Development and validation of a novel predictive score for sepsis risk among trauma patients
Development and validation of a novel predictive score for sepsis risk among trauma patients
复制标题
创伤患者脓毒症风险的新型预测评分的开发和验证
DOI：
10.1186/s13017-019-0231-8
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发表时间：
2019-03-12
影响因子：
8
通讯作者：
Jiang, Jian-xin
中科院分区：
文献类型：
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作者：
Lu, Hong-xiang;Du, Juan;Jiang, Jian-xin
背景遭受重大创伤的患者经常会出现脓毒症等并发症。早期认识到创伤后脓毒症的高危患者是精确治疗的关键。材料与方法收集入院后24 h内6 84例创伤患者的临床和实验室变量，其中训练队列4 11例，验证队列2 73例。采用最小绝对收缩和选择算子(LASSO)技术识别影响创伤性脓毒症早期预测的变量。然后，我们根据套索分析中选择的变量，使用Logistic回归模型构建了创伤性脓毒症评分(TSS)。结果根据套索评分，筛选出7个变量(创伤严重程度评分、格拉斯哥昏迷评分、温度、心率、白蛋白、国际标准化比率、C反应蛋白)用于构建创伤严重度评分系统。结果表明，创伤后脓毒症的发生率随着TSS的增加而增加(训练队列的P趋势= 7.44 × 10−21，验证队列的P趋势= 1.16 × 10−13)。训练数据集和验证数据集的受试者工作特征(ROC)曲线下面积分别为0.799(0.757-0.837)和0.790(0.736-0.836)。我们的模型的判别能力优于单变量和序贯器官衰竭评估(SOFA)评分(P<； 0.001)。结论基于 数据，我们开发并验证了一种新的创伤患者脓毒症风险预测方法，该方法具有良好的区分力和校准能力。
BackgroundPatients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma.Materials and methodsClinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test.ResultsBased on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (Ptrend= 7.44 × 10−21for the training cohort andPtrend= 1.16 × 10−13for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757–0.837) and 0.790 (0.736–0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P< 0.001). Moreover, the TSS was well calibrated (P> 0.05).ConclusionsWe developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.