Stress-Induced Hyperglycemia as an Independent Predictor of Infectious Pancreatic Necrosis in Acute Pancreatitis: A Machine Learning-Driven Prognostic Model
Bibliographic record
Abstract
Xuchen Zhao,1,* Jiale Xu,2,* Congzhong Hu,1,* Wei Xue,1 Zongyuan Che,1 Ruiqi Ling,1 Haoyang Chen,1 Yulin Feng,1 Xiaolong Li,1 Shaojian Mo,1 Yanzhang Tian1 1Department of Biliary and Pancreatic Surgery, The Third Hospital of Shanxi Medical University (Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital), Taiyuan, Shanxi, People’s Republic of China; 2Department of General Surgery, General Hospital of Tisco (The Sixth Hospital of Shanxi Medical University), Taiyuan, Shanxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yanzhang Tian, Department of Biliary and Pancreatic Surgery, The Third Hospital of Shanxi Medical University (Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital), Taiyuan, Shanxi, People’s Republic of China, Email tianyanzhang@sxbqeh.com.cn Shaojian Mo, Department of Biliary and Pancreatic Surgery, The Third Hospital of Shanxi Medical University (Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital), Taiyuan, Shanxi, People’s Republic of China, Email moshaojian@sxbqeh.com.cnObjective: 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.Keywords: acute pancreatitis, stress-induced hyperglycemia, propensity score matching, clinical prediction model
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".