Background: Asthma is a chronic inflammatory disease with airway hyperresponsiveness. Spirometry is the most commonly used test among asthmatic patients. Another functional test used for diagnosing asthma is the bronchial challenge test. The aim of this study was to analyze the accuracy of spirometry for detecting asthma in the general population.
Design And Setting: Cross-sectional study with data analysis to evaluate the accuracy of spirometry through calculating sensitivity, specificity and predictive values and through the kappa agreement test.
Methods: Subjects who constituted a birth cohort were enrolled at the age of 23 to 25 years. Spirometric abnormality was defined as reduced forced expiratory volume in one second, i.e. lower than 80% of the predicted value. Measurement of bronchial responsiveness was performed by means of the bronchial challenge test with methacholine. The gold-standard diagnosis of asthma was defined as the presence of bronchial hyperresponsiveness in association with respiratory symptoms.
Results: Asthma was detected in 200 subjects (10.4%) out of the sample of 1922 individuals. Spirometric abnormality was detected in 208 subjects (10.9%) of the sample. The specificity of spirometric abnormality for detecting asthma was 90%, sensitivity was 23%, positive predictive value was 22%, and negative predictive value was 91%. The kappa test revealed weak agreement of 0.13 (95% confidence interval, CI: 0.07-0.19) between spirometry and the diagnosis of asthma.
Conclusion: Spirometry, as a single test, has limitations for detecting asthma in the general population.
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http://dx.doi.org/10.1590/1516-3180.2017.0041250517 | DOI Listing |
Background: Though European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation recommend the use of the forced vital capacity (FVC) lower limit of normal (LLN) to exclude restriction, recent data suggest that the negative predictive value (NPV) of the FVC LLN is lower than has been accepted, particularly among non-Hispanic Black patients. Using a machine learning (ML) model-rather than the FVC LLN-to exclude restriction may improve the accuracy and equity of PFT interpretation. We sought to develop and externally validate a ML model to predict restriction from spirometry and to assess the potential impact of this model on PFT interpretation.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department for Respiratory Diseases Jordanovac, University Hospital Centre Zagreb, 10000 Zagreb, Croatia.
: Cough variant asthma (CVA) is characterized by nonspecific symptoms and normal spirometric values, which makes diagnosis challenging. To diagnose CVA it is necessary to document airway hyperreactivity (AHR). The aim of our study was to evaluate the diagnostic value of body plethysmography in the assessment of AHR using the methacholine challenge test (MCT).
View Article and Find Full Text PDFData Brief
December 2024
Department of Physiology and Membrane Biology, Tupper Hall, Rm 4327, 1275 Med Sciences Drive, University of California, Davis, CA 95616, United States.
Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are widely used for developing spirometric reference equations but are often complex, requiring additional spline tables. This study explores the potential of Segmented (piecewise) Linear Regression as an alternative, comparing its predictive accuracy to GAMLSS and examining the agreement between the two methods. Spirometry data from nearly 16,600 patients, deemed Grade "A" and "B" acceptable from the NHANES 2007-2012 dataset, was analyzed.
View Article and Find Full Text PDFAllergy
December 2024
Service de Pneumologie, Centre Hospitalier Universitaire UCL Namur, Université Catholique de Louvain, Yvoir, Belgium.
Background: Exposure-related changes in exhaled nitric oxide (FeNO) and sputum eosinophils have not been thoroughly compared in the investigation of occupational asthma.
Objective: This study aimed at comparing the accuracies of the changes in FeNO concentrations and sputum eosinophil counts in identifying asthmatic reactions induced by occupational agents during specific inhalation challenges (SICs).
Methods: This retrospective multicenter study included 321 subjects who completed an assessment of FeNO and sputum eosinophils before and 24 h after SICs with various occupational agents, of whom 156 showed a positive result.
ACS Sens
December 2024
Department of Electrical and Computer Systems Engineering, Monash University, Wellington Rd, Clayton, VIC 3800, Australia.
Wearable devices have been developed for the continuous and long-term monitoring of respiration. Although current wearable devices are able to measure the respiration rate, extracting breathing volume has been challenging. In this paper, we propose a wearable respiration monitoring sensor based on triboelectric nanogenerator (TENG) technology.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!