[Construction and validation of a nomogram for predicting the prognosis of patients with septic shock in department of emergency medicine].

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.