Abstract
Purpose
Hybrid operating rooms benefit patients with severe trauma but have a prerequisite of significant resources. This paper proposes a practical triage method to determine patients that should enter the hybrid operating room considering a limited availability of medical resources.
Methods
This retrospective observational study was conducted using the database from the Japan Trauma Data Bank comprising information collected between January 2004 and December 2018. A machine-learning-based triage algorithm was developed using the baseline demographics, injury mechanisms, and vital signs obtained from the database. The analysis dataset comprised information regarding 117,771 trauma patients with an abbreviated injury scale (AIS) > 3. The performance of the proposed model was compared against those of other statistical models [logistic regression and classification and regression tree (CART) models] while considering the status quo entry condition (systolic blood pressure < 90 mmHg).
Results
The proposed trauma hybrid-suite entry algorithm (THETA) outperformed other pre-existing algorithms [precision–recall area under the curve: THETA (0.59), logistic regression model (0.22), and classification and regression tree (0.20)].
Conclusion
A machine-learning-based algorithm was developed to triage patient entry into hybrid operating rooms. Although the validation in a prospective multicentre arrangement is warranted, the proposed algorithm could be a potentially useful tool in clinical practice.
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Acknowledgements
We would like to thank Editage (http://www.editage.com) for English language editing.
Funding
The study was funded by Japan Society for the Promotion of Science (Grant No. 21K16585).
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AS and EA contributed to the acquisition of data, while AS, EA, and TK jointly conceived of and designed this study. AS and TK jointly analysed and interpreted the data; EA confirmed the analysis, whereas EA and YO drafted the manuscript. All authors reviewed and discussed the manuscript. EA and TK revised the manuscript for important intellectual content. All the authors read and approved the final manuscript.
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68_2022_2002_MOESM1_ESM.pdf
Supplementary file1 Supplementary file1 Supplemental Fig. 1 Schematic of the proposed machine-learning model (PDF 99 KB)
Supplementary file3 Supplemental Movie Screen recording demonstrating use of the proposed web application (MOV 6535 KB)
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Senda, A., Endo, A., Kinoshita, T. et al. Development of practical triage methods for critical trauma patients: machine-learning algorithm for evaluating hybrid operation theatre entry of trauma patients (THETA). Eur J Trauma Emerg Surg 48, 4755–4760 (2022). https://doi.org/10.1007/s00068-022-02002-0
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DOI: https://doi.org/10.1007/s00068-022-02002-0