Establishment of a prognostic Nomogram model for predicting the first 72-hour mortality in polytrauma patients
10.3760/cma.j.cn121430-20200706-00500
- VernacularTitle:预测多发伤患者入院72 h内死亡风险列线图模型的建立
- Author:
Tian XIE
1
;
Xiangda ZHANG
;
Bin CHENG
;
Min HUANG
;
Shikai WANG
;
Sihua OU
Author Information
1. 华中科技大学协和深圳医院肝胆胰腺外科，广东深圳 518000
- From:
Chinese Critical Care Medicine
2020;32(10):1208-1212
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a prognostic Nomogram model for predicting the risk of early death in polytrauma patients.Methods:Data extracted from a polytrauma study on Dryad, an open access database, was selected for secondary analysis. Patients from 18 to 65 years old with polytrauma in the original data were included. All patients with missing variables, such as blood lactic acid (Lac), Glasgow coma score (GCS) and injury severity score (ISS) at admission, were excluded. The differences of gender, age, Lac, ISS and GCS scores between the patients who died within 72 hours and those who survived were analyzed. The risk factors for 72-hour death were analyzed by Logistic regression, and the Nomogram prediction model was established using R software. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model, and the Bootstrap method was used for internal verification by repeating sample for 1 000 times. Decision curve (DCA) was applied to analyze the clinical practical value of the model.Results:A total of 2 315 polytrauma patients were included. Logistic regression analysis showed that Lac, GCS score and age > 55 years old were the risk factors for early death in polytrauma patients [Lac: odds ratio ( OR) = 1.36, 95% confidence interval (95% CI) was 1.29-1.42, P < 0.001; GCS score: OR = 0.76, 95% CI was 0.73-0.79, P < 0.001; age > 55 years old: OR = 1.92, 95% CI was 1.37-2.66, P < 0.001]. The prediction model was established by using the above risk factors and displayed by Nomogram. ROC curve analysis showed that the area under the ROC curve (AUC) of Nomogram model to predict the risk of death within 72 hours was 0.858, and the predictive ability of Nomogram model was significantly higher than that of Lac (AUC = 0.743), GCS score (AUC = 0.774) and ISS score (AUC = 0.699), all P < 0.05. The model calibration chart showed that the predicting probability was consistent with the actual occurrence probability, and the DCA showed that Nomogram model presented excellent clinical value in predicting the 72-hour death risk for polytrauma patients. Conclusions:The prognostic Nomogram model presents significantly predictive value for the risk of death within 72 hours in polytrauma patients. Prognostic Nomogram model could offer individualized, visualized and graphical prediction pattern, and provide physicians with practical diagnostic tool for triage system and management of polytrauma according to precision medicine.
