[Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3]
- PMID: 38404275
- DOI: 10.3760/cma.j.cn121430-20230128-00041
[Construction of prediction model of severe acute pancreatitis based on serum soluble T cell immunogloblulin and mucin domain-containing protein 3]
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, APACHE II score and MCTSI score were significantly increased, and pulse oxygen saturation (SpO2), 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].
Conclusions: The model based on serum sTIM3 demonstrated good predictive value for the progression of SAP in patients with AP.
Similar articles
-
[Serum Claudin-5 levels facilitate the early prediction of severe acute pancreatitis: a prospective observational study].Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024 Sep;36(9):930-936. doi: 10.3760/cma.j.cn121430-20240318-00247. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024. PMID: 39380513 Chinese.
-
Serum soluble T cell immunoglobulin mucin domain-3 as an early predictive marker for severity of acute pancreatitis; a retrospective analysis.BMC Gastroenterol. 2022 Dec 16;22(1):522. doi: 10.1186/s12876-022-02537-x. BMC Gastroenterol. 2022. PMID: 36526975 Free PMC article.
-
[Analysis of risk factors of major adverse kidney events within 30 days in patients with acute pancreatitis].Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2022 Jul;34(7):727-731. doi: 10.3760/cma.j.cn121430-20211206-01835. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2022. PMID: 36100411 Chinese.
-
Computed Tomography Severity Index vs. Other Indices in the Prediction of Severity and Mortality in Acute Pancreatitis: A Predictive Accuracy Meta-analysis.Front Physiol. 2019 Aug 27;10:1002. doi: 10.3389/fphys.2019.01002. eCollection 2019. Front Physiol. 2019. PMID: 31507427 Free PMC article.
-
AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas). 2025 Mar 29;61(4):629. doi: 10.3390/medicina61040629. Medicina (Kaunas). 2025. PMID: 40282920 Free PMC article. Review.
Publication types
MeSH terms
Substances
LinkOut - more resources
Medical
Research Materials
Miscellaneous