DiRusso S M, Sullivan T, Holly C, Cuff S N, Savino J
New York Medical College and Westchester Medical Center, Department of Surgery, Valhalla 10595, USA.
J Trauma. 2000 Aug;49(2):212-20; discussion 220-3. doi: 10.1097/00005373-200008000-00006.
To develop and validate an artificial neural network (ANN) for predicting survival of trauma patients based on standard prehospital variables, emergency room admission variables, and Injury Severity Score (ISS) using data derived from a regional area trauma system, and to compare this model with known trauma scoring systems.
The study was composed of 10,609 patients admitted to 24 hospitals comprising a seven-county suburban/rural trauma region adjacent to a major metropolitan area. The data was generated as part of the New York State trauma registry. Study period was from January 1993 through December 1996 (1993-1994: 5,168 patients; 1995: 2,768 patients; 1996: 2,673 patients).
A standard feed-forward back-propagation neural network was developed using Glasgow Coma Scale, systolic blood pressure, heart rate, respiratory rate, temperature, hematocrit, age, sex, intubation status, ICD-9-CM Injury E-code, and ISS as input variables. The network had a single layer of hidden nodes. Initial network development of the model was performed on the 1993-1994 data. Subsequent models were generated using the 1993, 1994, and 1995 data. The model was tested first on the 1995 and then on the 1996 data. The ANN model was tested against Trauma and Injury Severity Score (TRISS) and ISS using the receiver operator characteristic (ROC) area under the curve [ROC-A(z)], Lemeshow-Hosmer C-statistic, and calibration curves.
The ANN showed good clustering of the data, with good separation of nonsurvivors and survivors. The ROCA(z) was 0.912 for the ANN, 0.895 for TRISS, and 0.766 for ISS. The ANN exceeded TRISS with respect to calibration (Lemeshow-Hosmer C-statistic: 7.4 for ANN; 17.1 for TRISS). The prediction of survivors was good for both models. The ANN exceeded TRISS in nonsurvivor prediction.
An ANN developed for trauma patients using prehospital, emergency room admission data, and ISS gave good prediction of survival. It was accurate and had excellent calibration. This study expands our previous results developed at a single Level I trauma center and shows that an ANN model for predicting trauma deaths can be applied across hospitals with good results
利用来自区域创伤系统的数据，开发并验证一种基于标准院前变量、急诊室入院变量和损伤严重度评分（ISS）来预测创伤患者生存率的人工神经网络（ANN），并将该模型与已知的创伤评分系统进行比较。
该研究由10609名入住24家医院的患者组成，这些医院位于一个与大城市相邻的七县郊区/农村创伤区域。数据作为纽约州创伤登记处的一部分生成。研究期间为1993年1月至1996年12月（1993 - 1994年：5168名患者；1995年：2768名患者；1996年：2673名患者）。
使用格拉斯哥昏迷量表、收缩压、心率、呼吸频率、体温、血细胞比容、年龄、性别、插管状态、ICD - 9 - CM损伤E编码和ISS作为输入变量，开发了一个标准的前馈反向传播神经网络。该网络有一层隐藏节点。模型的初始网络开发基于1993 - 1994年的数据。随后的模型使用1993年、1994年和1995年的数据生成。该模型首先在1995年的数据上进行测试，然后在1996年的数据上进行测试。使用曲线下面积（ROC - A(z)）、Lemeshow - Hosmer C统计量和校准曲线，将人工神经网络模型与创伤和损伤严重度评分（TRISS）以及ISS进行比较。
人工神经网络显示出良好的数据聚类，非幸存者和幸存者之间有良好的区分。人工神经网络的ROC - A(z)为0.912，TRISS为0.895，ISS为0.766。在校准方面（Lemeshow - Hosmer C统计量：人工神经网络为7.4；TRISS为17.1），人工神经网络超过了TRISS。两种模型对幸存者的预测都很好。在非幸存者预测方面，人工神经网络超过了TRISS。
利用院前、急诊室入院数据和ISS为创伤患者开发的人工神经网络对生存率有良好的预测。它准确且校准良好。本研究扩展了我们之前在单一一级创伤中心取得的结果，表明用于预测创伤死亡的人工神经网络模型可以在各医院广泛应用并取得良好效果。
