Evidence map›Paper›PMID 42298037›Full record

ArticleSurgical endoscopy2026

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

Wenjun Ping, Peng Cao, Jiangtao Li, Bibo Zhao, Yunpeng Lin, Cheng Liu, Huiqing Wang, Guodong Chen

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wenjun Ping *The First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Peng Cao *The First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Jiangtao LiThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Bibo ZhaoThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Yunpeng LinThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Cheng LiuThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Huiqing WangThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China. 18974735227@163.com.
Guodong ChenThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China. chenguodong@usc.edu.cn.ORCID http://orcid.org/0009-0003-5196-2007

Funding

the Central Government Guided Local Science and Technology Development Fund Project in the Xinjiang Uygur Autonomous Region ZYYD2024CG17the Health Technology Promotion Project in the Xinjiang Uygur Autonomous Region SYTG-Y202429the Hunan Province Innovation Ecological Construction Plan Science and Technology Assistance Project in the Xinjiang Uygur Autonomous Region 2024WK4008the Hunan Provincial Health Commission Project 202214024284the Interdisciplinary Research Program in Medicine and Engineering at The First Affiliated Hospital of the University of South China IRP-M&E-2025-08the University of South China Clinical Research 4310 Program 20224310NHYCG01
6 · The paper itself

Abstract

backgroundEndoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is associated with significant complications, notably post-ERCP pancreatitis (PEP), which has an incidence ranging from 3.5 to 9.7%.

objectiveThis study aims to develop machine learning-based predictive models for PEP in patients receiving prophylactic non-steroidal anti-inflammatory drugs (NSAIDs).

methodsWe retrospectively analyzed the data from 991 patients who underwent ERCP between January 2018 and February 2024. A mixed sampling technique was employed to address data imbalance. Predictive models were constructed using logistic regression, light gradient boosting machine, multi-layer perceptron, random forest, and extreme gradient boosting. The top-performing models were combined to enhance predictive accuracy. Model evaluation was conducted using a prospective cohort of 124 patients as an independent internal test set.

resultsAmong the patients, 57 (5.75%) developed PEP. The models identified significant pre-procedure and post-procedure risk factors. The fusion model demonstrated an area under the curve of 0.84 for pre-procedure predictions and 0.86 for post-procedure predictions. High specificity and negative predictive values were noted, indicating potential clinical utility.

conclusionThis study presents a novel fusion model for predicting PEP in NSAID-treated patients, offering improved tools for risk stratification and clinical decision-making. Future research should focus on multi-center validation and the integration of additional predictive factors to enhance model robustness and applicability.

Indexed as

Anti-Inflammatory Agents, Non-SteroidalCholangiopancreatography, Endoscopic RetrogradePancreatitisPostoperative ComplicationsAgedBoosting Machine Learning AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRandom ForestRetrospective StudiesRisk FactorsAnti-Inflammatory Agents, Non-SteroidalERCPMachine learningPEPPredictive model

Identifiers

PMID42298037

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.