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DOI 10.2147/jir.s553311

Медицинская библиотека · Journal of inflammation research · 2025

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

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

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Аннотация

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.

Для цитирования: Zhao Xuchen, Xu Jiale, Hu Congzhong, Xue Wei, Che Zongyuan, Ling Ruiqi, Chen Haoyang, Feng Yulin, Li Xiaolong, Mo Shaojian, Tian Yanzhang. Stress-Induced Hyperglycemia as an Independent Predictor of Infectious Pancreatic Necrosis in Acute Pancreatitis: A Machine Learning-Driven Prognostic Model.. Journal of inflammation research. 2025. DOI: 10.2147/jir.s553311.
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