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Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

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World Journal of Gastroenterology 2024年第23期30卷 2991-3004页

作者： Li-Qun Cai Da-Qing Yang Rong-Jian Wang He Huang Yi-Xiong Shi Department of Colorectal and Anal Surgery Whenzhou Central HospitalWenzhou 325000Zhejiang ProvinceChina Department of Colorectal and Anorectal Surgery The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000Zhejiang ProvinceChina

BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient *** predictive models for these reoperations lack precision in integrating complex clinical *** To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer *** Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively *** were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation *** were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve *** risk scores of the two groups were calculated and compared to validate the *** More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control *** logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(Ppostoperative unplanned reoperation in patients with colorectal cancer,which can improve tr

关键词： Colorectal cancer postoperative unplanned reoperation unplanned reoperation Clinical validation Nomogram Machine learning models

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