Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia.
Department of Public Health, University of Virginia, Charlottesville, Virginia.
Traffic Inj Prev. 2021;22(sup1):S74-S81. doi: 10.1080/15389588.2021.1975275. Epub 2021 Oct 21.
Transporting severely injured pediatric patients to a trauma center has been shown to decrease mortality. A decision support tool to assist emergency medical services (EMS) providers with trauma triage would be both as parsimonious as possible and highly accurate. The objective of this study was to determine the minimum set of predictors required to accurately predict severe injury in pediatric patients.
Crash data and patient injuries were obtained from the NASS and CISS databases. A baseline multivariable logistic model was developed to predict severe injury in pediatric patients using the following predictors: age, sex, seat row, restraint use, ejection, entrapment, posted speed limit, any airbag deployment, principal direction of force (PDOF), change in velocity (delta-V), single vs. multiple collisions, and non-rollover vs. rollover. The outcomes of interest were injury severity score (ISS) ≥16 and the Target Injury List (TIL). Accuracy was measured by the cross-validation mean of the receiver operator curve (ROC) area under the curve (AUC). We used Bayesian Model Averaging (BMA) based on all subsets regression to determine the importance of each variable separately for each outcome. The AUC of the highest performing model for each number of variables was compared to the baseline model to assess for a statistically significant difference (p < 0.05). A reduced variable set model was derived using this information.
The baseline models performed well (ISS ≥ 16: AUC 0.91 [95% CI: 0.86-0.95], TIL: AUC 0.90 [95% CI: 0.86-0.94]). Using BMA, the rank of the importance of the predictors was identical for both ISS ≥ 16 and TIL. There was no statistically significant decrease in accuracy until the models were reduced to fewer than five and six variables for predicting ISS ≥ 16 and TIL, respectively. A reduced variable set model developed using the top five variables (delta-V, entrapment, ejection, restraint use, and near-side collision) to predict ISS ≥ 16 had an AUC 0.90 [95% CI: 0.84-0.96]. Among the models that did not include delta-V, the highest AUC was 0.82 [95% CI: 0.77-0.87].
A succinct logistic regression model can accurately predict severely injured pediatric patients, which could be used for prehospital trauma triage. However, there remains a critical need to obtain delta-V in real-time.
将严重受伤的儿科患者转运到创伤中心已被证明可以降低死亡率。一种用于协助紧急医疗服务（EMS）提供者进行创伤分诊的决策支持工具应尽可能简约且高度准确。本研究的目的是确定准确预测儿科患者严重损伤所需的最小预测因子集。
从 NASS 和 CISS 数据库中获取碰撞数据和患者损伤情况。使用以下预测因子开发了基线多变量逻辑模型，以预测儿科患者的严重损伤：年龄、性别、座位排、约束使用、弹射、被困、公布限速、任何安全气囊展开、主要力方向（PDOF）、速度变化（delta-V）、单次碰撞与多次碰撞、非翻车与翻车。感兴趣的结果是损伤严重程度评分（ISS）≥16 和目标损伤列表（TIL）。准确性通过交叉验证的接收器操作特征（ROC）曲线下面积（AUC）的均值进行测量。我们使用基于全子集回归的贝叶斯模型平均（BMA）来确定每个变量对于每个结果的重要性。与基线模型相比，比较每个变量数量最高的模型的 AUC，以评估是否存在统计学差异（p<0.05）。使用该信息推导出一个简化变量集模型。
基线模型表现良好（ISS≥16：AUC 0.91[95%CI：0.86-0.95]，TIL：AUC 0.90[95%CI：0.86-0.94]）。使用 BMA，对于 ISS≥16 和 TIL，预测因子的重要性排名相同。直到模型减少到预测 ISS≥16 和 TIL 的变量少于五和六个时，准确性才没有统计学显著下降。使用前五个变量（delta-V、被困、弹射、约束使用和近侧碰撞）开发的简化变量集模型预测 ISS≥16 的 AUC 为 0.90[95%CI：0.84-0.96]。在不包括 delta-V 的模型中，最高 AUC 为 0.82[95%CI：0.77-0.87]。
简洁的逻辑回归模型可以准确预测严重受伤的儿科患者，可用于院前创伤分诊。然而，仍然迫切需要实时获得 delta-V。
