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Survival prediction of trauma patients: a study on US National Trauma Data Bank

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Abstract

Background

Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed.

Methods

In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank.

Results

Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools.

Conclusion

Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology.

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Notes

  1. The tool is available online at https://github.com/imanesefrioui/tool_prediction_survival_executable.

  2. In our case the learning rate was set to 0.3, the momentum to 0.2, the number of iterations to 500, and the hidden layer was composed of 9 nodes.

  3. We used the 3.7.12 version of WEKA.

  4. In WEKA this can be done with the “-M” option of the SMO classifier.

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Acknowledgements

This research work was carried out with the support of the Erasmus Mundus program of the European Union. We would also like to thank the Committee on Trauma, American College of Surgeons for providing us the National Trauma Data Bank used to conduct the experiments.

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Authors and Affiliations

  1. Faculty of Sciences of Tetouan, University Abdelmalek Essaadi, Tétouan, Morocco

    I. Sefrioui

  2. Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    R. Amadini

  3. Department of Informatics, University of Oslo, Oslo, Norway

    J. Mauro

  4. National School of Applied Sciences of Tetouan, University Abdelmalek Essaadi, Tétouan, Morocco

    A. El Fallahi

  5. Department of Computer Science, University of Bologna/Lab. Focus INRIA, Bologna, Italy

    M. Gabbrielli

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  1. I. Sefrioui
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Correspondence to I. Sefrioui.

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Imane Sefrioui, Roberto Amadini, Jacopo Mauro, Abdellah El Fallahi, and Maurizio Gabbrielli declare that they have no conflict of interest.

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Sefrioui, I., Amadini, R., Mauro, J. et al. Survival prediction of trauma patients: a study on US National Trauma Data Bank. Eur J Trauma Emerg Surg 43, 805–822 (2017). https://doi.org/10.1007/s00068-016-0757-3

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  • DOI: https://doi.org/10.1007/s00068-016-0757-3

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