Burd Randall S, Jang Tai S, Nair Satish S
Department of Surgery, Division of Pediatric Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA.
J Trauma. 2006 Apr;60(4):792-801. doi: 10.1097/01.ta.0000214589.02515.dd.
The purpose of this study was to develop a model that accurately predicts mortality among injured children based on components of the initial patient evaluation and that is generalizable to diverse acute care settings. Important predictive variables obtained in an emergency setting are frequently missing in even large national databases, limiting their effectiveness for developing predictions. In this study, a model predicting pediatric trauma mortality was developed using a national database and methods to handle missing data that may avoid biases that can occur restricting analyses to complete cases.
Records of pediatric patients included in the National Pediatric Trauma Registry (NPTR) between 1996 and 1999 were used as a training set in a logistic regression model to predict hospital mortality using vital signs, Glasgow Coma Scale (GCS) score, and intubation status. Multiple imputation was applied to handle missing data. The model was tested using independent data from the NPTR and National Trauma Data Bank (NTDB).
Complete case analysis identified only GCS-eye and intubation status as predictors of mortality. A model based on complete case analysis had good discrimination (c-index = 0.784) and excellent calibration (Hosmer-Lemeshow c-statistic, 6.8) (p > 0.05). Using multiple imputation, three additional predictors of mortality (systolic blood pressure, pulse, and GCS-motor) were identified and improved model performance was observed. The model developed using multiple imputation had excellent discrimination (c-index, 0.947-0.973) in both test datasets. Calibration was better in the NPTR testing set than in the NTDB (Hosmer-Lemeshow c-statistic, 9.2 for NPTR [p > 0.05] and 258.2 for NTDB [p < 0.05]). At a probability cutoff that minimized misclassification in the training set, the false-negative and false-negative rates of the model were better than those obtained with either the Revised Trauma Score (RTS) or Pediatric Trauma Score using data from the NPTR testing set. Although the false-positive rates were lower with the RTS using data from the NTDB, the false-negative rates of the proposed model and the RTS were similar in this test dataset.
Using multiple imputation to handle missing data, a model predicting pediatric trauma mortality was developed that compared favorably with existing trauma scores. Application of these methods may produce predictive trauma models that are more statistically reliable and applicable in clinical practice.
本研究的目的是开发一种模型，该模型基于初始患者评估的组成部分准确预测受伤儿童的死亡率，并且能够推广到不同的急性护理环境。在急诊环境中获得的重要预测变量在即使是大型国家数据库中也经常缺失，这限制了它们用于开发预测的有效性。在本研究中，使用国家数据库和处理缺失数据的方法开发了一种预测儿科创伤死亡率的模型，该方法可以避免因将分析限制于完整病例而可能产生的偏差。
将1996年至1999年纳入国家儿科创伤登记处（NPTR）的儿科患者记录用作逻辑回归模型中的训练集，以使用生命体征、格拉斯哥昏迷量表（GCS）评分和插管状态预测医院死亡率。应用多重填补法处理缺失数据。使用来自NPTR和国家创伤数据库（NTDB）的独立数据对该模型进行测试。
完整病例分析仅将GCS-眼睛和插管状态确定为死亡率的预测因素。基于完整病例分析的模型具有良好的区分度（c指数 = 0.784）和出色的校准度（Hosmer-Lemeshow c统计量，6.8）（p > 0.05）。使用多重填补法，确定了另外三个死亡率预测因素（收缩压、脉搏和GCS-运动），并观察到模型性能得到改善。在两个测试数据集中，使用多重填补法开发的模型都具有出色的区分度（c指数，0.947 - 0.973）。NPTR测试集中的校准度优于NTDB（Hosmer-Lemeshow c统计量，NPTR为9.2 [p > 0.05]，NTDB为258.2 [p < 0.05]）。在使训练集中错误分类最小化的概率截断值下，该模型的假阴性率和假阳性率优于使用NPTR测试集数据的修订创伤评分（RTS）或儿科创伤评分。尽管使用NTDB数据时RTS的假阳性率较低，但在该测试数据集中，所提出模型和RTS的假阴性率相似。
使用多重填补法处理缺失数据，开发了一种预测儿科创伤死亡率的模型，该模型与现有的创伤评分相比具有优势。应用这些方法可能会产生在统计学上更可靠且适用于临床实践的预测性创伤模型。
