Factors related to the diagnostic yield of flexible bronchoscopy without guidance in bronchoscopically invisible peripheral lung lesions

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Abstract

Background: We aimed to analyze the factors predicting the diagnostic performance of flexible bronchoscopy without guidance in peripheral lung lesions that are endoscopically invisible. Methods: This was a retrospective study conducted in St. Paul's Hospital, The Catholic University of Korea, between January 2007 and March 2013. We included all patients who received bronchoscopy during this period. The analyzed variables were age, sex, the etiology of the lesion, lesion size, distance from the pleura, and presence of the bronchus sign. We used multiple logistic regression analysis to identify the significant independent factors associated with diagnostic yield. Results: We included 151 patients in this study. The overall diagnostic yield was 58.3%. The sensitivity was 43.2% for malignant disease and 78.1% for benign disease. The benign lung lesions (p<0.001), lesion size (p=0.015), presence of the exposed type of bronchus sign (p<0.001), and presence of cavitary lung lesions (p=0.005) were factors influencing the yield of flexible bronchoscopy by univariate analysis. In a multivariate logistic regression analysis, the exposed type of bronchus sign and benign lung lesions were independent predicting factors (odds ratio [OR]: 27.95; 95% confidence interval [CI], 7.56-103.32; p<0.001 and OR, 4.91; 95% CI, 1.76-13.72; p=0.002). Conclusion: The presence of the exposed type of bronchus sign and benign lung lesions are determining factors of the diagnostic yield in flexible bronchoscopy in evaluating peripheral lesions that are not endoscopically visible.

Original languageEnglish
Pages (from-to)284-290
Number of pages7
JournalTuberculosis and Respiratory Diseases
Volume80
Issue number3
DOIs
StatePublished - Jul 2017

Bibliographical note

Publisher Copyright:
Copyright © 2017 The Korean Academy of Tuberculosis and Respiratory Diseases. All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Bronchi
  • Bronchoscopy
  • Lung
  • Multidetector computed tomography

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