Anaesthesia and ICU S.M.M. Hospital, Udine/Regional Health Agency of Emilia-Romagna, Bologna, Italy.
Scand J Trauma Resusc Emerg Med. 2011 Apr 19;19:26. doi: 10.1186/1757-7241-19-26.
Injury scoring is important to formulate prognoses for trauma patients. Although scores based on empirical estimation allow for better prediction, those based on expert consensus, e.g. the New Injury Severity Score (NISS) are widely used. We describe how the addition of a variable quantifying the number of injuries improves the ability of NISS to predict mortality.
We analyzed 2488 injury cases included into the trauma registry of the Italian region Emilia-Romagna in 2006-2008 and assessed the ability of NISS alone, NISS plus number of injuries, and the maximum Abbreviated Injury Scale (AIS) to predict in-hospital mortality. Hierarchical logistic regression was used. We measured discrimination through the C statistics, and calibration through Hosmer-Lemeshow statistics, Akaike's information criterion (AIC) and calibration curves.
The best discrimination and calibration resulted from the model with NISS plus number of injuries, followed by NISS alone and then by the maximum AIS (C statistics 0.775, 0.755, and 0.729, respectively; AIC 1602, 1635, and 1712, respectively). The predictive ability of all the models improved after inclusion of age, gender, mechanism of injury, and the motor component of Glasgow Coma Scale (C statistics 0.889, 0.898, and 0.901; AIC 1234, 1174, and 1167). The model with NISS plus number of injuries still showed the best performances, this time with borderline statistical significance.
In NISS, the same weight is assigned to the three worst injuries, although the contribution of the second and third to the probability of death is smaller than that of the worst one. An improvement of the predictive ability of NISS can be obtained adjusting for the number of injuries.
创伤评分对于制定创伤患者的预后非常重要。虽然基于经验估计的评分可以更好地进行预测，但基于专家共识的评分，如新损伤严重程度评分（NISS），则被广泛使用。我们描述了如何通过添加一个量化损伤数量的变量来提高 NISS 预测死亡率的能力。
我们分析了 2006-2008 年意大利艾米利亚-罗马涅地区创伤登记处纳入的 2488 例创伤病例，并评估了 NISS 单独、NISS 加损伤数量以及最大简明损伤评分（AIS）预测院内死亡率的能力。使用分层逻辑回归。我们通过 C 统计量衡量判别能力，通过 Hosmer-Lemeshow 统计量、Akaike 信息量准则（AIC）和校准曲线衡量校准能力。
NISS 加损伤数量模型的判别和校准效果最好，其次是 NISS 单独模型，然后是最大 AIS 模型（C 统计量分别为 0.775、0.755 和 0.729；AIC 分别为 1602、1635 和 1712）。纳入年龄、性别、损伤机制和格拉斯哥昏迷量表的运动成分后，所有模型的预测能力均有所提高（C 统计量分别为 0.889、0.898 和 0.901；AIC 分别为 1234、1174 和 1167）。NISS 加损伤数量模型仍显示出最佳性能，这次具有边缘统计学意义。
在 NISS 中，三个最严重的损伤赋予相同的权重，尽管第二和第三个损伤对死亡概率的贡献小于最严重的损伤。通过调整损伤数量，可以提高 NISS 的预测能力。
