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已发表论文
急性胰腺炎多器官功能障碍预测列线图的开发
Authors Che Z , Zhao X , Xue W, Wang Z , Hu C , Feng Y , Ru R, Tian Y, Mo S
Received 8 November 2025
Accepted for publication 11 December 2025
Published 24 December 2025 Volume 2025:18 Pages 18025—18037
DOI https://doi.org/10.2147/JIR.S577488
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Nadia Andrea Andreani
Zongyuan Che,* Xuchen Zhao,* Wei Xue, Zhiyao Wang, Congzhong Hu, Yulin Feng, Ruijun Ru, Yanzhang Tian, Shaojian Mo
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yanzhang Tian, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China, Tel +86-13903512030, Email tianyanzhang@sxbqeh.com.cn Shaojian Mo, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China, Tel +86-19834514208, Email moshaojian@sxbqeh.com.cn
Background: Multiple Organ Dysfunction Syndrome (MODS) is a lethal complication of acute pancreatitis (AP), making early identification of high-risk patients crucial for improving outcomes.
Aim: To develop and validate a clinically applicable nomogram for predicting MODS risk in acute pancreatitis patients based on clinical and laboratory variables collected within the first 24 hours of admission.
Methods: We conducted a single-center retrospective cohort study using routinely collected electronic health records from Shanxi Bethune Hospital (Taiyuan, China), including 693 adult patients with acute pancreatitis admitted between January 1, 2019 and December 31, 2021. From 29 candidate indicators obtained within 24 hours of admission, key predictors were selected via LASSO regression, and three machine-learning models – generalized linear model (GLM), random forest (RF), and support vector machine (SVM) – were constructed and compared.
Results: Among the candidate models, the GLM-based nomogram showed the best overall performance. Using a parsimonious variable selection strategy, we derived a final 7-variable model. This model demonstrated good discrimination, with areas under the curve of 0.829 in the training cohort and 0.846 in the validation cohort, and showed satisfactory calibration, a high negative predictive value, and clinical net benefit.
Conclusion: Our internally validated 7-variable nomogram, based on a generalized linear model, showed good discrimination and a high negative predictive value for early prediction of multiple organ dysfunction syndrome in patients with acute pancreatitis, and may assist clinicians in early risk stratification and resource allocation. Further external validation and prospective studies are warranted to confirm its generalizability and clarify its impact on clinical decision-making and patient outcomes.
Keywords: acute pancreatitis, multiple organ dysfunction syndrome, risk stratification, electronic health records, machine learning
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yanzhang Tian, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China, Tel +86-13903512030, Email tianyanzhang@sxbqeh.com.cn Shaojian Mo, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, People’s Republic of China, Tel +86-19834514208, Email moshaojian@sxbqeh.com.cn
Background: Multiple Organ Dysfunction Syndrome (MODS) is a lethal complication of acute pancreatitis (AP), making early identification of high-risk patients crucial for improving outcomes.
Aim: To develop and validate a clinically applicable nomogram for predicting MODS risk in acute pancreatitis patients based on clinical and laboratory variables collected within the first 24 hours of admission.
Methods: We conducted a single-center retrospective cohort study using routinely collected electronic health records from Shanxi Bethune Hospital (Taiyuan, China), including 693 adult patients with acute pancreatitis admitted between January 1, 2019 and December 31, 2021. From 29 candidate indicators obtained within 24 hours of admission, key predictors were selected via LASSO regression, and three machine-learning models – generalized linear model (GLM), random forest (RF), and support vector machine (SVM) – were constructed and compared.
Results: Among the candidate models, the GLM-based nomogram showed the best overall performance. Using a parsimonious variable selection strategy, we derived a final 7-variable model. This model demonstrated good discrimination, with areas under the curve of 0.829 in the training cohort and 0.846 in the validation cohort, and showed satisfactory calibration, a high negative predictive value, and clinical net benefit.
Conclusion: Our internally validated 7-variable nomogram, based on a generalized linear model, showed good discrimination and a high negative predictive value for early prediction of multiple organ dysfunction syndrome in patients with acute pancreatitis, and may assist clinicians in early risk stratification and resource allocation. Further external validation and prospective studies are warranted to confirm its generalizability and clarify its impact on clinical decision-making and patient outcomes.
Keywords: acute pancreatitis, multiple organ dysfunction syndrome, risk stratification, electronic health records, machine learning