From the Baylor University Medical Center (A.C., J.W.) Department of Surgery, Trauma Division, Dallas, Texas; Johns Hopkins Bloomberg School of Public Health (S.B.), Department of Health Policy and Management, Baltimore, Maryland; University of Massachusetts School of Public Health and Health Sciences, Division of Biostatistics and Epidemiology (D.H.), Amherst, Massachusetts; University of Rochester School of Medicine (L.G.), Department of Anesthesiology, Rochester, New York; University of Illinois at Chicago (L.F.), School of Public Health, Chicago, Illinois; University of Vermont College of Medicine (T.O.), Department of Surgery, Burlington, Vermont.
J Trauma Acute Care Surg. 2014 Jan;76(1):47-52; discussion 52-3. doi: 10.1097/TA.0b013e3182ab0d5d.
Performance benchmarking requires accurate measurement of injury severity. Despite its shortcomings, the Injury Severity Score (ISS) remains the industry standard 40 years after its creation. A new severity measure, the Trauma Mortality Prediction Model (TMPM), uses either the Abbreviated Injury Scale (AIS) or DRG International Classification of Diseases-9th Rev. (ICD-9) lexicons and may better quantify injury severity compared with ISS. We compared the performance of TMPM with ISS and other measures of injury severity in a single cohort of patients.
We included 337,359 patient records with injuries reliably described in both the AIS and the ICD-9 lexicons from the National Trauma Data Bank. Five injury severity measures (ISS, maximum AIS score, New Injury Severity Score [NISS], ICD-9-Based Injury Severity Score [ICISS], TMPM) were computed using either the AIS or ICD-9 codes. These measures were compared for discrimination (area under the receiver operating characteristic curve), an estimate of proximity to a model that perfectly predicts the outcome (Akaike information criterion), and model calibration curves.
TMPM demonstrated superior receiver operating characteristic curve, Akaike information criterion, and calibration using either the AIS or ICD-9 lexicons. Calibration plots demonstrate the monotonic characteristics of the TMPM models contrasted by the nonmonotonic features of the other prediction models.
Severity measures were more accurate with the AIS lexicon rather than ICD-9. NISS proved superior to ISS in either lexicon. Since NISS is simpler to compute, it should replace ISS when a quick estimate of injury severity is required for AIS-coded injuries. Calibration curves suggest that the nonmonotonic nature of ISS may undermine its performance. TMPM demonstrated superior overall mortality prediction compared with all other models including ISS whether the AIS or ICD-9 lexicons were used. Because TMPM provides an absolute probability of death, it may allow clinicians to communicate more precisely with one another and with patients and families.
Disagnostic study, level I; prognostic study, level II.
绩效基准测试需要准确测量伤害严重程度。尽管存在缺陷，但在创建 40 年后，损伤严重度评分（ISS）仍然是行业标准。一种新的严重程度衡量标准，创伤死亡率预测模型（TMPM），使用简略损伤评分（AIS）或 DRG 国际疾病分类-9 修订版（ICD-9）词库，与 ISS 相比，可能更好地量化损伤严重程度。我们在单一患者队列中比较了 TMPM 与 ISS 和其他损伤严重程度衡量标准的性能。
我们纳入了来自国家创伤数据库的 337359 份患者记录，这些记录可靠地描述了 AIS 和 ICD-9 词库中的损伤。使用 AIS 或 ICD-9 编码计算了 5 种损伤严重程度衡量标准（ISS、最大 AIS 评分、新损伤严重度评分（NISS）、ICD-9 基础损伤严重度评分（ICISS）、TMPM）。比较这些指标的判别能力（接受者操作特征曲线下面积）、接近完美预测结果的模型的估计值（Akaike 信息准则）和模型校准曲线。
使用 AIS 或 ICD-9 词库时，TMPM 显示出更好的接受者操作特征曲线、Akaike 信息准则和校准。校准图显示了 TMPM 模型的单调特征，与其他预测模型的非单调特征形成对比。
使用 AIS 词库时，严重程度衡量标准更准确，而不是 ICD-9。在任何词库中，NISS 都优于 ISS。由于 NISS 计算更简单，因此在需要 AIS 编码损伤的快速损伤严重程度估计时，它应该替代 ISS。校准曲线表明，ISS 的非单调性质可能会影响其性能。与包括 ISS 在内的所有其他模型相比，TMPM 显示出对总体死亡率的更好预测能力，无论使用 AIS 还是 ICD-9 词库。由于 TMPM 提供了死亡的绝对概率，它可以使临床医生更精确地相互交流，以及与患者和家属交流。
诊断研究，一级；预后研究，二级。
