Evidence mapPaperPMID 41656804Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2025

[Interpretable machine learning-based predictive model for assessing abdominal surgery risk and biologic therapy efficacy in CDAI 0 to 1 level Crohn disease patients].

Kailing Xie, Qi Sun, Zhixian Jiang, Hengchang Yao, Lichao Yang, Yawei Zhang, Hao Liu, Baojia Yao, Qiang Wu, Dan Zhang and 1 more

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Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Kailing XieDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011. 3212136489@qq.com.
Qi SunDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011. sunqi03720@163.com.
Zhixian JiangDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Hengchang YaoDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Lichao YangDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Yawei ZhangDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Hao LiuDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Baojia YaoDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Qiang WuDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011.
Dan ZhangDepartment of Clinical Nursing Teaching and Research, Second Xiangya Hospital, Central South University, Changsha 410011, China. zhangdan7030@csu.edu.cn.
Lianwen YuanDepartment of General Surgery, Second Xiangya Hospital, Central South University, Changsha 410011. yuanlianwen@csu.edu.cn.

Funding

the Natural Science Foundation of Hunan Province 2025JJ50584the Standardization Project of Hunan Province (Xiangshijian Biaohan〔2024〕No. 24), and the Fundamental Research Funds for the Central Universities of Central South University 2024ZZTS0959
6 · The paper itself

Abstract

objectivesCrohn disease (CD) patients face a clinically significant high risk of abdominal surgery. This study aims to develop a predictive model for estimating abdominal surgery in CD patients with Crohn Disease Activity Index (CDAI) 0-1.

methodsCD patients treated at the Second Xiangya Hospital of Central South University between 2016 and 2022 were retrospectively enrolled. Using a fixed random seed, the full cohort was randomly split into a training set and a validation set at a 5:5 ratio. Final predictors were selected using multivariable backward stepwise Cox regression, and hazard ratios (

resultsA total of 615 patients were included in the study, comprising 307 patients in the training set and 308 patients in the validation set. Multivariable backward stepwise Cox regression identified 4 key variables significantly associated with abdominal surgery risk in CD patients with CDAI 0-1, including C-reactive protein (CRP,

conclusionsThe CoxBoost model developed in this study effectively predicts abdominal surgery risk in CD patients with CDAI 0-1, and supports clinical decision-making regarding biologic therapy, providing evidence for personalized treatment planning.

Indexed as

AbdomenBiological TherapyCrohn DiseaseMachine LearningAdultBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsProportional Hazards ModelsRandom ForestRetrospective StudiesRisk Assessmentabdominal surgery riskartificial intelligencebiological agentCrohn diseaseinflammatory bowel disease

Identifiers

PMID41656804
PMCPMC12949868

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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 Socratic graph.