Development of a web application to evaluate spirometric curve and clinical variables to support COPD diagnosis in primary care

Adriana Maldonado-Franco, Luis F. Giraldo-Cadavid, Eduardo Tuta-Quintero, Alirio R. Bastidas, Angélica Moreno-Giraldo , Daniel A. Botero-Rosas, .

Keywords: Pulmonary disease, chronic obstructive, diagnosis, spirometry, data accuracy

Abstract

Introduction. Choric obstructive pulmonary disease (COPD) is the third mortality cause in the world, and the development of useful diagnostic tools is necessary to improve timely diagnostic rates in primary care settings.
Objective. To develop a web application displaying spirometric and clinical information – including respiratory symptoms and risk factors– to facilitate a COPD diagnosis.
Materials and methods. In this cross-sectional study, an expert consensus was carried out with three specialists using the Delphi method to choose the relevant variables for COPD diagnosis. We developed a Python-based web application to diagnose COPD, displaying the clinical variables deemed relevant by the experts along the spirometric curve.
Results. Twenty-six clinical variables were included in the web application for the diagnosis of COPD. A fourth expert used the web application to classify a cohort of 695 patients who had undergone spirometry in a third-level centre and had answered at least one of five questionnaires for COPD screening. Out of the 695 subjects, 34% had COPD, according to the expert that diagnosed them using the web application. Only 42% of the patients in the COPD group had received a previous COPD diagnosis and 19% of the patients in the no COPD group had been misdiagnosed with the disease.
Conclusion. We developed a web application that displays demographic and clinical information, as well as spirometric data, to facilitate the process of diagnosing COPD in primary care settings.

Downloads

Download data is not yet available.

References

World Health Organization. Chronic obstructive pulmonary disease (COPD). 2021. Accessed: April 16, 2023. Available at: https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)

Global Initiative for Chronic Obstructive Lung Disease. Pocket guide to COPD diagnosis, management, and prevention. A guide for health care professionals. 2023. Accessed: Feb 19, 2023. Available from: https://goldcopd.org/wp-content/uploads/2023/03/POCKETGUIDE-GOLD-2023-ver-1.2-17Feb2023_WMV.pdf

Koblizek V, Novotna B, Zbozinkova Z, Hejduk K. Diagnosing COPD: Advances in training and practice - A systematic review. Adv Med Educ Pract. 2016;7:219-31. https://doi.org/10.2147/AMEP.S76976

Meteran H, Miller MR, Thomsen SF, Christensen K, Sigsgaard T, Backer V. The impact of different spirometric definitions on the prevalence of airway obstruction and their association with respiratory symptoms. ERJ Open Res. 2017;3:00110-2017. https://doi.org/10.1183/23120541.00110-2017

Pothirat C, Chaiwong W, Phetsuk N, Liwsrisakun C. Misidentification of airflow obstruction: Prevalence and clinical significance in an epidemiological study. Int J Chron Obstruct Pulmon Dis. 2015;10:535-40. https://doi.org/10.2147/COPD.S80765

Menezes AMB, Perez-Padilla R, Jardim JRB, Muiño A, Lopez MV, Valdivia G, et al. Chronic obstructive pulmonary disease in five Latin American cities (the PLATINO study): A prevalence study. Lancet. 2005;366:1875-81. https://doi.org/10.1016/S0140-6736(05)67632-5

Casas Herrera A, Montes de Oca M, López Varela MV, Aguirre C, Schiavi E, Jardim JR. COPD underdiagnosis and misdiagnosis in a high-risk primary care population in four Latin American countries. A key to enhance disease diagnosis: The PUMA Study. PLoS One. 2016;11:e0152266. https://doi.org/10.1371/journal.pone.0152266

Pan MM, Zhang HS, Sun TY. Value of forced expiratory volume in 6 seconds (FEV6) in the evaluation of pulmonary function in Chinese elderly males. Chin Med J. 2017;97:1556-61. https://doi.org/10.3760/cma.j.issn.0376-2491.2017.20.011

Chung KS, Jung JY, Park MS, Kim YS, Kim SK, Chang J, et al. Cut-off value of FEV1/FEV6 as a surrogate for FEV1/FVC for detecting airway obstruction in a Korean population. Int J Chron Obstruct Pulmon Dis. 2016;11:1957-63. https://doi.org/10.2147/COPD.S113568

Oh A, Morris TA, Yoshii IT, Morris TA. Flow decay: A novel spirometric index to quantify dynamic airway resistance. Respir Care. 2017;62:928-35. https://doi.org/10.4187/respcare.04850

Li H, Liu C, Zhang Y, Xiao W. The concave shape of the forced expiratory flow-volume curve in 3 seconds is a practical surrogate of FEV1/FVC for the diagnosis of airway limitation in inadequate spirometry. Respir Care. 2017;62:363-9. https://doi.org/10.4187/respcare.05016

Martinez FJ, Raczek AE, Seifer FD, Conoscenti CS, Curtice TG, D’Eletto T, et al. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD. 2008;5:85-95. https://doi.org/10.1080/15412550801940721

