University of Manchester, Faculty of Biology, Medicine and Health, School of Medical Sciences, Division of Cardiovascular Sciences, Oxford Road, Manchester, M13 9PL.
Centre for Urgent and Emergency Care Research, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.
Injury. 2021 May;52(5):1108-1116. doi: 10.1016/j.injury.2021.01.039. Epub 2021 Jan 28.
This paper investigates the use of a major trauma prediction model in the UK setting. We demonstrate that application of this model could reduce the number of patients with major trauma being incorrectly sent to non-specialist hospitals. However, more research is needed to reduce over-triage and unnecessary transfer to Major Trauma Centres.
To externally validate the Dutch prediction model for identifying major trauma in a large unselected prehospital population of injured patients in England.
External validation using a retrospective cohort of injured patients who ambulance crews transported to hospitals.
South West region of England.
All patients ≥16 years with a suspected injury and transported by ambulance in the year from February 1, 2017.
Tested the accuracy of the prediction model in terms of discrimination, calibration, clinical usefulness, sensitivity and specificity and under- and over triage rates compared to usual triage practices in the South West region.
Major trauma defined as an Injury Severity Score>15.
A total of 68799 adult patients were included in the external validation cohort. The median age of patients was 72 (i.q.r. 46-84); 55.5% were female; and 524 (0.8%) had an Injury Severity Score>15. The model achieved good discrimination with a C-Statistic 0.75 (95% CI, 0.73 - 0.78). The maximal specificity of 50% and sensitivity of 83% suggests the model could improve undertriage rates at the expense of increased overtriage rates compared with routine trauma triage methods used in the South West, England.
The Dutch prediction model for identifying major trauma could lower the undertriage rate to 17%, however it would increase the overtriage rate to 50% in this United Kingdom cohort. Further prospective research is needed to determine whether the model can be practically implemented by paramedics and is cost-effective.
本文研究了在英国使用一种主要创伤预测模型。我们证明,应用该模型可以减少将大量创伤患者错误送往非专科医院的数量。然而,还需要更多的研究来减少过度分诊和不必要的向重大创伤中心转移。
在英格兰一个大型、未选择的、院前受伤患者人群中,对荷兰创伤预测模型进行外部验证,以确定其是否能识别主要创伤。
使用受伤患者的回顾性队列进行外部验证,这些患者由救护人员送往医院。
英格兰西南部地区。
所有 16 岁以上有疑似损伤并由救护车送往医院的患者。
1)年龄≤15 岁的患者;2)非救护车在医院就诊的患者;3)现场死亡的患者;4)乘坐直升机的患者。本研究在一年的时间内对我们提供了一个病例的普查样本。
根据在英国西南部地区的常规分诊实践,测试该预测模型在准确性、校准、临床实用性、灵敏度和特异性以及分诊率过低和过高方面的准确性。
严重创伤定义为损伤严重程度评分>15。
共纳入 68799 例成年患者进行外部验证队列分析。患者的中位年龄为 72 岁(四分位距 46-84 岁);55.5%为女性;524 例(0.8%)的损伤严重程度评分>15。该模型具有良好的区分度,C 统计量为 0.75(95%置信区间,0.73-0.78)。模型的最大特异性为 50%,敏感性为 83%,表明与英国西南部常规创伤分诊方法相比,该模型可以提高低危患者的分诊率,同时增加高危患者的分诊率。
荷兰用于识别严重创伤的预测模型可以将低危患者的分诊率降低到 17%,但在英国的这个队列中,高危患者的分诊率会增加到 50%。还需要进一步的前瞻性研究来确定该模型是否可以由护理人员实际实施,并具有成本效益。