Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Predicting survival and prognosis of postoperative breast cancer brain metastasis:a population-based retrospective analysis

Background:Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis.The study aimed to construct a novel predictive clinical model to evaluate the overall survival(OS)of patients with postoperative brain metastasis of breast cancer(BCBM)and validate its effectiveness.Methods:From 2010 to 2020,a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University,and they were randomly assigned to the training cohort and the validation cohort.Data of another 173 BCBM patients were collected from the Surveillance,Epidemiology,and End Results Program(SEER)database as an external validation cohort.In the training cohort,the least absolute shrinkage and selection operator(LASSO)Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS.The model capability was assessed using receiver operating characteristic,C-index,and calibration curves.Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model.The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.Results:LASSO Cox regression analysis revealed that lymph node metastasis,molecular subtype,tumor size,chemotherapy,radiotherapy,and lung metastasis were statistically significantly correlated with BCBM.The C-indexes of the survival nomogram in the training,validation,and SEER cohorts were 0.714,0.710,and 0.670,respectively,which showed good prediction capability.The calibration curves demonstrated that the nomogram had great forecast precision,and a dynamic diagram was drawn to increase the maneuverability of the results.The Risk Stratification System showed that the OS of lowrisk patients was considerably better than that of high-risk patients(P<0.001).Conclusion:The nomogram prediction model constructed in this study has a good predictive value,which can effectively evaluate the survival rate of patients with postoperative BCBM.