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Abstract
Introduction The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes.
Methods A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration.
Results Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort.
Conclusion The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.
- trauma management
- accident & emergency medicine
- orthopaedic & trauma surgery
- biotechnology & bioinformatics
Data availability statement
Data are available on reasonable request. Our prediction models are freely available and can be found in the online supplemental content. Models are provided in the supplementary material as JSON files with model parameters and binaries that can be loaded by the XGBoost packages in programming environments, such as R.
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- trauma management
- accident & emergency medicine
- orthopaedic & trauma surgery
- biotechnology & bioinformatics
Data availability statement
Data are available on reasonable request. Our prediction models are freely available and can be found in the online supplemental content. Models are provided in the supplementary material as JSON files with model parameters and binaries that can be loaded by the XGBoost packages in programming environments, such as R.
Footnotes
Collaborators Members of the Prehospital Trauma Triage Research Collaborative (PTTRC) are: Koen W W Lansink (ETZ Hospital Tilburg), Mariska A C de Jongh (Netwerk Acute Zorg Brabant), Dennisden Hartog (Erasmus University Medical Centre), Jens A Halm, Georgios F Giannakópoulos (Amsterdam University Medical Centre), Michael J R Edwards (Radboud University Medical Centre), Martijn Poeze (Maastricht University Medical Centre), Pierre M van Grunsven (Veiligheidsregio Gelderland-Zuid), Nancy WPL van der Waarden (Regionale Ambulance Voorziening Rotterdam-Rijnmond), Merel Willeboer (Regionale Ambulance Voorziening Zuid-Holland Zuid), Arjen Siegers (Regionale Ambulance Voorziening Ambulance Amsterdam-Amstelland, Regionale Ambulance Voorziening Zaanstreek-Waterland), Risco van Vliet (Regionale Ambulance Voorziening Brabant Midden-West-Noord), Rinske M Tuinema (Regionale Ambulance Voorziening Utrecht).
Contributors RDL, JFW, RvdS and MvH conceived and designed the study. RDL, RvdS, JFW and MvH obtained data for this study. All authors contributed to the data collection, statistical analysis and interpretation of the results. RDL drafted the manuscript, and all authors contributed substantially to its revision. RDL, RvdS and MvH are the guarantors of this paper.
Funding This study was partly funded by grants from the Netherlands Organization for Health Research and Development (80-84300-98-18555), the Innovation Fund Health Insurers (3383) and the Dutch Ministry of Defense (VitalsIQ 2.0).
Disclaimer The funding sources had no influence on the study design, data collection, statistical analysis, interpretation of data and writing of the report.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.