Literature DB >> 33147584

Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China.

Bo Wang1, Yanbin Liu2, Ye Wang1, Wanhong Yin3, Tao Liu4, Dan Liu1, Diandian Li1, Mei Feng1, Yanlin Zhang1, Zong'an Liang1, Ziqiao Fu5, Siyun Fu6, Weimin Li1, Nian Xiong7, Gang Wang1, Fengming Luo8.   

Abstract

BACKGROUND: Effective auscultations are often hard to implement in isolation wards. To date, little is known about the characteristics of pulmonary auscultation in novel coronavirus (COVID-19) pneumonia.
OBJECTIVES: The aim of this study was to explore the features and clinical significance of pulmonary auscultation in COVID-19 pneumonia using an electronic stethoscope in isolation wards.
METHODS: This cross-sectional, observational study was conducted among patients with laboratory-confirmed COVID-19 at Wuhan Red-Cross Hospital during the period from January 27, 2020, to February 12, 2020. Standard auscultation with an electronic stethoscope was performed and electronic recordings of breath sounds were analyzed.
RESULTS: Fifty-seven patients with average age of 60.6 years were enrolled. The most common symptoms were cough (73.7%) during auscultation. Most cases had bilateral lesions (96.4%) such as multiple ground-glass opacities (69.1%) and fibrous stripes (21.8%). High-quality auscultation recordings (98.8%) were obtained, and coarse breath sounds, wheezes, coarse crackles, fine crackles, and Velcro crackles were identified. Most cases had normal breath sounds in upper lungs, but the proportions of abnormal breath sounds increased in the basal fields where Velcro crackles were more commonly identified at the posterior chest. The presence of fine and coarse crackles detected 33/39 patients with ground-glass opacities (sensitivity 84.6% and specificity 12.5%) and 8/9 patients with consolidation (sensitivity 88.9% and specificity 15.2%), while the presence of Velcro crackles identified 16/39 patients with ground-glass opacities (sensitivity 41% and specificity 81.3%).
CONCLUSIONS: The abnormal breath sounds in COVID-19 pneumonia had some consistent distributive characteristics and to some extent correlated with the radiologic features. Such evidence suggests that electronic auscultation is useful to aid diagnosis and timely management of the disease. Further studies are indicated to validate the accuracy and potential clinical benefit of auscultation in detecting pulmonary abnormalities in COVID-19 infection.
© 2020 S. Karger AG, Basel.

Entities:  

Keywords:  Auscultation; Breath sounds; Electronic stethoscope; Novel coronavirus pneumonia

Mesh:

Substances:

Year:  2020        PMID: 33147584     DOI: 10.1159/000509610

Source DB:  PubMed          Journal:  Respiration        ISSN: 0025-7931            Impact factor:   3.580


  5 in total

1.  A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital.

Authors:  Nour Kasim; Noa Bachner-Hinenzon; Shay Brikman; Ori Cheshin; Doron Adler; Guy Dori
Journal:  Biomed Signal Process Control       Date:  2022-06-27       Impact factor: 5.076

2.  Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.

Authors:  Mohanad Alkhodari; Ahsan H Khandoker
Journal:  PLoS One       Date:  2022-01-13       Impact factor: 3.240

3.  Automated lung sound analysis using the LungPass platform: a sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19.

Authors:  Elena A Lapteva; Olga N Kharevich; Victoria V Khatsko; Natalia A Voronova; Maksim V Chamko; Irina V Bezruchko; Elena I Katibnikova; Elena I Loban; Mostafa M Mouawie; Helena Binetskaya; Sergey Aleshkevich; Aleksey Karankevich; Vitaly Dubinetski; Jørgen Vestbo; Alexander G Mathioudakis
Journal:  Eur Respir J       Date:  2021-12-02       Impact factor: 16.671

4.  VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds.

Authors:  Fabrizio Pancaldi; Giuseppe Stefano Pezzuto; Giulia Cassone; Marianna Morelli; Andreina Manfredi; Matteo D'Arienzo; Caterina Vacchi; Fulvio Savorani; Giovanni Vinci; Francesco Barsotti; Maria Teresa Mascia; Carlo Salvarani; Marco Sebastiani
Journal:  Comput Biol Med       Date:  2022-01-06       Impact factor: 4.589

5.  Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1.

Authors:  Fu-Shun Hsu; Shang-Ran Huang; Chien-Wen Huang; Chao-Jung Huang; Yuan-Ren Cheng; Chun-Chieh Chen; Jack Hsiao; Chung-Wei Chen; Li-Chin Chen; Yen-Chun Lai; Bi-Fang Hsu; Nian-Jhen Lin; Wan-Ling Tsai; Yi-Lin Wu; Tzu-Ling Tseng; Ching-Ting Tseng; Yi-Tsun Chen; Feipei Lai
Journal:  PLoS One       Date:  2021-07-01       Impact factor: 3.240

  5 in total

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