Analysis of early risk factors and establishment of predictive model for prognosis of traumatic pancreatitis
10.3760/cma.j.issn.1671-0282.2023.05.009
- VernacularTitle:创伤性胰腺炎预后早期危险因素分析及预测模型建立
- Author:
Chaopeng MEI
1
;
Huning CUI
;
Mengwei CUI
;
Qianqian HE
;
Yaodong SONG
;
Qiaofang WANG
;
Yanna LIU
;
Dejian LI
;
Sanyang CHEN
;
Changju ZHU
Author Information
1. 郑州大学第一附属医院急诊医学部，郑州 450052
- Keywords:
Traumatic pancreatitis;
Prognosis;
Risk factors;
Prediction model
- From:
Chinese Journal of Emergency Medicine
2023;32(5):617-623
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the prognostic risk factors of patients with traumatic pancreatitis (TP) and establish an early combined prediction of multiple indicators model for TP.Methods:Patients admitted to the ICU of the First Affiliated Hospital of Zhengzhou University from June 2017 to June 2022 were collected retrospectively. Based on their prognosis, the patients were divided into two groups: the good prognosis group and the poor prognosis group. The general data such as sex, age, underlying diseases, Glasgow Coma Scale (GCS), acute physiology and chronic health evaluationⅡ (APACHEⅡ), injury severity score (ISS), bedside index for severity in acute pancreatitis (BISAP), and clinical test indices such as blood routine, blood coagulation, blood gas analysis, and liver and kidney function at admission were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were used to screen the early independent predictors of poor prognosis of TP, and the prediction model of TP was established by combining all of the independent indicators. The receiver operating characteristic (ROC) curve of each independent predictor and prediction model was drawn, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off value were calculated to examine the diagnostic impact of each independent predictor and the combined prediction model.Results:There were statistically significant differences in the complication rate of mental disorders, GCS, APACHE II, combined craniocerebral injury, combined chest injury, activated partial thromboplastin time, fibrin(pro)degradation products, lactate, aspartate aminotransferase, glomerular filtration rate, amylase, lipase, NT-proBNP, myoglobin, procalcitonin, ISS, and BISAP between the good and poor prognosis groups (all P<0.05). Multivariate logistic regression analysis showed that lactate ( OR=1.636, 95% CI: 1.046-2.559), lipase ( OR=1.005, 95% CI: 1.001-1.008), and ISS ( OR=1.161, 95% CI: 1.064-1.266) were independent risk factors influencing the prognosis of patients with TP. Based on the risk factors listed above, a prediction model was created: Logit P=-9.260+0.492×lactate+0.005×lipase+0.149×ISS, and the ROC curve was plotted. The AUC curve of the prediction model was 0.96 (95% CI: 0.91-1.00). Conclusions:Lactate, lipase, and ISS are early independent risk factors associated with the prognosis of TP. Their combined multi-indicator prediction model has an excellent clinical prediction effect, which can provide a clinical reference for early prediction and treatment of TP.
