Skip to main content
SpringerLink
Log in

The validation of chest tube management after lung resection surgery using a random forest classifier

  • Applications
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

After lung surgery, chest tubes and a pump are used to manage air leaks and fluid output drainage from the chest. The decision to remove or maintain chest tubes is based on drainage data collected from the digital pump which continuously monitors the patient. Follow-up data include time-stamped drainage events accompanied with corresponding chest X-ray reports and patient charts. We construct, test and validate a random forest classifier to support this clinical decision-making process by identifying patients who can have their chest tubes removed safely and timely during their follow-up. Intuitively, this problem can be modeled as a time-series fitted to monitoring data. However, we present a simpler and less demanding classifier constructed from follow-up data collected in specific, non-overlapping post-operative time frames of equal length. We hypothesize that after surgery, patients struggle to attain a stable status (favorable or adverse), which prevails after a period of discrepancies and inconsistencies in the data. We identify this time frame when the majority of patients achieve that stability. The benefits lie in improving classification performance while lowering the burden of data collection during patient treatment. We present chest tube management as a classification task performed in a sliding window over time during patient monitoring. Our results show that reliable predictions can be achieved early on in the process. Further, our classifier ensures that chest tubes that need to be maintained are not removed at the cost of potentially maintaining a few unnecessarily.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  2. Brunelli, A., Cassivi, S.D., Salati, M., Fibla, J., Pompili, C., Halgren, L.A., Wigle, D.A., Di Nunzio, L.: Digital measurements of air leak flow and intra-pleural pressures in the immediate postoperative period predict risk of prolonged air leak after pulmonary lobectomy. Eur. J. Cardio-Thoracic Surg. 39, 584–8 (2011)

    Article  Google Scholar 

  3. French, D.G., Dilena, M., LaPlante, S., Shamji, F., Sundaresan, S., Villeneuve, J., Seely, A., Maziak, D., Gilbert, S.: Optimizing postoperative care protocols in thoracic surgery: best evidence and new technology. J. Thorac. Dis. 8, S3-11 (2016)

    Google Scholar 

  4. French, D.G., Thompson, C., Gilbert, S.: Transition from multiple port to single port video-assisted thoracoscopic anatomic pulmonary resection: early experience and comparison of perioperative outcomes. Ann. Cardiothorac. Surg. 5, 92–9 (2016)

    Article  Google Scholar 

  5. Gilbert, S., McGuire, A.L., Maghera, S., Sundaresan, S.R., Seely, A.J., Maziak, D.E., Shamji, F.M., Villeneuve, P.J.: Randomized trial of digital versus analog pleural drainage in patients with or without a pulmonary air leak after lung resection. J. Thorac. Cardiovasc. Surg. 150, 1243–9 (2015)

    Article  Google Scholar 

  6. Gilbert, S., Maghera, S., Seely, A.J., Maziak, D.E., Shamji, F.M., Sundaresan, S.R., Villeneuve, P.J.: Identifying patients at higher risk of prolonged air leak after lung resection. Ann. Thorac. Surg. 102, 1674–9 (2016)

    Article  Google Scholar 

  7. Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(11), 29–36 (1982)

    Article  Google Scholar 

  8. John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345 (1995)

  9. Klement, W., Gilbert, S., Maziak, D.E., Seely, A.J.E., Shamji, F.M., Sundaresan, S.R., Villeneuve, P.J., Japkowicz, N.: Chest Tube Management After Lung Resection Surgery using a Classifier. In: IEEE International Conference on Data Science and Advanced Analytics, DSAA 432-441 (2019)

  10. Lazarus, D.R., Casal, R.F.: Persistent air leaks: a review with an emphasis on bronchoscopic management. J. Thorac. Dis. 9(11), 4660–4670 (2017). https://doi.org/10.21037/jtd.2017.10.122

    Article  Google Scholar 

  11. Ling, C.X., Huang, J., Zhang, H.: Auc: A better measure than accuracy in comparing learning algorithms. In: Canadian Conference on AI, 329-341 (2003)

  12. McGuire, A.L., Petrcich, W., Maziak, D.E., Shamji, F.M., Sundaresan, S.R., Seely, A.J.E., Gilbert, S.: Digital versus analogue pleural drainage phase 1: prospective evaluation of interobserver reliability in the assessment of pulmonary air leaks. Interact. Cardiovasc. Thorac. Surg. 21, 403–7 (2015)

    Article  Google Scholar 

  13. Mohapatra, S.K., Mohanty, M.N.: Big data analysis and classification of biomedical signal using random forest algorithm. New Paradigm in Decision Science and Management (pp. 217-224). Springer, Singapore (2020)

  14. Mohapatra, S.K., Swarnkar, T., Mohanty, M.N.: Design of Random Forest Algorithm Based Model for Tachycardia Detection. Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2018, Volume 1, 1082, 191 (2020)

  15. Pompili, C., Salati, M., Refai, M., Xiume, F., Sabbatini, A., Tiberi, M., Cregan, I., Brunelli, A.: Recurrent air leak soon after pulmonary lobectomy: an analysis based on an electronic airflow evaluation. Eur. J. Cardiothorac. Surg. 49 1091-4 discussion 1094.(2016)

  16. Provost, F., Fawcett, T.: Robust classifica- tion systems for imprecise environments. Mach. Learn. 42, 203–231 (2001)

