Establishment of multiple predictor models of severe acute pancreatitis with intestine functional disturbance
10.3760/cma.j.issn.2095-4352.2019.10.016
- VernacularTitle: 重症急性胰腺炎合并胃肠功能障碍早期预测模型的建立及其应用价值
- Author:
Chunmei GUO
1
;
Hong LIU
1
;
Weiping TAI
1
;
Yadan WANG
1
;
Nan WEI
1
;
Wu LIN
1
Author Information
1. Department of Gastroenterology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
- Publication Type:Journal Article
- Keywords:
Severe acute pancreatitis;
Intestine functional disturbance;
Predictor model
- From:
Chinese Critical Care Medicine
2019;31(10):1264-1268
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the factors related to severe acute pancreatitis (SAP) with intestine functional disturbance (IFD) and to establish the multiple predictor models of SAP with IFD.
Methods:Clinical data of consecutive SAP patients admitted to department of gastroenterology of Beijing Shijitan Hospital, Capital Medical University from January 2015 to March 2019 were retrospectively collected and analyzed. According to the occurrence of IFD at 48 hours after onset, the patients were divided into IFD group and control group. The clinical indicators within 4 hours after admission were compared between the two groups, and the independent predictive factors for SAP with IFD were screened by single factor analysis and multiple classified Logistic regression analysis. The unweighted predictive score (unwScore) and weighted predictive score (wScore) models were constructed by combining the independent predictors. The receiver operating characteristic (ROC) curves of SAP patients with IFD were plotted by independent predictive factors and predictive models, and the clinical predictive effect of each independent predictive index and predictive models were analyzed.
Results:A total of 149 patients with SAP were enrolled, including 87 males and 62 females, with age of (52.8±18.1) years old. There were 45 patients in IFD group and 104 patients in control group.Univariate analysis and multiple classified Logistic regression analysis showed that high sensitive C-reactive protein (hs-CRP), blood urea nitrogen (BUN), serum creatinine (SCr), serum calcium (Ca), procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR) were independent predictive factors of SAP with IFD. The ROC curve was used to calculate the cut-off value of the above indexes to predict IFD, and unwScore model was established. The cut-off score of IFD prediction by the unwScore model was 3 points, and the probability of IFD increased with the increase of the score. The area under ROC curve (AUC) of unwScore was 0.944, the sensitivity was 95.6%, the specificity was 94.2%, the positive predictive value (PPV) was 87.8%, and the negative predictive value (NPV) was 98.0%. The binary Logistic regression analysis of hs-CRP, BUN, Ca, SCr, PCT and NLR were carried out, and wScore model was established. The AUC of wScore was 0.959, the sensitivity was 95.9%, the specificity was 96.2%, the PPV was 91.5%, and the NPV was 98.1%; predictive value was superior to each independent index and unwScore model.
Conclusions:hs-CRP, BUN, SCr, Ca, PCT and NLR were independent predictive factors of SAP with IFD. The multiple predictor models of SAP with IFD have a good predictive efficiency which may provide valuable clinical reference for prediction and treatment.
