Glance Laurent G, Osler Turner M, Mukamel Dana B, Meredith Wayne, Wagner Jacob, Dick Andrew W
Department of Anesthesiology, University of Rochester School of Medicine, Rochester, New York 14642, USA.
Ann Surg. 2009 Jun;249(6):1032-9. doi: 10.1097/SLA.0b013e3181a38f28.
To develop and validate a new ICD-9 injury model that uses regression modeling, as opposed to a simple ratio measurement, to estimate empiric injury severities for each of the injuries in the ICD-9-CM lexicon.
The American College of Surgeons now requires International Classification of diseases ninth Edition (ICD-9-CM) codes for injury coding in the National Trauma Databank. International Classification of diseases ninth Edition Injury Severity Score (ICISS) is the best-known risk-adjustment model when injuries are recorded using ICD-9-CM coding, and would likely be used to risk-adjust outcome measures for hospital trauma report cards. ICISS, however, has been criticized for its poor calibration.
We developed and validated a new ICD-9 injury model using data on 749,374 patients admitted to 359 hospitals in the National Trauma Databank (version 7.0). Empiric measures of injury severity for each of the trauma ICD-9-CM codes were estimated using a regression-based approach, and then used as the basis for a new Trauma Mortality Prediction Model (TMPM-ICD9). ICISS and the Single-Worst Injury (SWI) model were also re-estimated. The performance of each of these models was compared using the area under the receiver operating characteristic (ROC), the Hosmer-Lemeshow statistic, and the Akaike information criterion statistic.
TMPM-ICD9 exhibits significantly better discrimination (ROCTMPM = 0.880 [0.876-0.883]; ROCICISS = 0.850 [0.846-0.855]; ROCSWI = 0.862 [0.858-0.867]) and calibration (HLTMPM = 29.3 [12.1-44.1]; HLICISS = 231 [176-279]; HLSWI = 462 [380-548]) compared with both ICISS and the Single Worst Injury model. All models were improved with the addition of age, gender, and mechanism of injury, but TMPM-ICD9 continued to demonstrate superior model performance.
Because TMPM-ICD9 uniformly out-performs ICISS and the SWI model, it should be used in preference to ICISS for risk-adjusting trauma outcomes when injuries are recorded using ICD9-CM codes.
开发并验证一种新的ICD - 9损伤模型,该模型使用回归建模而非简单的比率测量方法,来估计ICD - 9 - CM词汇表中每种损伤的经验性损伤严重程度。
美国外科医师学会现在要求在国家创伤数据库中使用国际疾病分类第九版(ICD - 9 - CM)代码进行损伤编码。当使用ICD - 9 - CM编码记录损伤时,国际疾病分类第九版损伤严重度评分(ICISS)是最著名的风险调整模型,并且可能会用于对医院创伤报告卡的结果指标进行风险调整。然而,ICISS因其校准不佳而受到批评。
我们使用国家创伤数据库(版本7.0)中359家医院收治的749374例患者的数据,开发并验证了一种新的ICD - 9损伤模型。使用基于回归的方法估计每个创伤ICD - 9 - CM代码的损伤严重程度的经验性测量值,然后将其用作新的创伤死亡率预测模型(TMPM - ICD9)的基础。还重新估计了ICISS和单一最严重损伤(SWI)模型。使用受试者工作特征曲线(ROC)下面积、Hosmer - Lemeshow统计量和Akaike信息准则统计量比较这些模型的性能。
与ICISS和单一最严重损伤模型相比,TMPM - ICD9表现出明显更好的区分度(ROCTMPM = 0.880 [0.876 - 0.883];ROCICISS = 0.850 [0.846 - 0.855];ROCSWI = 0.862 [0.858 - 0.867])和校准度(HLTMPM = 29.3 [12.1 - 44.1];HLICISS = 231 [176 - 279];HLSWI = 462 [380 - 548])。加入年龄、性别和损伤机制后,所有模型都有所改进,但TMPM - ICD9继续表现出卓越的模型性能。
由于TMPM - ICD9在整体上优于ICISS和SWI模型,当使用ICD9 - CM代码记录损伤时,在对创伤结果进行风险调整时,应优先使用TMPM - ICD9而非ICISS。