Laytin Adam D, Dicker Rochelle A, Gerdin Martin, Roy Nobhojit, Sarang Bhakti, Kumar Vineet, Juillard Catherine
Department of Surgery, Center for Global Surgical Studies, University of California, San Francisco, California.
Department of Public Health Sciences, Health Systems and Policy, Karolinska Institutet, Stockholm, Sweden.
J Surg Res. 2017 Jul;215:60-66. doi: 10.1016/j.jss.2017.03.032. Epub 2017 Apr 3.
In most low- and middle-income countries (LMICs), the resources to accurately quantify injury severity using traditional injury scoring systems are limited. Novel injury scoring systems appear to have adequate discrimination for mortality in LMIC contexts, but they have not been rigorously compared where traditional injury scores can be accurately calculated. To determine whether novel injury scoring systems perform as well as traditional ones in a HIC with complete and comprehensive data collection.
Data from an American level-I trauma registry collected 2008-2013 were used to compare three traditional injury scoring systems: Injury Severity Score (ISS); Revised Trauma Score (RTS); and Trauma Injury Severity Score (TRISS); and three novel injury scoring systems: Kampala Trauma Score (KTS); Mechanism, GCS, Age and Pressure (MGAP) score; and GCS, Age and Pressure (GAP) score. Logistic regression was used to assess the association between each scoring system and mortality. Standardized regression coefficients (β), Akaike information criteria, area under the receiver operating characteristics curve, and the calibration line intercept and slope were used to evaluate the discrimination and calibration of each model.
Among 18,746 patients, all six scores were associated with hospital mortality. GAP had the highest effect size, and KTS had the lowest median Akaike information criteria. Although TRISS discriminated best, the discrimination of KTS approached that of TRISS and outperformed GAP, MGAP, RTS, and ISS. MGAP was best calibrated, and KTS was better calibrated than RTS, GAP, ISS, or TRISS.
The novel injury scoring systems (KTS, MGAP, and GAP), which are more feasible to calculate in low-resource settings, discriminated hospital mortality as well as traditional injury scoring systems (ISS and RTS) and approached the discrimination of a sophisticated, data-intensive injury scoring system (TRISS) in a high-resource setting. Two novel injury scoring systems (KTS and MGAP) surpassed the calibration of TRISS. These novel injury scoring systems should be considered when clinicians and researchers wish to accurately account for injury severity. Implementation of these resource-appropriate tools in LMICs can improve injury surveillance, guiding quality improvement efforts, and supporting advocacy for resource allocation commensurate with the volume and severity of trauma.
在大多数低收入和中等收入国家（LMICs），使用传统损伤评分系统准确量化损伤严重程度的资源有限。新型损伤评分系统在LMICs环境中对死亡率似乎有足够的区分度，但在可以准确计算传统损伤评分的情况下，它们尚未得到严格比较。为了确定在一个数据收集完整且全面的高收入国家（HIC）中，新型损伤评分系统的表现是否与传统系统一样好。
使用2008 - 2013年从美国一级创伤登记处收集的数据，比较三种传统损伤评分系统：损伤严重度评分（ISS）；修订创伤评分（RTS）；创伤损伤严重度评分（TRISS）；以及三种新型损伤评分系统：坎帕拉创伤评分（KTS）；机制、格拉斯哥昏迷评分、年龄和血压（MGAP）评分；以及格拉斯哥昏迷评分、年龄和血压（GAP）评分。使用逻辑回归评估每个评分系统与死亡率之间的关联。标准化回归系数（β）、赤池信息准则、受试者工作特征曲线下面积以及校准线截距和斜率用于评估每个模型的区分度和校准度。
在18746例患者中，所有六个评分均与医院死亡率相关。GAP的效应量最高，KTS的赤池信息准则中位数最低。虽然TRISS的区分度最佳，但KTS的区分度接近TRISS，且优于GAP、MGAP、RTS和ISS。MGAP的校准度最佳，KTS的校准度优于RTS、GAP、ISS或TRISS。
新型损伤评分系统（KTS、MGAP和GAP）在资源匮乏环境中计算起来更可行，其对医院死亡率的区分度与传统损伤评分系统（ISS和RTS）相当，并且在高资源环境中接近一种复杂的、数据密集型损伤评分系统（TRISS）的区分度。两种新型损伤评分系统（KTS和MGAP）的校准度超过了TRISS。当临床医生和研究人员希望准确评估损伤严重程度时，应考虑这些新型损伤评分系统。在LMICs中实施这些适合资源情况的工具可以改善损伤监测，指导质量改进工作，并支持根据创伤的数量和严重程度进行资源分配的倡导。
