Medical School of Chinese People's Liberation Army, No. 28, Fuxing Road, Beijing 100853, China.
Department of Emergency, The First Medical Center to Chinese People's Liberation Army General Hospital, Beijing 100853, China.
Chin Med J (Engl). 2020 Mar 5;133(5):583-589. doi: 10.1097/CM9.0000000000000675.
Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology.
A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC).
The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure.
The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.
发热是急诊患者最常见的主诉。早期识别出死亡风险增加的患者，可能避免不良结局。本研究旨在通过大数据技术提取关键指标，建立发热患者致命不良预后的早期预测模型。
采用中国人民解放军总医院急诊急救数据库进行回顾性研究。患者分为预后不良组和预后良好组。比较常用的临床指标。采用递归特征消除（RFE）方法确定纳入变量的最佳数量。在训练模型中，选择逻辑回归、随机森林、自适应增强和袋装法。我们还收集了 2018 年 12 月至 2019 年 12 月的急诊室数据，纳入和排除标准相同。通过准确性、F1 分数、精度、敏感性和接受者操作特征曲线（ROC-AUC）下的面积来评估模型的性能。
逻辑回归、决策树、自适应增强和袋装法的准确性分别为 0.951、0.928、0.924 和 0.924，F1 分数分别为 0.938、0.933、0.930 和 0.930，精度分别为 0.943、0.938、0.937 和 0.937，ROC-AUC 分别为 0.808、0.738、0.736 和 0.885。逻辑回归和袋装模型的十折交叉验证 ROC-AUC 分别为 0.80 和 0.87。逻辑回归中变量的前六个系数和优势比（OR）值为肌钙蛋白 T（CTnT）（系数=0.346，OR=1.413）、温度（T）（系数=0.235，OR=1.265）、呼吸频率（RR）（系数=-0.206，OR=0.814）、血清钾（K）（系数=0.137，OR=1.146）、脉搏血氧饱和度（SPO2）（系数=-0.101，OR=0.904）和白蛋白（ALB）（系数=-0.043，OR=0.958）。袋装模型中前六个变量的权重为：CTnT、RR、乳酸脱氢酶、血清淀粉酶、心率和收缩压。
主要关注的临床指标包括 CTnT、RR、SPO2、T、ALB 和 K。袋装模型和逻辑回归模型的诊断性能综合较好。这些可能有助于医生早期识别发热的危急患者。
