Machine learning-based prediction of postoperative mortality risk after abdominal surgery  

作　　者：Ji-Hong Yuan Yong-Mei Jin Jing-Ye Xiang Shuang-Shuang Li Ying-Xi Zhong Shu-Liu Zhang Bin Zhao 

机构地区：[1]Department of General Surgery,Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine,Shanghai 201317,China [2]Department of Health Management,Zhenru Community Health Service Center of Putuo District,Shanghai 200333,China [3]Department of Oncology,Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine,Shanghai 201317,China [4]Department of Rehabilitation,Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine,Shanghai 201317,China [5]Department of Critical Care Medicine,The 960^(th)Hospital of the PLA Joint Logistics Support Force,Jinan 250000,Shandong Province,China

出　　处：《World Journal of Gastrointestinal Surgery》2025年第4期187-198,共12页世界胃肠外科杂志(英文)

基　　金：Supported by the Shanghai Municipal Health Commission Project,No.20214Y0284.

摘　　要：BACKGROUND Preoperative risk assessments are vital for identifying patients at high risk of postoperative mortality.However,traditional scoring systems can be time consuming.We hypothesized that the use of machine learning models would enable rapid and accurate risk assessments to be performed.AIM To assess the potential of machine learning algorithms to develop predictive models of mortality risk after abdominal surgery.METHODS This retrospective study included 230 individuals who underwent abdominal surgery at the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine between January 2023 and December 2023.Demographic and surgery-related data were collected and used to develop nomogram,decision-tree,random-forest,gradient-boosting,support vector machine,and naïve Bayesian models to predict 30-day mortality risk after abdominal surgery.Models were assessed using receiver operating characteristic curves and compared using the DeLong test.RESULTS Of the 230 included patients,52 died and 178 survived.Models were developed using the training cohort(n=161)and assessed using the validation cohort(n=68).The areas under the receiver operating characteristic curves for the nomogram,decision-tree,random-forest,gradient-boosting tree,support vector machine,and naïve Bayesian models were 0.908[95%confidence interval(CI):0.824-0.992],0.874(95%CI:0.785-0.963),0.928(95%CI:0.869-0.987),0.907(95%CI:0.837-0.976),0.983(95%CI:0.959-1.000),and 0.807(95%CI:0.702-0.911),respectively.CONCLUSION Nomogram,random-forest,gradient-boosting tree,and support vector machine models all demonstrate strong performances for the prediction of postoperative mortality and can be selected based on the clinical circumstances.

关 键 词：Abdominal surgery Postoperative death PREDICTION Machine learning Risk assessment 

分 类 号：R73[医药卫生—肿瘤]