Abstract：
OBJECTIVE: To construct a nomogram model for predicting the 28-day mortality of patients with septic shock in the emergency medicine department and to validate the predictive efficacy.
METHODS: Based on the database of the emergency medicine department of Chu Hsien-I Memorial Hospital of Tianjin Medical University, Tianjin Medical University General Hospital and the Second Hospital of Tianjin Medical University, the data of 913 patients with septic shock admitted to the emergency medicine department from January 2017 to October 2020 were collected, including baseline demographic information and clinical characteristics, laboratory indices, and the main endpoints (28-day mortality). The patients were divided into a training set and a validation set based on simple random sampling. All significant variables from the one-way binary Logistic regression analysis of the training set were included in the multivariate Logistic regression analysis to analyze the risk factors for 28-day mortality in patients with septic shock and to construct a column-line graphical model. The predictive efficacy of the nomogram model was assessed using calibration curves and receiver operator characteristic curve (ROC curve).
RESULTS: A total of 860 patients with septic shock meeting the criteria were finally enrolled, including 472 in the training set and 388 in the validation set. The 28-day mortalities were 52.5% (248/472) and 54.1% (210/388) for the training and validation sets, respectively. In the training set, age, respiratory rate (RR), the levels of C-reactive protein (CRP), D-dimer, white blood cell count (WBC), neutrophil count (NEU), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), mean platelet volume (MPV), and platelet count (PLT) in the death group were significantly higher than those in the survival group, and the levels of base remaining (BE), lymphocyte count (LYM), hemoglobin (Hb) and the proportion of chronic obstructive pulmonary diseases (COPD) were significantly lower than those in the survival group (all P < 0.05). Multifactorial Logistic regression analysis showed that NLR [odds ratio (OR) = 0.023 0, 95% confidence interval (95%CI) was -0.204 4 to 0.113 0], MPV (OR = 0.179 8, 95%CI was -0.877 6 to 0.172 7), Hb (OR = 0.007 8, 95%CI was 0.010 3 to 0.040 8), procalcitonin (PCT; OR = 1.957 0, 95%CI was 1.243 0 to 3.081 0), and D-dimer (OR = 0.000 1, 95%CI was -0.000 4 to 0.000 1) were independent predictors of 28-day mortality in patients with septic shock in the emergency department (all P < 0.05). A column-line graph model was established based on the above variables, and the ROC curves showed that the area under the ROC curve (AUC) of the nomogram model in the training set and validation set for predicting the 28-day mortality of patients with septic shock was 0.907 (95%CI was 0.864 to 0.940) and 0.822 (95%CI was 0.781 to 0.863), respectively. The calibration curves showed good agreement between the predicted and observed results for both the training and validation sets.
CONCLUSIONS: The nomogram model constructed based on NLR, MPV, Hb, PCT and D-dimer has significant clinical value in predicting the 28-day mortality of patients with septic shock in the emergency medicine department.
METHODS: Based on the database of the emergency medicine department of Chu Hsien-I Memorial Hospital of Tianjin Medical University, Tianjin Medical University General Hospital and the Second Hospital of Tianjin Medical University, the data of 913 patients with septic shock admitted to the emergency medicine department from January 2017 to October 2020 were collected, including baseline demographic information and clinical characteristics, laboratory indices, and the main endpoints (28-day mortality). The patients were divided into a training set and a validation set based on simple random sampling. All significant variables from the one-way binary Logistic regression analysis of the training set were included in the multivariate Logistic regression analysis to analyze the risk factors for 28-day mortality in patients with septic shock and to construct a column-line graphical model. The predictive efficacy of the nomogram model was assessed using calibration curves and receiver operator characteristic curve (ROC curve).
RESULTS: A total of 860 patients with septic shock meeting the criteria were finally enrolled, including 472 in the training set and 388 in the validation set. The 28-day mortalities were 52.5% (248/472) and 54.1% (210/388) for the training and validation sets, respectively. In the training set, age, respiratory rate (RR), the levels of C-reactive protein (CRP), D-dimer, white blood cell count (WBC), neutrophil count (NEU), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), mean platelet volume (MPV), and platelet count (PLT) in the death group were significantly higher than those in the survival group, and the levels of base remaining (BE), lymphocyte count (LYM), hemoglobin (Hb) and the proportion of chronic obstructive pulmonary diseases (COPD) were significantly lower than those in the survival group (all P < 0.05). Multifactorial Logistic regression analysis showed that NLR [odds ratio (OR) = 0.023 0, 95% confidence interval (95%CI) was -0.204 4 to 0.113 0], MPV (OR = 0.179 8, 95%CI was -0.877 6 to 0.172 7), Hb (OR = 0.007 8, 95%CI was 0.010 3 to 0.040 8), procalcitonin (PCT; OR = 1.957 0, 95%CI was 1.243 0 to 3.081 0), and D-dimer (OR = 0.000 1, 95%CI was -0.000 4 to 0.000 1) were independent predictors of 28-day mortality in patients with septic shock in the emergency department (all P < 0.05). A column-line graph model was established based on the above variables, and the ROC curves showed that the area under the ROC curve (AUC) of the nomogram model in the training set and validation set for predicting the 28-day mortality of patients with septic shock was 0.907 (95%CI was 0.864 to 0.940) and 0.822 (95%CI was 0.781 to 0.863), respectively. The calibration curves showed good agreement between the predicted and observed results for both the training and validation sets.
