BACKGROUND Postoperative pancreatic fistula(PF)is a serious life-threatening complication after pancreaticoduodenectomy(PD).Our research aimed to develop a machine learning(ML)-aided model for PF risk stratification.A...BACKGROUND Postoperative pancreatic fistula(PF)is a serious life-threatening complication after pancreaticoduodenectomy(PD).Our research aimed to develop a machine learning(ML)-aided model for PF risk stratification.AIM To develop an ML-aided model for PF risk stratification.METHODS We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021.We used an ML algorithm to build predictive models,and subject prediction index,that is,decision curve analysis,area under operating characteristic curve(AUC)and clinical impact curve to assess the predictive efficiency of each model.RESULTS A total of 29 variables were used to build the ML predictive model.Among them,the best predictive model was random forest classifier(RFC),the AUC was[0.897,95%confidence interval(CI):0.370–1.424],while the AUC of the artificial neural network,eXtreme gradient boosting,support vector machine,and decision tree were between 0.726(95%CI:0.191–1.261)and 0.882(95%CI:0.321–1.443).CONCLUSION Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.展开更多
文摘BACKGROUND Postoperative pancreatic fistula(PF)is a serious life-threatening complication after pancreaticoduodenectomy(PD).Our research aimed to develop a machine learning(ML)-aided model for PF risk stratification.AIM To develop an ML-aided model for PF risk stratification.METHODS We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021.We used an ML algorithm to build predictive models,and subject prediction index,that is,decision curve analysis,area under operating characteristic curve(AUC)and clinical impact curve to assess the predictive efficiency of each model.RESULTS A total of 29 variables were used to build the ML predictive model.Among them,the best predictive model was random forest classifier(RFC),the AUC was[0.897,95%confidence interval(CI):0.370–1.424],while the AUC of the artificial neural network,eXtreme gradient boosting,support vector machine,and decision tree were between 0.726(95%CI:0.191–1.261)and 0.882(95%CI:0.321–1.443).CONCLUSION Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.
