Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3
10.3760/cma.j.cn121430-20230128-00041
- VernacularTitle:基于外周血可溶性T细胞免疫球蛋白黏蛋白3构建重症急性胰腺炎预测模型
- Author:
Minghui ZHU
1
;
Daming WANG
;
Wenlong WANG
;
Yao MENG
;
Min LIN
Author Information
1. 常州市第一人民医院急诊医学科，江苏常州 213000
- Keywords:
Acute pancreatitis;
Interleukin-6;
Predictive model;
soluble T cell immunogloblulin and mucin domain-containing protein 3
- From:
Chinese Critical Care Medicine
2024;36(1):67-72
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the predictive value of the model based on soluble T cell immunogloblulin and mucin domain-containing protein 3 (sTIM3) for the progression of severe acute pancreatitis (SAP) in patients with acute pancreatitis (AP).Methods:A retrospective cohort study was conducted. The AP patients admitted to Changzhou First People's Hospital and Changzhou Second People's Hospital from June 1, 2020 to June 30, 2022 were enrolled. Mild AP (MAP) and moderately severe AP (MSAP) patients were classified as non-SAP group, and SAP patients were classified as SAP group according to the progression of AP patients during hospitalization. The basic data, blood biological indicators, serum sTIM3 level, bedside index for severity in acute pancreatitis (BISAP), acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score, modified computed tomography severity index (MCTSI) score within 48 hours of admission, and prognosis indicators were collected. Multivariate Logistic regression analysis was conducted to analyze the risk factors of the progression of SAP in patients with AP during hospitalization. Based on the results of multivariate analysis and the best parameters selected based on the minimal Akaike information criterion (AIC), the SAP prediction model based on sTIM3 was constructed. The receive operator characteristic curve (ROC curve) was plotted to analyze the predictive efficacy of the model.Results:A total of 99 AP patients were enrolled, 80 patients in the non-SAP group and 19 patients in the SAP group. Compared with the non-SAP group, body mass index (BMI), drinking history ratio, heart rate (HR), respiration rate (RR), white blood cell count (WBC), red blood cell count (RBC), C-reactive protein (CRP), alanine aminotransferase (ALT), serum creatinine (SCr), procalcitonin (PCT), interleukin-6 (IL-6), sTIM3, BISAP score, APACHEⅡ score and MCTSI score were significantly increased, and pulse oxygen saturation (SpO 2), direct bilirubin (DBil) and IL-10 were significantly decreased. The length of intensive care unit (ICU) stay and total length of hospital stay of patients in the SAP group were significantly longer than those in the non-SAP group [length of ICU stay (days): 1.0 (0, 1.5) vs. 0 (0, 0), total length of hospital stay (days): 17.11±9.39 vs. 8.40±3.08, both P < 0.01]. Multivariate Logistic regression analysis showed that HR [odds ratio ( OR) = 1.059, 95% confidence interval (95% CI) was 1.010-1.110, P = 0.017], DBil ( OR = 0.981, 95% CI was 0.950-0.997, P = 0.043), and sTIM3 ( OR = 1.002, 95% CI was 1.001-1.003, P = 0.027) were independent risk factors for predicting the progression of SAP in patients with AP, and the SAP prediction model based on sTIM3 was constructed: Logit( P) = -14.602+0.187×BMI+0.057×HR+0.006×CRP-0.020×DBil+0.002×sTIM3. ROC curve analysis showed that among the aforementioned single factor quantitative indicators, IL-6 was the most effective in predicting the progression of AP patients to SAP during hospitalization, but the predictive performance of prediction model based on the sTIM3 was significantly better than IL-6 [area under the ROC curve (AUC) and 95% CI: 0.957 (0.913-1.000) vs. 0.902 (0.845-0.958), P < 0.05]. Conclusion:The model based on serum sTIM3 demonstrated good predictive value for the progression of SAP in patients with AP.
