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利用机器学习增强机构间分诊指南的实用性:老年患者机构间创伤分诊评分的制定。

Enhancing utility of interfacility triage guidelines using machine learning: Development of the Geriatric Interfacility Trauma Triage score.

机构信息

From the Department of Surgery (T.G., K.S., R.M.A.), University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Department of Biostatistics and Epidemiology (T.G., J.C., P.A.), University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Emergency Systems Division (Y.W.), Oklahoma State Department of Health, Oklahoma City, Oklahoma; Center for Policy and Research in Emergency Medicine (C.G.N.), Department of Emergency Medicine, Oregon Health and Science University, Portland, Oregon; and Norman Regional Health System (P.C.), Norman, Oklahoma.

出版信息

J Trauma Acute Care Surg. 2023 Apr 1;94(4):546-553. doi: 10.1097/TA.0000000000003846. Epub 2022 Nov 21.

DOI:10.1097/TA.0000000000003846
PMID:36404409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10038832/
Abstract

BACKGROUND

Undertriage of injured older adults to tertiary trauma centers (TTCs) has been demonstrated by many studies. In predominantly rural regions, a majority of trauma patients are initially transported to nontertiary trauma centers (NTCs). Current interfacility triage guidelines do not highlight the hierarchical importance of risk factors nor do they allow for individual risk prediction. We sought to develop a transfer risk score that may simplify secondary triage of injured older adults to TTCs.

METHODS

This was a retrospective prognostic study of injured adults 55 years or older initially transported to an NTC from the scene of injury. The study used data reported to the Oklahoma State Trauma Registry between 2009 and 2019. The outcome of interest was either mortality or serious injury (Injury Severity Score, ≥16) requiring an interventional procedure at the receiving facility. In developing the model, machine-learning techniques including random forests were used to reduce the number of candidate variables recorded at the initial facility.

RESULTS

Of the 5,913 injured older adults initially transported to an NTC before subsequent transfer to a TTC, 32.7% (1,696) had the outcome of interest at the TTC. The final prognostic model (area under the curve, 75.4%; 95% confidence interval, 74-76%) included the following top four predictors and weighted scores: airway intervention (10), traffic-related femur fracture (6), spinal cord injury (5), emergency department Glasgow Coma Scale score of ≤13 (5), and hemodynamic support (4). Bias-corrected and sample validation areas under the curve were 74% and 72%, respectively. A risk score of 7 yields a sensitivity of 78% and specificity of 56%.

CONCLUSION

Secondary triage of injured older adults to TTCs could be enhanced by use of a risk score. Our study is the first to develop a risk stratification tool for injured older adults requiring transfer to a higher level of care.

LEVEL OF EVIDENCE

Prognostic and Epidemiolgical; Level III.

摘要

背景

许多研究表明,受伤的老年患者分诊至三级创伤中心(TTC)的情况较为常见。在以农村为主的地区,大多数创伤患者最初被送往非三级创伤中心(NTC)。目前的院内分诊指南没有突出风险因素的层次重要性,也不允许进行个体风险预测。我们试图开发一种转移风险评分,以简化对受伤老年患者分诊至 TTC 的二次分诊。

方法

这是一项回顾性预后研究,纳入了从受伤现场最初被送往 NTC 的 55 岁及以上的受伤成年人。该研究使用了 2009 年至 2019 年期间向俄克拉荷马州创伤登记处报告的数据。研究的主要结局为死亡或严重损伤(损伤严重程度评分≥16),需要在接收机构进行介入性治疗。在构建模型时,使用了机器学习技术,包括随机森林,以减少初始机构记录的候选变量数量。

结果

在 5913 名最初被送往 NTC 随后转诊至 TTC 的受伤老年患者中,32.7%(1696 人)在 TTC 发生了研究结局。最终的预后模型(曲线下面积为 75.4%;95%置信区间为 74-76%)包括以下四个最重要的预测因子和加权评分:气道干预(10 分)、交通相关股骨骨折(6 分)、脊髓损伤(5 分)、急诊科格拉斯哥昏迷量表评分≤13(5 分)和血流动力学支持(4 分)。经过偏倚校正和样本验证的曲线下面积分别为 74%和 72%。风险评分 7 分的敏感性为 78%,特异性为 56%。

结论

使用风险评分可提高对 TTC 就诊的受伤老年患者的二次分诊。本研究首次为需要转至更高级别治疗的老年创伤患者开发了风险分层工具。

证据等级

预后和流行病学;三级。

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