Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma

BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management.

Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy

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.