Development and validation of prediction models for prehospital triage of military trauma patients.

IF 1.4 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL Bmj Military Health Pub Date : 2024-11-02 DOI:10.1136/military-2023-002644
Robin D Lokerman, R van der Sluijs, J F Waalwijk, E J M M Verleisdonk, R A Haasdijk, M M van Deemter, L P H Leenen, M van Heijl
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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.

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军事创伤患者院前分流预测模型的开发与验证。
导言:无线传感器的引入将使军事护理人员能够持续远程评估/监测生命体征。需要建立预测模型来充分使用这些数据,并帮助军队医疗服务提供者做出现场决策,以优化院前分流和改善患者预后:方法:利用由八个紧急医疗服务机构和七个创伤地区的数据组成的前瞻性队列来开发和验证预测模型,以帮助军队医疗服务提供者做出院前分流决策。研究对象包括健康的(美国麻醉医师协会身体状况分类 1 或 2 级)入院成人创伤患者(年龄≥16 岁且≤50 岁),这些患者的创伤机制可能发生在军人身上,并由救护车从受伤现场送往医院。采用了全模型策略,包括院前预测因素,这些预测因素可通过无线传感器自动收集,或纳入可运行模型的个性化设备中。建立的模型可预测早期危急资源使用(ECRU)、严重头部损伤(简易损伤量表(AIS)≥4)、严重胸部损伤(AIS≥3)和严重内出血(失血量>20%)。从区分度和校准方面对模型性能进行了评估:利用 4625 名患者(80.0%)的数据开发了预测模型,并利用 1157 名患者(20.0%)的数据进行了验证。在验证队列中,这些模型对预测结果具有良好至卓越的判别性能,ECRU 的曲线下面积为 0.80(95% CI 0.76 至 0.84),严重头部损伤为 0.83(0.76 至 0.91),严重胸部损伤为 0.75(0.70 至 0.80),严重内出血为 0.85(0.78 至 0.93)。所有模型在验证队列中均显示出令人满意的校准效果:结论:所开发的模型可以可靠地预测模拟军事创伤人群的预后,并有可能为院前护理人员的分诊决策提供支持。
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Bmj Military Health
Bmj Military Health MEDICINE, GENERAL & INTERNAL-
CiteScore
3.10
自引率
20.00%
发文量
116
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