Accuracy of spirometry for detection of asthma: a cross-sectional study.

Sao Paulo Med J

MD, PhD. Associate Professor, Department of Medicine, Faculdade de Medicina de Ribeirão Preto (FMRP), Universidade de São Paulo (USP), Ribeirão Preto (SP), Brazil.

Published: January 2018

AI Article Synopsis

  • Asthma, a chronic inflammatory disease, often diagnosed using spirometry and bronchial challenge tests, was studied to assess the accuracy of spirometry in identifying cases in the general population.* -
  • In a sample of 1,922 individuals aged 23 to 25, asthma was found in 200 participants (10.4%); however, spirometry showed weak sensitivity (23%) and specific predictive values (22%), with a specificity of 90% for detecting asthma.* -
  • The study concluded that relying solely on spirometry has significant limitations for asthma detection, indicating a need for additional diagnostic methods.*

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027253PMC
http://dx.doi.org/10.1590/1516-3180.2017.0041250517DOI Listing

Publication Analysis

Top Keywords

accuracy spirometry
12
detecting asthma
12
spirometric abnormality
12
asthma
8
cross-sectional study
8
bronchial challenge
8
challenge test
8
asthma general
8
test
6
spirometry
5

Similar Publications

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 PDF

: 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 PDF

A refined spirometry dataset for comparing segmented (piecewise) linear models to that of GAMLSS.

Data 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 PDF

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.

View Article and Find Full Text PDF

A Wearable Device with Triboelectric Nanogenerator Sensing for Respiration and Spirometry Monitoring.

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 PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!