Development and Validation of Artificial Neural Networks for Survival Prediction Model for Patients with Spontaneous Hepatocellular Carcinoma Rupture After Transcatheter Arterial Embolization. Cancer management and research BACKGROUND:Spontaneous rupture bleeding is a fatal hepatocellular carcinoma (HCC) complication and a significant determinant of survival outcomes. This study aimed to develop and validate a novel artificial neural network (ANN)-based survival prediction model for patients with spontaneous HCC rupture after transcatheter arterial embolization (TAE). METHODS:Patients with spontaneous HCC rupture bleeding who underwent TAE at our hospital between January 2010 and December 2018 were included in our study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen clinical variables related to prognosis. We incorporated the above clinical variables identified by LASSO Cox regression into the ANNs model. Multilayer perceptron ANNs were used to develop the 1-year overall survival (OS) prediction model for patients with spontaneous HCC ruptured bleeding in the training set. The area under the receiver operating characteristic curve and decision curve analysis were used to compare the predictive capability of the ANNs model with that of existing conventional prediction models. RESULTS:The median survival time for the whole set was 11.8 months, and the 1-year OS rate was 47.5%. LASSO Cox regression revealed that sex, extrahepatic metastasis, macroscopic vascular invasion, tumor number, hepatitis B surface antigen, hepatitis B e antigen, tumor size, alpha-fetoprotein, fibrinogen, direct bilirubin, red blood cell, and γ-glutamyltransferase were risk factors for OS. An ANNs model with 12 input nodes, seven hidden nodes, and two corresponding prognostic outcomes was constructed. In the training set and the validation set, AUCs for the ability of the ANNs model to predict the 1-year OS of patients with spontaneous HCC rupture bleeding were 0.923 (95% CI, 0.890-0.956) and 0.930 (95% CI, 0.875-0.985), respectively, which were higher than that of the existing conventional models (all P < 0.0001). CONCLUSION:The ANNs model that we established has better survival prediction performance. 10.2147/CMAR.S328307

Development and validation of a machine learning-based early prediction model for massive intraoperative bleeding in patients with primary hepatic malignancies. World journal of gastrointestinal oncology BACKGROUND:Surgical resection remains the primary treatment for hepatic malignancies, and intraoperative bleeding is associated with a significantly increased risk of death. Therefore, accurate prediction of intraoperative bleeding risk in patients with hepatic malignancies is essential to preventing bleeding in advance and providing safer and more effective treatment. AIM:To develop a predictive model for intraoperative bleeding in primary hepatic malignancy patients for improving surgical planning and outcomes. METHODS:The retrospective analysis enrolled patients diagnosed with primary hepatic malignancies who underwent surgery at the Hepatobiliary Surgery Department of the Fourth Hospital of Hebei Medical University between 2010 and 2020. Logistic regression analysis was performed to identify potential risk factors for intraoperative bleeding. A prediction model was developed using Python programming language, and its accuracy was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS:Among 406 primary liver cancer patients, 16.0% (65/406) suffered massive intraoperative bleeding. Logistic regression analysis identified four variables as associated with intraoperative bleeding in these patients: ascites [odds ratio (OR): 22.839; < 0.05], history of alcohol consumption (OR: 2.950; < 0.015), TNM staging (OR: 2.441; < 0.001), and albumin-bilirubin score (OR: 2.361; < 0.001). These variables were used to construct the prediction model. The 406 patients were randomly assigned to a training set (70%) and a prediction set (30%). The area under the ROC curve values for the model's ability to predict intraoperative bleeding were 0.844 in the training set and 0.80 in the prediction set. CONCLUSION:The developed and validated model predicts significant intraoperative blood loss in primary hepatic malignancies using four preoperative clinical factors by considering four preoperative clinical factors: ascites, history of alcohol consumption, TNM staging, and albumin-bilirubin score. Consequently, this model holds promise for enhancing individualised surgical planning. 10.4251/wjgo.v16.i1.90

