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Quantification of respiratory sounds by a continuous monitoring system can be used to predict complications after extubation: a pilot study

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Abstract

To show that quantification of abnormal respiratory sounds by our developed device is useful for predicting respiratory failure and airway problems after extubation. A respiratory sound monitoring system was used to collect respiratory sounds in patients undergoing extubation. The recorded respiratory sounds were subsequently analyzed. We defined the composite poor outcome as requiring any of following medical interventions within 48 h as defined below. This composite outcome includes reintubation, surgical airway management, insertion of airway devices, unscheduled use of noninvasive ventilation or high-flow nasal cannula, unscheduled use of inhaled medications, suctioning of sputum by bronchoscopy and unscheduled imaging studies. The quantitative values (QV) for each abnormal respiratory sound and inspiratory sound volume were compared between composite outcome groups and non-outcome groups. Fifty-seven patients were included in this study. The composite outcome occurred in 18 patients. For neck sounds, the QVs of stridor and rhonchi were significantly higher in the outcome group vs the non-outcome group. For anterior thoracic sounds, the QVs of wheezes, rhonchi, and coarse crackles were significantly higher in the outcome group vs the non-outcome group. For bilateral lateral thoracic sounds, the QV of fine crackles was significantly higher in the outcome group vs the non-outcome group. Cervical inspiratory sounds volume (average of five breaths) immediately after extubation was significantly louder in the outcome group vs non-outcome group (63.3 dB vs 54.3 dB, respectively; p < 0.001). Quantification of abnormal respiratory sounds and respiratory volume may predict respiratory failure and airway problems after extubation.

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Not applicable.

Abbreviations

NIV:

Noninvasive ventilation

HFNC:

High-flow nasal cannula

QV:

Quantitative value

STQV:

Stridor quantitative value

RHQV:

Rhonchi quantitative value

GAQV:

Gargling quantitative value

WHQV:

Wheezes quantitative value

CCQV:

Coarse crackles quantitative value

FCQV:

Fine crackles quantitative value

AUC:

Area under the curve

ROC:

Receiver operating characteristic

References

  1. Thille AW, Richard JC, Brochard L. The decision to extubate in the intensive care unit. Am J Respir Crit Care Med. 2013;187(12):1294–302. https://doi.org/10.1164/rccm.201208-1523CI.

    Article  Google Scholar 

  2. Nava S, Gregoretti C, Fanfulla F, et al. Noninvasive ventilation to prevent respiratory failure after extubation in high-risk patients*. Crit Care Med. 2005;33(11):2465–70. https://doi.org/10.1097/01.Ccm.0000186416.44752.72.

    Article  Google Scholar 

  3. Hernandez G, Vaquero C, Gonzalez P, et al. Effect of Postextubation high-flow nasal cannula vs conventional oxygen therapy on reintubation in low-risk patients: a Randomized Clinical Trial. JAMA. 2016;315(13):1354–61. https://doi.org/10.1001/jama.2016.2711.

    Article  CAS  Google Scholar 

  4. Pramono RXA, Bowyer S, Rodriguez-Villegas E. Automatic adventitious respiratory sound analysis: a systematic review. PLoS ONE. 2017;12(5):e0177926. https://doi.org/10.1371/journal.pone.0177926.

    Article  CAS  Google Scholar 

  5. Hafke-Dys H, Breborowicz A, Kleka P, Kocinski J, Biniakowski A. The accuracy of lung auscultation in the practice of physicians and medical students. PLoS ONE. 2019;14(8):e0220606. https://doi.org/10.1371/journal.pone.0220606.

    Article  CAS  Google Scholar 

  6. Kikutani K, Ohshimo S, Sadamori T, et al. A novel system that continuously visualizes and analyzes respiratory sounds to promptly evaluate upper airway abnormalities: a pilot study. J Clin Monit Comput. 2021. https://doi.org/10.1007/s10877-020-00641-5.

    Article  Google Scholar 

  7. Horimasu Y, Ohshimo S, Yamaguchi K, et al. A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: a proof-of-concept study. Medicine (Baltimore). 2021;100(7):e24738. https://doi.org/10.1097/MD.0000000000024738.

    Article  Google Scholar 

  8. Nishikimi M, Yagi T, Shoaib M, et al. Phospholipid screening postcardiac arrest detects decreased plasma lysophosphatidylcholine: supplementation as a new therapeutic approach. Crit Care Med. 2021;50(2):e199–208. https://doi.org/10.1097/ccm.0000000000005180.

