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The prediction of the survival in patients with severe trauma during prehospital care: Analyses based on NTDB database

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European Journal of Trauma and Emergency Surgery Aims and scope Submit manuscript

Abstract

Purpose

Traumas cause great casualties, accompanied by heavy economic burdens every year. The study aimed to use ML (machine learning) survival algorithms for predicting the 8-and 24-hour survival of severe traumas.

Methods

A retrospective study using data from National Trauma Data Bank (NTDB) was conducted. Four ML survival algorithms including survival tree (ST), random forest for survival (RFS) and gradient boosting machine (GBM), together with a Cox proportional hazard model (Cox), were utilized to develop the survival prediction models. Following this, model performance was determined by the comparison of the C-index, integrated Brier score (IBS) and calibration curves in the test datasets.

Results

A total of 191,240 individuals diagnosed with severe trauma between 2015 and 2018 were identified. Glasgow Coma Scale (GCS), trauma type, age, SaO2, respiratory rate (RR), systolic blood pressure (SBP), EMS transport time, EMS on-scene time, pulse, and EMS response time were identified as the main predictors. For predicting the 8-hour survival with the complete cases, the C-indexes in the test sets were 0.853 (0.845, 0.861), 0.823 (0.812, 0.834), 0.871 (0.862, 0.879) and 0.857 (0.849, 0.865) for Cox, ST, RFS and GBM, respectively. Similar results were observed in the 24-hour survival prediction models. The prediction error curves based on IBS also showed a similar pattern for these models. Additionally, a free web-based calculator was developed for potential clinical use.

Conclusion

The RFS survival algorithms provide non-parametric alternatives to other regression models to be of clinical use for estimating the survival probability of severe trauma patients.

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Data availability

Publicly available datasets were analyzed in this study. These data can be found here: https://www.facs.org/quality-programs/trauma/quality/national-trauma-data-bank.

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Acknowledgements

Not applicable.

Funding

This study was supported by the San Hang Program of Naval Medical University and Clinical Research Program of Shanghai Municipal Health Commission (202340037).

Author information

Author notes

  1. Chi Peng, Liwei Peng, Fan Yang and Hang Yu contributed equally to this work and are co-first authors.

Authors and Affiliations

  1. Department of Health Statistics, Naval Military Medical University, No. 800 Xiangyin Road, Shanghai, 200433, China

    Chi Peng MPH, Qi Chen PhD, Yibin Guo PhD & Zhichao Jin PhD

  2. Department of Neurosurgery, Tangdu Hospital, Air Force Medical University, Xi’an, 710038, China

    Liwei Peng MD

  3. Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology, Chongqing, 400014, China

    Fan Yang MD

  4. Department of Emergency, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, China

    Hang Yu MD & Shuogui Xu MD, PhD

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  1. Chi Peng MPH
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Contributions

Shuogui xu and Zhichao Jin contributed to the conception and design; Chi Peng, Hang Yu, Qi Chen and Yibin Guo analyzed and interpreted of data; Fan Yang, Chi Peng and Liwei Peng drafted the manuscript or revised it critically for important intellectual content: Chi Peng performed the statistical analysis; All authors read and approved the final manuscript

Corresponding authors

Correspondence to Shuogui Xu MD, PhD or Zhichao Jin PhD.

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Ethics approval and consent to participate

The study protocol was approved and the requirement for informed consent from the participants was waived by the Institutional Review Board of Naval Medical University.

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Not applicable.

Competing interests

None declared.

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Peng, C., Peng, L., Yang, F. et al. The prediction of the survival in patients with severe trauma during prehospital care: Analyses based on NTDB database. Eur J Trauma Emerg Surg (2024). https://doi.org/10.1007/s00068-024-02484-0

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