  17. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA (1993)

  18. Rodríguez, M., Jiménez, M.F., Hernàndez, M., Novoa, N.M., Aranda, J., Varela, G.: Usefulness of conventional pleural drainage systems to predict the occurrence of prolonged air leak after anatomical pulmonary resection. Eur. J. Cardio-Thoracic. Surg. 48, 612–5 (2015)

    Article  Google Scholar 

  19. Seder, C.W., Basu, S., Ramsay, T., Rocco, G., Blackmon, S., Liptay, M.J., Gilbert, S.: A prolonged air leak score for lung cancer resection: an analysis of the society of thoracic surgeons general thoracic surgery database. Ann. Thorac. Surg. 108, 1478–83 (2019)

    Article  Google Scholar 

  20. Shintani, Y., Funaki, S., Ose, N., Kawamura, T., Kanzaki, R., Minami, M., Okumura, M.: Air leak pattern shown by digital chest drainage system predict prolonged air leakage after pulmonary resection for patients with lung cancer. J. Thorac. Dis. 10, 3714–21 (2018)

    Article  Google Scholar 

  21. Silber, J.H., Rosenbaum, P.R., Koziol, L.F., Sutaria, N., Marsh, R.R., Even-Shoshan, O.: Conditional length of stay. Health Serv. Res. 34(1 Pt 2), 349–363 (1999)

    Google Scholar 

  22. Sumner, M., Frank, E., Hall, M.: Speeding up logistic model tree induction. In: European conference on principles and practice of knowledge discovery in databases, 675-683 (2005)

  23. Takamochi, K., Imashimizu, K., Fukui, M., Maeyashiki, T., Suzuki, M., Ueda, T., Matsuzawa, H., Hirayama, S., Matsunaga, T., Oh, S., Suzuki, K.: Utility of objective chest tube management after pulmonary resection using a digital drainage system. Ann. Thorac. Surg. 1-3 (2017)

  24. Wang, S., Cao, J., Yu, P.S.: Deep learning for spatio-temporal data mining: a survey. IEEE Trans. Knowl. Data Eng. (2020). https://doi.org/10.1109/TKDE.2020.3025580

    Article  Google Scholar 

  25. Yeung, C., Ghazel, M., French, D., Japkowicz, N., Gottlieb, B., Maziak, D., Seely, A.J.E., Shamji, F., Sundaresan, S., Villeneuve, P.J., Gilbert, S.: Forecasting pulmonary air leak duration following lung surgery using transpleural airflow data from a digital pleural drainage device. Thorac. Dis. 10(Suppl 32), S3747–S3754 (2018)

    Article  Google Scholar 

  26. Zhu, R., Wang, Y., Liu, J.X., Dai, L.Y.: IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier. BMC Bioinform. 22(1), 1–17 (2021)

    Article  Google Scholar 

  27. Zou, Q., Xie, S., Lin, Z., Wu, M., Ju, Y.: Finding the best classification threshold in imbalanced classification. Big Data Res. 5, 2–8 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Dr. Jennifer Dawson for her contributions to this manuscript, and we thank the reviewers and program committee for their valuable feedback. We also express gratitude for financial support by the Innovation Grant from the Ministry of Health and Long-term Care Innovation and Ontario Medical Association (Grant No. TOH 15-001).

Author information

Authors and Affiliations

  1. Thoracic Surgery, The Ottawa Hospital, Ottawa, Canada

    Sebastien Gilbert, Virginia F. Resende, Donna E. Maziak, Andrew J. E. Seely, Farid M. Shamji, Sudhir R. Sundaresan & Patrick J. Villeneuve

  2. Faculty of Medicine, University of Ottawa, Ottawa, Canada

    Sebastien Gilbert, Virginia F. Resende, Donna E. Maziak, Andrew J. E. Seely, Farid M. Shamji, Sudhir R. Sundaresan & Patrick J. Villeneuve

  3. The Ottawa Hospital Research Institute, Ottawa, Canada

    William Klement, Sebastien Gilbert, Virginia F. Resende, Donna E. Maziak, Andrew J. E. Seely, Farid M. Shamji, Sudhir R. Sundaresan & Patrick J. Villeneuve

  4. Faculty of Computer Science, Dalhousie University, Halifax, Canada

    William Klement

  5. Department of Computer Science, American University, Washington, USA

    Nathalie Japkowicz

  6. The Ottawa Hospital, General Campus Ste 6363, 501 Smyth Rd, Ottawa, Ontario, K1H 8L6, Canada

    William Klement

Authors
  1. William Klement
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastien Gilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Virginia F. Resende
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Donna E. Maziak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew J. E. Seely
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Farid M. Shamji
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sudhir R. Sundaresan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Patrick J. Villeneuve
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nathalie Japkowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Conflict of interest

The authors have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This project received funding from an Innovation Grant awarded to Dr. Sebastien Gilbert by the Ministry of Health and Long-Term Care of Ontario, Academic Health Science Center Alternate Funding Plan through the Ottawa Hospital Academic Medical Organization.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Klement, W., Gilbert, S., Resende, V.F. et al. The validation of chest tube management after lung resection surgery using a random forest classifier. Int J Data Sci Anal 13, 251–263 (2022). https://doi.org/10.1007/s41060-021-00296-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-021-00296-8

Keywords

Search

Navigation