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Stress-Induced Hyperglycemia as an Independent Predictor of Infectious Pancreatic Necrosis in Acute Pancreatitis: A Machine Learning-Driven Prognostic Model

Xuchen Zhao,Jiale Xu,Congzhong Hu,Wei Xue,Zongyuan Che,Ruiqi Ling,Haoyang Chen,Yulin Feng,Xiaolong Li,Shaojian Mo,Yanzhang Tian

Published 2025 in Journal of Inflammation Research

ABSTRACT

Objective To investigate the impact of stress-induced hyperglycemia (SHG) at admission on clinical outcomes in acute pancreatitis (AP) by collecting and analyzing relevant clinical data. Methods This study enrolled AP patients diagnosed at Shanxi Bethune Hospital from January 1, 2017, to December 31, 2022. Clinical data and 24-h laboratory indicators were retrospectively collected. We employed propensity score matching (PSM) to compare the impact of SHG on AP clinical outcomes before and after matching. A temporal split allocated patients into training/validation cohorts for developing and validating a clinical prediction model for infected pancreatic necrosis (IPN). Results This study included 1343 acute pancreatitis patients, with 348 having SHG at admission. Before PSM, SHG patients showed significantly longer hospital stays (13.8 vs 12.28 days, p<0.001), higher ICU admission rates (6% vs 2%, p<0.001), and increased infected pancreatic necrosis (IPN) (15% vs.6%). After using PSM to control for confounding factors, SHG patients maintained longer hospitalizations (13.61 vs 12.28 days, p=0.004), higher ICU admissions (6% vs 2%, p=0.005), and IPN rates (15% vs 6%, p<0.001). These results confirm SHG as an independent poor prognostic factor for AP rather than a reflection of baseline differences. In the training cohort, seven independent IPN predictors were identified: hyperlipidemia, SHG, modified CT severity index (MCTSI), systemic inflammatory response syndrome (SIRS), Prothrombin Time Activity (PT%), LDL-C, and peripancreatic effusion. The clinical prediction model demonstrated good performance in the validation cohort, with an area under the receiver operating characteristic curve (AUC) of 0.891. Conclusion PSM confirmed that SHG adversely impacts clinical outcomes in acute pancreatitis. The prediction model incorporating seven variables—hyperlipidemia, SHG, MCTSI, SIRS, PT%, LDL-C, and peripancreatic effusion—demonstrated favorable predictive performance and clinical utility for infected pancreatic necrosis (IPN) in acute pancreatitis patients. Meanwhile, we developed a web-based calculator to enhance its clinical utility.

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