West T A, Rivara F P, Cummings P, Jurkovich G J, Maier R V
Department of Surgery, University of Texas Southwestern Medical Center, Dallas 75235-9158, USA.
J Trauma. 2000 Sep;49(3):530-40; discussion 540-1. doi: 10.1097/00005373-200009000-00022.
There have been several attempts to develop a scoring system that can accurately reflect the severity of a trauma patient's injuries, particularly with respect to the effect of the injury on survival. Current methodologies require unreliable physiologic data for the assignment of a survival probability and fail to account for the potential synergism of different injury combinations. The purpose of this study was to develop a scoring system to better estimate probability of mortality on the basis of information that is readily available from the hospital discharge sheet and does not rely on physiologic data.
Records from the trauma registry from an urban Level I trauma center were analyzed using logistic regression. Included in the regression were Internation Classification of Diseases-9th Rev (ICD-9CM) codes for anatomic injury, mechanism, intent, and preexisting medical conditions, as well as age. Two-way interaction terms for several combinations of injuries were also included in the regression model. The resulting Harborview Assessment for Risk of Mortality (HARM) score was then applied to an independent test data set and compared with Trauma and Injury Severity Score (TRISS) probability of survival and ICD-9-CM Injury Severity Score (ICISS) for ability to predict mortality using the area under the receiver operator characteristic curve.
The HARM score was based on analysis of 16,042 records (design set). When applied to an independent validation set of 15,957 records, the area under the receiver operator characteristic curve (AUC) for HARM was 0.9592. This represented significantly better discrimination than both TRISS probability of survival (AUC = 0.9473, p = 0.005) and ICISS (AUC = 0.9402, p = 0.001). HARM also had a better calibration (Hosmer-Lemeshow statistic [HL] = 19.74) than TRISS (HL = 55.71) and ICISS (HL = 709.19). Physiologic data were incomplete for 6,124 records (38%) of the validation set; TRISS could not be calculated at all for these records.
The HARM score is an effective tool for predicting probability of in-hospital mortality for trauma patients. It outperforms both the TRISS and ICD9-CM Injury Severity Score (ICISS) methodologies with respect to both discrimination and calibration, using information that is readily available from hospital discharge coding, and without requiring emergency department physiologic data.
人们多次尝试开发一种能准确反映创伤患者损伤严重程度的评分系统，特别是关于损伤对生存的影响。目前的方法在确定生存概率时需要不可靠的生理数据，并且没有考虑不同损伤组合的潜在协同作用。本研究的目的是开发一种评分系统，以便根据医院出院单中 readily available 的信息更好地估计死亡概率，且不依赖生理数据。
使用逻辑回归分析了一家城市一级创伤中心创伤登记处的记录。回归分析中纳入了国际疾病分类第九版临床修订本（ICD - 9CM）中关于解剖损伤、机制、意图和既往病史的编码以及年龄。回归模型中还纳入了几种损伤组合的双向交互项。然后将得到的哈博维尤死亡风险评估（HARM）评分应用于一个独立的测试数据集，并与创伤和损伤严重程度评分（TRISS）的生存概率以及 ICD - 9 - CM 损伤严重程度评分（ICISS）进行比较，以使用受试者工作特征曲线下面积来预测死亡能力。
HARM 评分基于对 16,042 条记录（设计集）的分析。当应用于 15,957 条记录的独立验证集时，HARM 的受试者工作特征曲线下面积（AUC）为 0.9592。这表明其区分能力明显优于 TRISS 的生存概率（AUC = 0.9473，p = 0.005）和 ICISS（AUC = 0.9402，p = 0.001）。HARM 的校准效果也优于 TRISS（Hosmer - Lemeshow 统计量[HL] = 19.74）和 ICISS（HL = 709.19）（TRISS 的 HL = 55.71）。验证集中有 6,124 条记录（38%）的生理数据不完整；对于这些记录根本无法计算 TRISS。
HARM 评分是预测创伤患者院内死亡概率的有效工具。在区分能力和校准方面，它优于 TRISS 和 ICD9 - CM 损伤严重程度评分（ICISS）方法，使用的是医院出院编码中 readily available 的信息，且不需要急诊科的生理数据。
