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BACKGROUND: COPD is diagnosed by using FEV1FVC, which has limitations as a diagnostic test. We assessed the validity of several measures derived from the expiratory phase of the flow-volume curve obtained from spirometry to diagnose COPD: the slopes that correspond to the volume expired after the 50% and 75% of the FVC, the slope formed between the peak expiratory flow (PEF) and the FVC, and the area under the expiratory flow/volume curve. METHODS: We conducted a cross-sectional diagnostic test study in 765 consecutive subjects referred for spirometry because of respiratory symptoms. We compared the reproducibility and accuracy of the proposed measures against post-bronchodilator FEV1/FVC < 0.70. We also evaluated the proportion of respiratory symptoms for the FEV1/FVC, FEV1 per FEV in the first 6 s (FEV6), and the PEF slope. RESULTS: The subjects had a mean age of 65.8 y, 57% were women, and 35% had COPD. The test-retest intraclass correlation coefficient values were 0.89, 0.85, and 0.83 for FEV1/FVC, FEV1/FEV6, and the PEF slope, respectively. The area under the curve values were 0.93 (expiratory flow/volume), 0.96 (potential expiratory flow/volume), 0.97 (potential expiratory flow/volume at 75% of FVC), and 0.82 (potential expiratory flow/volume at 50% of FVC). The area under the receiver operating characteristic curve was 0.99 for FEV1/FEV6, 0.99 for the slope at 50% of the FVC, and 0.98 for the PEF slope. CONCLUSIONS: The FEV1/FEV6, PEF slope, and 50% FVC slopes had similar diagnostic performances compared with FEV1/FVC. [ABSTRACT FROM AUTHOR]

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.