background: Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. Aims: We examined how two different SIAs may influence decision making among primary-care physicians. Methods: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. Results: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity) ratio was &gt;0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV 1 that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV 1 /FVC ratio was &lt;0.70 after bronchodilator challenge but FEV 1 increased &gt;12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV 1 /FVC decision node in algorithm 1 did not permit consideration of possible COPD. Conclusions: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. 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Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. Aims: We examined how two different SIAs may influence decision making among primary-care physicians. Methods: Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. Results: We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity) ratio was &gt;0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV 1 that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV 1 /FVC ratio was &lt;0.70 after bronchodilator challenge but FEV 1 increased &gt;12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV 1 /FVC decision node in algorithm 1 did not permit consideration of possible COPD. Conclusions: This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. Respiratory medicine: Interpreting asthma Variations among algorithms used to interpret ‘blow’ tests for diagnosis of asthma and lung disease may be skewing test results. The researchers, led by Anthony D'Urzo from the University of Toronto in Canada, had 113 primary care physicians make diagnoses from nine sets of blow test or spirogram results using two different spirogram interpretation algorithms (SIAs). They found for a particular case with impaired blow test results, one SIA resulted in a ‘normal’ diagnosis by 78% of physicians, while the other resulted in a diagnosis of ‘consistent with asthma’ by 94% of doctors. The findings suggest a need to standardise the algorithms in order to minimise differences in interpreting data, and underscore the importance of educating physicians about the pitfalls of using spirograms in isolation.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>25763716</pmid><doi>10.1038/npjpcrm.2015.8</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
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subjects 631/114
631/378/2649/1409
692/699/1785
692/700/139
Algorithms
Asthma - diagnosis
Asthma - physiopathology
Decision Making
Diagnosis, Differential
Forced Expiratory Volume
Humans
Internal Medicine
Medicine
Medicine & Public Health
Physicians, Primary Care
Pneumology/Respiratory System
Primary Care Medicine
Pulmonary Disease, Chronic Obstructive - diagnosis
Pulmonary Disease, Chronic Obstructive - physiopathology
Spirometry
Thoracic Surgery
Vital Capacity
title Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians
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