    Article  Google Scholar 

  9. Nabi FG, Sundaraj K, Lam CK, Palaniappan R. Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features. Comput Biol Med. 2019;104:52–61. https://doi.org/10.1016/j.compbiomed.2018.10.035.

    Article  Google Scholar 

  10. Enseki M, Nukaga M, Tadaki H, et al. A breath sound analysis in children with cough variant asthma. Allergol Int. 2019;68(1):33–8. https://doi.org/10.1016/j.alit.2018.05.003.

    Article  Google Scholar 

  11. Zhou L, Marzbanrad F, Ramanathan A, Fattahi D, Pharande P, Malhotra A. Acoustic analysis of neonatal breath sounds using digital stethoscope technology. Pediatr Pulmonol. 2020;55(3):624–30. https://doi.org/10.1002/ppul.24633.

    Article  Google Scholar 

  12. Ramanathan A, Marzbanrad F, Tan K, et al. Assessment of breath sounds at birth using digital stethoscope technology. Eur J Pediatr. 2020;179(5):781–9. https://doi.org/10.1007/s00431-019-03565-8.

    Article  Google Scholar 

  13. Mohamed N, Kim HS, Kang KM, Mohamed M, Kim SH, Kim JG. Heart and lung sound measurement using an esophageal Stethoscope with adaptive noise cancellation. Sensors (Basel). 2021. https://doi.org/10.3390/s21206757.

    Article  Google Scholar 

  14. Moon YJ, Kim SH, Park YS, Kim JM, Hwang GS. Quantitative analysis of an intraoperative digitalized esophageal heart sound signal to speculate on perturbed cardiovascular function. J Clin Med. 2019. https://doi.org/10.3390/jcm8050715.

    Article  Google Scholar 

  15. Li SH, Lin BS, Tsai CH, Yang CT, Lin BS. Design of wearable breathing sound monitoring system for real-time wheeze detection. Sensors (Basel). 2017. https://doi.org/10.3390/s17010171.

    Article  Google Scholar 

  16. Jafarian K, Hassani K, Doyle DJ, et al. Color spectrographic respiratory monitoring from the external ear canal. Clin Sci (Lond). 2018;132(24):2599–607. https://doi.org/10.1042/CS20180748.

    Article  Google Scholar 

  17. Guler I, Polat H, Ergun U. Combining neural network and genetic algorithm for prediction of lung sounds. J Med Syst. 2005;29(3):217–31. https://doi.org/10.1007/s10916-005-5182-9.

    Article  Google Scholar 

  18. Kim Y, Hyon Y, Jung SS, et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep. 2021;11(1):17186. https://doi.org/10.1038/s41598-021-96724-7.

    Article  CAS  Google Scholar 

  19. Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respir Res. 2020;21(1):253. https://doi.org/10.1186/s12931-020-01523-9.

    Article  Google Scholar 

  20. Kikutani K, Ohshimo S, Sadamori T, et al. Regional respiratory sound abnormalities in pneumothorax and pleural effusion detected via respiratory sound visualization and quantification: case report. J Clin Monit Comput. 2022. https://doi.org/10.1007/s10877-022-00824-2.

    Article  Google Scholar 

  21. Kato H, Suzuki A, Nakajima Y, et al. A visual stethoscope to detect the position of the tracheal tube. Anesth Analg. 2009;109(6):1836–42. https://doi.org/10.1213/ANE.0b013e3181bb4967.

    Article  Google Scholar 

  22. Niu J, Shi Y, Cai M, et al. Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques. Bioinformatics. 2018;34(5):820–7. https://doi.org/10.1093/bioinformatics/btx652.

    Article  CAS  Google Scholar 

  23. Moon YJ, Bechtel AJ, Kim SH, Kim JW, Thiele RH, Blank RS. Detection of intratracheal accumulation of thick secretions by using continuous monitoring of respiratory acoustic spectrum: a preliminary analysis. J Clin Monit Comput. 2020;34(4):763–70. https://doi.org/10.1007/s10877-019-00359-z.

