Department of Neurosurgery, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan.
Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan.
Int J Environ Res Public Health. 2018 Feb 6;15(2):277. doi: 10.3390/ijerph15020277.
In trauma patients, pancreatic injury is rare; however, if undiagnosed, it is associated with high morbidity and mortality rates. Few predictive models are available for the identification of pancreatic injury in trauma patients with elevated serum pancreatic enzymes. In this study, we aimed to construct a model for predicting pancreatic injury using a decision tree (DT) algorithm, along with data obtained from a population-based trauma registry in a Level I trauma center.
A total of 991 patients with elevated serum levels of amylase (>137 U/L) or lipase (>51 U/L), including 46 patients with pancreatic injury and 865 without pancreatic injury between January 2009 and December 2016, were allocated in a ratio of 7:3 to training (n = 642) or test (n = 269) sets. Using the data on patient and injury characteristics as well as laboratory data, the DT algorithm with Classification and Regression Tree (CART) analysis was performed based on the Gini impurity index, using the rpart function in the rpart package in R.
Among the trauma patients with elevated amylase or lipase levels, three groups of patients were identified as having a high risk of pancreatic injury, using the DT model. These included (1) 69% of the patients with lipase level ≥306 U/L; (2) 79% of the patients with lipase level between 154 U/L and 305 U/L and shock index (SI) ≥ 0.72; and (3) 80% of the patients with lipase level <154 U/L with abdomen injury, glucose level <158 mg/dL, amylase level <90 U/L, and neutrophil percentage ≥76%; they had all sustained pancreatic injury. With all variables in the model, the DT achieved an accuracy of 97.9% (sensitivity of 91.4% and specificity of 98.3%) for the training set. In the test set, the DT achieved an accuracy of 93.3%, sensitivity of 72.7%, and specificity of 94.2%.
We established a DT model using lipase, SI, and additional conditions (injury to the abdomen, glucose level <158 mg/dL, amylase level <90 U/L, and neutrophils ≥76%) as important nodes to predict three groups of patients with a high risk of pancreatic injury. The proposed decision-making algorithm may help in identifying pancreatic injury among trauma patients with elevated serum amylase or lipase levels.
在创伤患者中，胰腺损伤较为少见；然而，如果未被诊断，其会导致较高的发病率和死亡率。目前，仅有少数预测模型可用于识别血清胰腺酶升高的创伤患者中的胰腺损伤。本研究旨在使用决策树（DT）算法构建一种预测胰腺损伤的模型，该模型的数据来自一级创伤中心的基于人群的创伤登记处。
2009 年 1 月至 2016 年 12 月，共纳入 991 例血清淀粉酶（>137 U/L）或脂肪酶（>51 U/L）升高的患者，其中 46 例患者发生胰腺损伤，865 例患者未发生胰腺损伤。将患者按照 7:3 的比例分为训练集（n=642）和测试集（n=269）。使用患者和损伤特征以及实验室数据，基于基尼不纯度指数，通过 R 中的 rpart 包的 rpart 函数，使用分类和回归树（CART）分析，进行 DT 算法分析。
在淀粉酶或脂肪酶升高的创伤患者中，使用 DT 模型确定了三组胰腺损伤高危患者。这三组患者包括：（1）脂肪酶水平≥306 U/L 的患者中，有 69%的患者发生胰腺损伤；（2）脂肪酶水平为 154 U/L 至 305 U/L 且休克指数（SI）≥0.72 的患者中，有 79%的患者发生胰腺损伤；（3）脂肪酶水平<154 U/L、腹部损伤、血糖水平<158 mg/dL、淀粉酶水平<90 U/L、中性粒细胞百分比≥76%的患者中，有 80%的患者发生胰腺损伤。在模型中使用所有变量，DT 在训练集的准确性为 97.9%（敏感性为 91.4%，特异性为 98.3%）。在测试集中，DT 的准确性为 93.3%，敏感性为 72.7%，特异性为 94.2%。
我们使用脂肪酶、SI 和其他条件（腹部损伤、血糖水平<158 mg/dL、淀粉酶水平<90 U/L、中性粒细胞≥76%）作为重要节点，建立了一个 DT 模型，以预测三组胰腺损伤风险较高的患者。该决策算法有助于识别血清淀粉酶或脂肪酶升高的创伤患者中的胰腺损伤。
