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

作　　者：Li-Qun Cai Da-Qing Yang Rong-Jian Wang He Huang Yi-Xiong Shi 

机构地区：[1]Department of Colorectal and Anal Surgery,Whenzhou Central Hospital,Wenzhou 325000,Zhejiang Province,China [2]Department of Colorectal and Anorectal Surgery,The First Affiliated Hospital of Wenzhou Medical University,Wenzhou 325000,Zhejiang Province,China

出　　处：《World Journal of Gastroenterology》2024年第23期2991-3004,共14页世界胃肠病学杂志（英文版）

基　　金：This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University,No.KY2024-R016.

摘　　要：BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS 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 collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients 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 analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.

关 键 词：Colorectal cancer Postoperative unplanned reoperation Unplanned reoperation Clinical validation NOMOGRAM Machine learning models 

分 类 号：R735.34[医药卫生—肿瘤]