CONCLUSIONS: The nomogram model constructed based on NLR, MPV, Hb, PCT and D-dimer has significant clinical value in predicting the 28-day mortality of patients with septic shock in the emergency medicine department.
摘要：
目的：构建预测急诊内科感染性休克患者28天死亡率的列线图模型，并验证其预测效果。
方法：基于天津医科大学楚显一纪念医院急诊医学科数据库,天津医科大学总医院、天津医科大学第二医院,收集2017年1月至2020年10月急诊内科收治的913例感染性休克患者的资料,包括基线人口统计信息和临床特征，实验室指数,和主要终点（28天死亡率）。根据简单随机抽样将患者分为训练集和验证集。所有来自训练集的单向二元Logistic回归分析的显著变量均纳入多变量Logistic回归分析,分析感染性休克患者28天死亡率的危险因素,并构建列线图形模型。使用校准曲线和受试者操作者特征曲线(ROC曲线)评估列线图模型的预测功效。
结果：最终纳入860例符合诊断标准的脓毒性休克患者，包括训练集中的472和验证集中的388。训练和验证集的28天死亡率分别为52.5%(248/472)和54.1%(210/388)。分别。在训练集中，年龄,呼吸频率（RR），C反应蛋白（CRP）水平，D-二聚体,白细胞计数(WBC),中性粒细胞计数（NEU），中性粒细胞/淋巴细胞比率（NLR），单核细胞/淋巴细胞比率（MLR），平均血小板体积（MPV），死亡组血小板计数(PLT)明显高于存活组,和剩余碱的水平(BE)，淋巴细胞计数（LYM），血红蛋白(Hb)和慢性阻塞性肺疾病(COPD)比例明显低于存活组(均P<0.05)。多因素Logistic回归分析显示,NLR[比值比(OR)=0.0230,95%置信区间(95CI)为-0.2044~0.1130],MPV（OR=0.1798，95CI为-0.8776至0.1727），Hb（OR=0.0078，95CI为0.0103至0.0408），降钙素原（PCT；OR=1.9570，95CI为1.2430至3.0810），D-二聚体（OR=0.0001，95CI为-0.0004～0.0001）是急诊感染性休克患者28d死亡的独立预测因子（均P<0.05）。基于上述变量建立了列线图模型,和ROC曲线显示，列线图模型在训练集和验证集中预测感染性休克患者28天死亡率的ROC曲线下面积（AUC）分别为0.907（0.895CI为0.864～0.940）和0.822（95CI为0.781～0.863），分别。校准曲线显示出训练集和验证集的预测结果和观察结果之间的良好一致性。
结论：基于NLR构建的列线图模型，MPV,Hb,PCT和D-二聚体对预测急诊内科感染性休克患者28d病死率具有重要的临床价值。
方法：基于天津医科大学楚显一纪念医院急诊医学科数据库,天津医科大学总医院、天津医科大学第二医院,收集2017年1月至2020年10月急诊内科收治的913例感染性休克患者的资料,包括基线人口统计信息和临床特征，实验室指数,和主要终点（28天死亡率）。根据简单随机抽样将患者分为训练集和验证集。所有来自训练集的单向二元Logistic回归分析的显著变量均纳入多变量Logistic回归分析,分析感染性休克患者28天死亡率的危险因素,并构建列线图形模型。使用校准曲线和受试者操作者特征曲线(ROC曲线)评估列线图模型的预测功效。
结果：最终纳入860例符合诊断标准的脓毒性休克患者，包括训练集中的472和验证集中的388。训练和验证集的28天死亡率分别为52.5%(248/472)和54.1%(210/388)。分别。在训练集中，年龄,呼吸频率（RR），C反应蛋白（CRP）水平，D-二聚体,白细胞计数(WBC),中性粒细胞计数（NEU），中性粒细胞/淋巴细胞比率（NLR），单核细胞/淋巴细胞比率（MLR），平均血小板体积（MPV），死亡组血小板计数(PLT)明显高于存活组,和剩余碱的水平(BE)，淋巴细胞计数（LYM），血红蛋白(Hb)和慢性阻塞性肺疾病(COPD)比例明显低于存活组(均P<0.05)。多因素Logistic回归分析显示,NLR[比值比(OR)=0.0230,95%置信区间(95CI)为-0.2044~0.1130],MPV（OR=0.1798，95CI为-0.8776至0.1727），Hb（OR=0.0078，95CI为0.0103至0.0408），降钙素原（PCT；OR=1.9570，95CI为1.2430至3.0810），D-二聚体（OR=0.0001，95CI为-0.0004～0.0001）是急诊感染性休克患者28d死亡的独立预测因子（均P<0.05）。基于上述变量建立了列线图模型,和ROC曲线显示，列线图模型在训练集和验证集中预测感染性休克患者28天死亡率的ROC曲线下面积（AUC）分别为0.907（0.895CI为0.864～0.940）和0.822（95CI为0.781～0.863），分别。校准曲线显示出训练集和验证集的预测结果和观察结果之间的良好一致性。
结论：基于NLR构建的列线图模型，MPV,Hb,PCT和D-二聚体对预测急诊内科感染性休克患者28d病死率具有重要的临床价值。