    Article  Google Scholar 

  24. Wittekamp BH, van Mook WN, Tjan DH, Zwaveling JH, Bergmans DC. Clinical review: post-extubation laryngeal edema and extubation failure in critically ill adult patients. Crit Care. 2009;13(6):233. https://doi.org/10.1186/cc8142.

    Article  Google Scholar 

  25. Bohadana A, Izbicki G, Kraman SS. Fundamentals of lung auscultation. N Engl J Med. 2014;370(8):744–51. https://doi.org/10.1056/NEJMra1302901.

    Article  CAS  Google Scholar 

  26. Alviar CL, Miller PE, McAreavey D, et al. Positive pressure ventilation in the cardiac intensive care unit. J Am Coll Cardiol. 2018;72(13):1532–53. https://doi.org/10.1016/j.jacc.2018.06.074.

    Article  Google Scholar 

  27. Braman SS, Davis SM. Wheezing in the elderly. Asthma and other causes. Clin Geriatr Med. 1986;2(2):269–83.

    Article  CAS  Google Scholar 

  28. Angerio AD, Kot PA. Pathophysiology of pulmonary edema. Crit Care Nurs Q. 1994;17(3):21–6. https://doi.org/10.1097/00002727-199411000-00004.

    Article  CAS  Google Scholar 

  29. Espinosa B, Llorens P, Gil V, et al. Prognosis of acute heart failure based on clinical data of congestion. Rev Clin Esp (Barc). 2021. https://doi.org/10.1016/j.rceng.2021.07.004.

    Article  Google Scholar 

  30. Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(08):1125–33. https://doi.org/10.5664/jcsm.7804.

    Article  Google Scholar 

  31. Reljin N, Reyes BA, Chon KH. Tidal volume estimation using the blanket fractal dimension of the tracheal sounds acquired by smartphone. Sensors (Basel). 2015;15(5):9773–90. https://doi.org/10.3390/s150509773.

    Article  Google Scholar 

  32. Chen G, de la Cruz I, Rodriguez-Villegas E. Automatic lung tidal volumes estimation from tracheal sounds. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1497–500. https://doi.org/10.1109/embc.2014.6943885.

    Article  Google Scholar 

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Acknowledgements

We thank Pioneer Corporation (Currently business transferred to Air Water Biodesign Inc.), Nihon Kohden Corporation, and Tokyo Denki University for developing the novel continuous visualization and analysis system for assessing respiratory sounds that was used in this study. We also thank Hideyuki Ohkubo, Yuji Shimizu, Ms Kasumi Ogata and Ms Atsuko Otani for their help and assistance with data acquisition and analysis. We thank Jane Charbonneau, DVM, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Funding

This work was supported by a Japan Agency for Medical Research and Development (AMED) Grant (20he1602002h0004).

Author information

Authors and Affiliations

  1. Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Kazuya Kikutani, Shinichiro Ohshimo, Takuma Sadamori, Shingo Ohki, Hiroshi Giga, Junki Ishii, Hiromi Miyoshi, Kohei Ota, Mitsuaki Nishikimi & Nobuaki Shime

Authors
  1. Kazuya Kikutani
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  2. Shinichiro Ohshimo
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  3. Takuma Sadamori
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  4. Shingo Ohki
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  5. Hiroshi Giga
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  6. Junki Ishii
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  7. Hiromi Miyoshi
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  8. Kohei Ota
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  9. Mitsuaki Nishikimi
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  10. Nobuaki Shime
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Contributions

KK collected the respiratory sounds data from the patients as well as their medical information and drafted the manuscript. SO contributed to the study conception, analyzed the data, and supervised the drafting of the manuscript. TS contributed to the study conception and constructed the project team. SO, HG, JI, HM, and KO collected the data from the patients. MN was contributed to the development of the predictive score and was responsible for the corresponding analysis. NS organized and supervised the entire project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shinichiro Ohshimo.

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The authors have no potential conflicts of interest to declare.

Ethical approval

This research was approved by our institutional ethics committee (Hiroshima University, approval number E-784-10).

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Consent for study participation was obtained from the patients or their closest relatives.

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Kikutani, K., Ohshimo, S., Sadamori, T. et al. Quantification of respiratory sounds by a continuous monitoring system can be used to predict complications after extubation: a pilot study. J Clin Monit Comput 37, 237–248 (2023). https://doi.org/10.1007/s10877-022-00884-4

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  • DOI: https://doi.org/10.1007/s10877-022-00884-4

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