The respiratory sound features of COVID-19 patients fill gaps between clinical data and screening methods

  1. Yinghui Huang
  2. Sijun Meng
  3. Yi Zhang
  4. Shuisheng Wu
  5. Yu Zhang
  6. Yawei Zhang
  7. Yixiang Ye
  8. Qifeng Wei
  9. Niangui Zhao
  10. Jianping Jiang
  11. Xiaoying Ji
  12. Chunxia Zhou
  13. Chao Zheng
  14. Wen Zhang
  15. Lizhong Xie
  16. Yongchao Hu
  17. Jianquan He
  18. Jian Chen
  19. Wangyue Wang
  20. Liming Cao
  21. Wen Xu
  22. Yunhong Lei
  23. Zhenghua Jiang
  24. Weiping Hu
  25. Wenjuan Qin
  26. Wanyu Wang
  27. Yulong He
  28. Hang Xiao
  29. Xiaofang Zheng
  30. Yiqun Hu
  31. Wensheng Pan
  32. Changhua Zhang
  33. Jianfeng Cai

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Abstract

Background

The 2019 novel coronavirus (COVID-19) has continuous outbreaks around the world. Lung is the main organ that be involved. There is a lack of clinical data on the respiratory sounds of COVID-19 infected pneumonia, which includes invaluable information concerning physiology and pathology. The medical resources are insufficient, which are now mainly supplied for the severe patients. The development of a convenient and effective screening method for mild or asymptomatic suspicious patients is highly demanded.

Methods

This is a retrospective case series study. 10 patients with positive results of nucleic acid were enrolled in this study. Lung auscultation was performed by the same physician on admission using a hand-held portable electronic stethoscope delivered in real time via Bluetooth. The recorded audio was exported, and was analyzed by six physicians. Each physician individually described the abnormal breathing sounds that he heard. The results were analyzed in combination with clinical data. Signal analysis was used to quantitatively describe the most common abnormal respiratory sounds.

Results

All patients were found abnormal breath sounds at least by 3 physicians, and one patient by all physicians. Cackles, asymmetrical vocal resonance and indistinguishable murmurs are the most common abnormal breath sounds. One asymptomatic patient was found vocal resonance, and the result was correspondence with radiographic computed tomography. Signal analysis verified the credibility of the above abnormal breath sounds.

Conclusions

This study describes respiratory sounds of patients with COVID-19, which fills up for the lack of clinical data and provides a simple screening method for suspected patients.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.07.20051060: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the ethics committee of NPFH (202002050).
    Consent: With the patient’s oral consent, we gave them lung
    Randomizationnot detected.
    BlindingAll the results of each physician were recorded in sheet, then summarized and normalized by two graduate students who were blinded to patient information.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In addition to the result judgments of respiratory doctors, the above audio is also graphically displayed and quantitatively described by means of signal analysis using Matlab R2019, v9.6.0 (MathWorks, USA).
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)
    These statistical analyses were performed using SPSS version 13.0 (SPSS Inc, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    At the same time, there are limitations in this study. Since the results of auscultation are mostly based on personal experience, we did not perform statistical analysis such as the sensitivity, specificity and other indicators of diagnostic test due to lacking effective gold standard. In addition, this is a small observed research, and only the lung breath sounds of the patients are collected at the time of admission. No follow-up research was conducted. This investigation complements the existing clinical data of COVID-19, which provides a convenient screening method. It is hoped that public health workers around the world will be able to use electronic stethoscope to increase the detection rate of mild and asymptomatic patients, reduce their own infection risk, and allow remote treatment to help fight the epidemic.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.07.20051060 on medRxiv