Establishment and Evaluation of A Early Prediction Model for Severe Acute Pancreatitis Complicated With Pancreatic Encephalopathy
10.3969/j.issn.1008-7125.2020.12.007
- Author:
Tian FU
1
;
Zhenggang LUAN
2
;
Xufeng ZHANG
3
Author Information
1. Department of Intensive Care Unit, Hanzhong 3201 Hospital
2. Department of Intensive Care Unit, The First Hospital of China Medical University
3. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University
- Publication Type:Journal Article
- Keywords:
Early Prediction Model;
Pancreatic Encephalopathy;
Risk Factors;
Severe Acute Pancreatitis
- From:
Chinese Journal of Gastroenterology
2020;25(12):740-744
- CountryChina
- Language:Chinese
-
Abstract:
Background: Pancreatic encephalopathy (PE) is one of the severe systemic complications of severe acute pancreatitis (SAP). In recent years, the incidence of PE was on the rise. There are few tools for early prediction of SAP complicated with PE. Aims: To screen the early independent risk factors of PE from clinical testing indices and scoring system of SAP patients, and then construct an early predictive scoring model of PE and used for intervening in advance. Methods: The clinical data of 130 patients with SAP from Jan. 2016 to Sept. 2020 at Shaanxi Hanzhong 3201 Hospital were analyzed retrospectively. Early independent risk factors of PE was screened by univariate analysis and multivariate Logistic regression analysis. The predictive scoring model was constructed by the weighted least square method. Results: Univariate analysis showed that history of alcohol abuse, lactic acid, intra-abdominal pressure (IAP), CT severity index (CTSI), extrapancreatic inflammation on CT (EPIC) and Glasgow coma scale (GCS) score were correlated to PE (P<0.05). Multivariate Logistic regression analysis showed that history of alcohol abuse (OR=2.843, 95% CI: 1.759-4.595, P=0.011), IAP (OR=1.077, 95% CI: 1.020-1.138, P=0.007), and EPIC score (OR=1.768, 95% CI: 1.181-2.649, P=0.006) were independent risk factors for PE in the early stage. According to the early predictive scoring model constructed, risk of PE was divided into low risk (0-3), medium risk (4-6) and high risk (>6), and differences in the incidence of PE in SAP patients among the three groups were statistically significant (P<0.05). Conclusions: The predictive scoring model constructed has the value for early prediction and evaluation of SAP complicated with PE, and risk stratification is helpful for taking intervention measures in advance to reduce the incidence of PE.