García-Ortiz JD, Cardona-Jiménez JL, Quijano-Almeida YM. Evaluation with COPD-PS questionnaire and vitalograph COPD-6 portable device as a strategy for early diagnosis of COPD in primary care. Iatreia. 2020;33:229-38. https://doi.org/10.17533/udea.iatreia.44

Yawn BP, Mapel DW, Mannino DM, Martínez FJ, Donohue JF, Hanania NA, et al. Development of the Lung Function Questionnaire (LFQ) to identify airflow obstruction. Int J Chron Obstruct Pulmon Dis. 2010;5:1-10. https://doi.org/10.2147/COPD.S7683

Hanania NA, Mannino DM, Yawn BP, Mapel DW, Martinez FJ, Donohue JF, et al. Predicting risk of airflow obstruction in primary care: Validation of the lung function questionnaire (LFQ). Respir Med. 2010;104:1160-70. https://doi.org/10.1016/j.rmed.2010.02.009

Price DB, Tinkelman DG, Halbert RJ, Nordyke RJ, Isonaka S, Nonikov D, et al. Symptombased questionnaire for identifying COPD in smokers. Respiration. 2006;73:285-95. https://doi.org/10.1159/000090142

Stanley AJ, Hasan I, Crockett AJ, van Schayck OC, Zwar NA. COPD Diagnostic Questionnaire (CDQ) for selecting at-risk patients for spirometry: A cross-sectional study in Australian general practice. NPJ Prim Care Respir Med. 2014;24:14024. https://doi.org/10.1038/npjpcrm.2014.24

López Varela MV, Montes de Oca M, Rey A, Casas A, Stirbulov R, Di Boscio V, et al. Development of a simple screening tool for opportunistic COPD case finding in primary care in Latin America: The PUMA study. Respirology. 2016;21:1227-34. https://doi.org/10.1111/resp.12834

Calverley PMA, Nordyke RJ, Halbert RJ, Isonaka S, Nonikov D. Development of a population-based screening questionnaire for COPD. COPD. 2005;2:225-32. https://doi.org/10.1081/COPD-57594

Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med. 1999;159:179-87. https://doi.org/10.1164/ajrccm.159.1.9712108

Makhmutov R. The Delphi method at a glance. Pflege. 2021;34:221.

Colson AR, Cooke RM. Expert elicitation: Using the classical model to validate experts’ judgments. Rev Environ Econ Policy. 2018;12:113-32. https://doi.org/10.1093/reep/rex022

Hunger T, Schnell-Inderst P, Sahakyan N, Siebert U. Using expert opinion in health technology assessment: A guideline review. Int J Technol Assess Health Care. 2016;32:131-9. https://doi.org/10.1017/S0266462316000209

Schultz K, D’Aquino LC, Soares MR, Gimenez A, Pereira CA. Lung volumes and airway resistance in patients with a possible restrictive pattern on spirometry. J Bras Pneumol. 2016;42:341-7. https://doi.org/10.1590/S1806-37562016000000091

Graham BL, Steenbruggen I, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, et al. Standardization of spirometry 2019 update an official American Thoracic Society and European Respiratory Society technical statement. Am J Respir Crit Care Med. 2019;200:e70-88. https://doi.org/10.1371/journal.pone.0188532

Mohamed Hoesein FA, Zanen P, Sachs AP, Verheij TJ, Lammers JW, Broekhuizen BD. Spirometric thresholds for diagnosing COPD: 0.70 or LLN, pre- or post-dilator values? COPD. 2012;9:338-43. https://doi.org/10.3109/15412555.2012.667851

van Dijk W, Tan W, Li P, Guo B, Li S, Benedetti A, et al. Clinical relevance of fixed ratio vs lower limit of normal of FEV1/FVC in COPD: Patient-reported outcomes from the CanCOLD cohort. Ann Fam Med. 2015;13:41-8. https://doi.org/10.1370/afm.1714

Wang S, Gong W, Tian Y, Zhou J. FEV1/FEV6 in primary care is a reliable and easy method for the diagnosis of COPD. Respir Care. 2016;61:349-53. https://doi.org/10.4187/respcare.04348

Cheng Q, Juen J, Bellam S, Fulara N, Close D, Silverstein JC, et al. Predicting pulmonary function from phone sensors. Telemed J E Health. 2017;23:913-9. https://doi.org/10.1089/tmj.2017.0008

Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS One. 2017;12:e0188532. https://doi.org/10.1371/journal.pone.0188532

How to Cite
1.
Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Bastidas AR, Moreno-Giraldo A, Botero-Rosas DA. Development of a web application to evaluate spirometric curve and clinical variables to support COPD diagnosis in primary care. biomedica [Internet]. 2024 May 31 [cited 2024 Jul. 17];44(Sp. 1):160-7. Available from: https://revistabiomedica.org/index.php/biomedica/article/view/7142

Some similar items:

Published
2024-05-31

Altmetric

Funding data

Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views
Crossref Cited-by logo
QR Code