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

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.