Systematic Reviews and Meta-analyses of the Procedure-specific Risks of Thrombosis and Bleeding in General Abdominal, Colorectal, Upper Gastrointestinal, and Hepatopancreatobiliary Surgery. Annals of surgery OBJECTIVE:To provide procedure-specific estimates of symptomatic venous thromboembolism (VTE) and major bleeding after abdominal surgery. BACKGROUND:The use of pharmacological thromboprophylaxis represents a trade-off that depends on VTE and bleeding risks that vary between procedures; their magnitude remains uncertain. METHODS:We identified observational studies reporting procedure-specific risks of symptomatic VTE or major bleeding after abdominal surgery, adjusted the reported estimates for thromboprophylaxis and length of follow-up, and estimated cumulative incidence at 4 weeks postsurgery, stratified by VTE risk groups, and rated evidence certainty. RESULTS:After eligibility screening, 285 studies (8,048,635 patients) reporting on 40 general abdominal, 36 colorectal, 15 upper gastrointestinal, and 24 hepatopancreatobiliary surgery procedures proved eligible. Evidence certainty proved generally moderate or low for VTE and low or very low for bleeding requiring reintervention. The risk of VTE varied substantially among procedures: in general abdominal surgery from a median of <0.1% in laparoscopic cholecystectomy to a median of 3.7% in open small bowel resection, in colorectal from 0.3% in minimally invasive sigmoid colectomy to 10.0% in emergency open total proctocolectomy, and in upper gastrointestinal/hepatopancreatobiliary from 0.2% in laparoscopic sleeve gastrectomy to 6.8% in open distal pancreatectomy for cancer. CONCLUSIONS:VTE thromboprophylaxis provides net benefit through VTE reduction with a small increase in bleeding in some procedures (eg, open colectomy and open pancreaticoduodenectomy), whereas the opposite is true in others (eg, laparoscopic cholecystectomy and elective groin hernia repairs). In many procedures, thromboembolism and bleeding risks are similar, and decisions depend on individual risk prediction and values and preferences regarding VTE and bleeding. 10.1097/SLA.0000000000006059

Development and validation of a prognostic score to identify the optimal candidate for preemptive TIPS in patients with cirrhosis and acute variceal bleeding. Hepatology (Baltimore, Md.) BACKGROUND AND AIM:Baveno VII workshop recommends the use of preemptive TIPS (p-TIPS) in patients with cirrhosis and acute variceal bleeding (AVB) at high- risk of treatment failure. However, the criteria defining "high-risk" have low clinical accessibility or include subjective variables. We aimed to develop and externally validate a model for better identification of p-TIPS candidates. APPROACH AND RESULTS:The derivation cohort included 1554 patients with cirrhosis and AVB who were treated with endoscopy plus drug (n = 1264) or p-TIPS (n = 290) from 12 hospitals in China between 2010 and 2017. We first used competing risk regression to develop a score for predicting 6-week and 1-year mortality in patients treated with endoscopy plus drugs, which included age, albumin, bilirubin, international normalized ratio, white blood cell, creatinine, and sodium. The score was internally validated with the bootstrap method, which showed good discrimination (6 wk/1 y concordance-index: 0.766/0.740) and calibration, and outperformed other currently available models. In the second stage, the developed score was combined with treatment and their interaction term to predicate the treatment effect of p-TIPS (mortality risk difference between treatment groups) in the whole derivation cohort. The estimated treatment effect of p-TIPS varied substantially among patients. The prediction model had good discriminative ability (6 wk/1 y c -for-benefit: 0.696/0.665) and was well calibrated. These results were confirmed in the validation dataset of 445 patients with cirrhosis with AVB from 6 hospitals in China between 2017 and 2019 (6-wk/1-y c-for-benefit: 0.675/0.672). CONCLUSIONS:We developed and validated a clinical prediction model that can help to identify individuals who will benefit from p-TIPS, which may guide clinical decision-making. 10.1097/HEP.0000000000000548