Rutledge R
North Carolina Trauma Registry, University of North Carolina School of Medicine, Chapel Hill, USA.
J Trauma. 1995 Apr;38(4):590-7; discussion 597-601. doi: 10.1097/00005373-199504000-00022.
Accurate assessment of injury severity is critical for decision making related to the prevention, triage, and treatment of injured patients. Presently, the standard method of controlling for variations of injury severity between groups has been based upon the Injury Severity Score (ISS) and the Trauma Score and the Trauma and Injury Severity Score (TRISS) methodology. The purpose of this study was to attempt to build upon previous work using International Classification of Diseases, ninth revision (ICD-9) coded diagnosis, and procedure information available from standard hospital discharge abstracts (UB-82 Billing format) to create a hierarchical network to provide a tool for predicting injury severity and probability of survival.
Data were obtained for this analysis from the North Carolina Medical Database. Data were available on all trauma patients admitted to hospitals in North Carolina from January 1, 1988 until June 30, 1992. The dependent variable of interest was the patient's survival after injury, coded as live or die. The independent variables used in the study included the ISS derived using the technique described by MacKenzie Abbreviated Injury Score (AIS) and body system maximum AIS scores, mortality risk ratios derived from the ICD-9-DM primary, secondary, and tertiary diagnoses, primary and secondary procedures as described in previous work, age and gender. Network generation used a commercial software package, AIM (Abtech Corp., Charlottesville, Va.), which is a numeric modeling tool that automatically "learns" knowledge from a data base of examples.
In the test data set an ISS and a prediction of survival based upon the derived network were calculated for each and every patient. The relative predictive power of these two scores were compared by calculating the overall accuracy, sensitivity, and specificity and the false positive and false negative rates. The receiver operator characteristic curves demonstrate that the network is a more effective tool in predicting the outcome of trauma patients. All the measures of predictive power show that the network was the better predictor of outcome than the ISS.
Given the recognized limitations of the ISS, the widespread availability of the ICD-9 coded diagnoses and procedures, and the availability of many state and regional data bases that have no ISS or Trauma Score, the purpose of this study was to assess the ability of a network derived from limited but widely available hospital discharge data to predict the outcome of injured patients. The study confirms previous work showing that the ICD-9 codes were strongly associated with outcome. The study demonstrated that the network created from these data was a better predictor of outcome than the derived ISS. When the results of the network were compared with other published series, the network, created without access to physiologic information, was almost as accurate, sensitive, and specific as reported values for TRISS and A Severity Characterization of Trauma (ASCOT). Because the present study is the first of its type, further investigations are needed to validate these findings. If other studies corroborate this study, a network model based upon ICD-9 codes could become the principal method for grading injury severity. This would provide superior predictive power of injury severity with important cost savings and universal application.
准确评估损伤严重程度对于受伤患者的预防、分诊及治疗决策至关重要。目前，控制组间损伤严重程度差异的标准方法基于损伤严重度评分（ISS）、创伤评分以及创伤和损伤严重度评分（TRISS）方法。本研究的目的是尝试在以往工作基础上，利用国际疾病分类第九版（ICD - 9）编码诊断以及标准医院出院摘要（UB - 82计费格式）中的程序信息，创建一个层次网络，以提供预测损伤严重程度和生存概率的工具。
本分析的数据取自北卡罗来纳医学数据库。数据涵盖了1988年1月1日至1992年6月30日期间北卡罗来纳州所有入院的创伤患者。感兴趣的因变量是患者受伤后的生存情况，编码为存活或死亡。研究中使用的自变量包括采用麦肯齐简化损伤评分（AIS）技术得出的ISS以及身体系统的最大AIS评分、从ICD - 9 - DM初级、次级和三级诊断得出的死亡风险比、如以往工作所述的初级和次级程序、年龄及性别。网络生成使用了一个商业软件包AIM（Abtech公司，弗吉尼亚州夏洛茨维尔），这是一个数字建模工具，可从示例数据库中自动“学习”知识。
在测试数据集中，为每位患者计算了ISS以及基于导出网络的生存预测。通过计算总体准确性、敏感性、特异性以及假阳性和假阴性率，比较了这两个评分的相对预测能力。受试者工作特征曲线表明，该网络在预测创伤患者的结局方面是更有效的工具。所有预测能力的指标均显示，网络比ISS更能准确预测结局。
鉴于ISS存在公认的局限性，ICD - 9编码诊断和程序广泛可用，且许多州和地区数据库没有ISS或创伤评分，本研究的目的是评估从有限但广泛可用的医院出院数据导出的网络预测受伤患者结局的能力。该研究证实了以往的工作，表明ICD - 9编码与结局密切相关。研究表明，从这些数据创建的网络比导出的ISS更能准确预测结局。当将该网络的结果与其他已发表系列进行比较时，在未获取生理信息的情况下创建的网络几乎与TRISS和创伤严重度特征化（ASCOT）报告值一样准确、敏感和特异。由于本研究是同类研究中的首个，需要进一步调查以验证这些发现。如果其他研究证实了本研究，基于ICD - 9编码的网络模型可能会成为分级损伤严重程度的主要方法。这将提供更高的损伤严重程度预测能力，同时显著节省成本并具有普遍适用性。
