BACKGROUND Recurrence prediction of hepatocellular carcinoma(HCC)after thermal ablation represents a challenge that can impact patients'quality of life.Artificial intelligence(AI)-based radiomics models applied to...BACKGROUND Recurrence prediction of hepatocellular carcinoma(HCC)after thermal ablation represents a challenge that can impact patients'quality of life.Artificial intelligence(AI)-based radiomics models applied to various imaging modalities can improve recurrence prediction,therefore guiding therapeutic decisions.AIM To evaluate the effectiveness of AI-driven predictive models in predicting HCC recurrence.METHODS A systematic literature search in PubMed and Scopus was performed,and a total of ten studies were included in this systematic review.All studies included response prediction evaluation with AI models for patients who underwent thermal ablation for HCC.Deep learning and machine learning algorithms were utilized to evaluate the predictive performance and accuracy through metrics such as the area under the curve and concordance index.RESULTS The developed models demonstrated high accuracy in predicting local progression and recurrence,allowing a solid risk stratification.In particular,the integration of imaging data and clinical-laboratory variables optimized treatment selection,highlighting the superior ability of imaging models to predict therapeutic outcomes compared to clinical parameters alone.Furthermore,radiomic analysis of follow-up imaging enabled highly accurate detection of ablation site recurrence.CONCLUSION AI-driven predictive models based on multimodal radiomic analyses integrated with clinical data represent promising tools for predicting tumor recurrence after thermal ablation in HCC patients.展开更多
BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction...BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.展开更多
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw...To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.展开更多
Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GID...Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?展开更多
BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the a...BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential ...Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.展开更多
Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experiment...Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipien...Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.展开更多
The lifespan of plasma-sprayed thermal barrier coating(TBC)systems is difficult to predict owing to the variety of microstructures and deterioration histories.In this study,we developed a novel TBC damage model to ref...The lifespan of plasma-sprayed thermal barrier coating(TBC)systems is difficult to predict owing to the variety of microstructures and deterioration histories.In this study,we developed a novel TBC damage model to reflect deterioration histories;thus,it can be applied to various TBCs.Damage to TBCs is classifed into oxidation and mechanical damage;therefore,a detailed deterioration history can be reflected.In addition,by introducing a virtual S–N diagram,a life prediction model that can be applied to TBCs with various microstructures was established.We used the proposed damage and life prediction models in isothermal aging and thermal cycle tests with different aging cycles.The predicted lifespan of TBCs by using the proposed models was within 95%of the results obtained by performing actual tests in the temperature range of 1150–1350℃.展开更多
Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin n...Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.展开更多
Many studies investigating postoperative pancreatic fistula(POPF)after gastrectomy,including studies measuring drain amylase content(D-AMY)as a predictive factor have been reported.This article reviews previous studie...Many studies investigating postoperative pancreatic fistula(POPF)after gastrectomy,including studies measuring drain amylase content(D-AMY)as a predictive factor have been reported.This article reviews previous studies and looks to the future of measuring D-AMY in patients after gastrectomy.The causes of pancreatic fluid leakage are;the parenchymal and/or thermal injury to the pancreas,and blunt injury to the pancreas by compression and retraction.Measurement of D-AMY to predict POPF has become common in clinical practice after pancreatic surgery and was later extended to the gastric surgery.Several studies have reported associations between D-AMY and POPF after gastrectomy,and the high value of D-AMY on postoperative day(POD)1 was an independent risk factor.To improve both sensitivity and specificity,attempts have been made to enhance the predictive accuracy of factors on POD 1 as well as on POD 3 as combined markers.Although several studies have shown a high predictive ability of POPF,it has not necessarily been exploited in clinical practice.Many problems remain unresolved;ideal timing for measurement,optimal cut-off value,and means of intervention after prediction.Prospective clinical trial could be imperative in order to develop D-AMY measurement in common clinical practice for gastric surgery.展开更多
One way to improve practicability of automatic program repair（APR） techniques is to build prediction models which can predict whether an application of a APR technique on a bug is effective or not. Existing predicti...One way to improve practicability of automatic program repair（APR） techniques is to build prediction models which can predict whether an application of a APR technique on a bug is effective or not. Existing prediction models have some limitations. First, the prediction models are built with hand crafted features which usually fail to capture the semantic characteristics of program repair task. Second, the performance of the prediction models is only evaluated on Genprog, a genetic-programming based APR technique. This paper develops prediction models, i.e., random forest prediction models for SPR, another kind of generate-and-validate APR technique, which can distinguish ineffective repair instances from effective repair instances. Rather than handcrafted features, we use features automatically learned by deep belief network（DBN） to train the prediction models. The empirical results show that compared to the baseline models, that is, all effective models, our proposed models can at least improve the F1 by 9% and AUC（area under the receiver operating characteristics curve） by 19%. At the same time, the prediction model using learned features at least outperforms the one using hand-crafted features in terms of F1 by 11%.展开更多
According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mea...According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mean square（LMS） adaptive filter is applied to estimate the future track of the target. The structure of this filter is simple and the calculation amount is small. It is therefore suitable to being used in real-time control system. Testing results have proved that the control method can improve the tracking precision for maneuvering targets obviously.展开更多
A visual software system has been developed for predicting and analyzing the shape of solder joints in surface mount technology (SMT). The formation of the solder joint is numerically simulated through Surface Evolver...A visual software system has been developed for predicting and analyzing the shape of solder joints in surface mount technology (SMT). The formation of the solder joint is numerically simulated through Surface Evolver program and the calculation is automated with an additional controller. A preprocessor is developed in which process parameters determining the shape of solder joints can be input visually and transferred into Evolver program automatically. A postprocessor is built to analyze the global three dimensional shape and cross section profiles of solder fillets in multiple windows. Also, the application for predicting the solder joint shape of RC chip component is conducted with the PSJS system.展开更多
Successful revascularization of chronic total occlusion (CTO) by percutaneous?coronary intervention (PCI) is associated with reduced major adverse cardiovascular events (MACEs) compared with CTO PCI failure. The devel...Successful revascularization of chronic total occlusion (CTO) by percutaneous?coronary intervention (PCI) is associated with reduced major adverse cardiovascular events (MACEs) compared with CTO PCI failure. The developments of new strategies and new devices have improved the success rate of CTO PCI. However, the complexity of CTO lesions, clinical characteristics of patients and operator experience highly determine the successful revascularization. Using search items,?“chronic total occluion”,?“percutaneous coronary intervention”,?“scoring systems”,?“predictablity”.?We searched Pubmed, ScienceDirect, Web of Science, Cochrane Library, and CNKI. We found six clinically used scoring systems from 2011 to 2018. They included J-CTO score, CT-RECTOR score, CL score, PROGRESS CTO score, ORA score, and Ellis score. All parameters of each scoring systems have been systematically reviewed. The patients with higher score have found to have?adecreased?probability of CTO recanalization. Ellis score that mainly focused on ambiguous proximal cap and hybrid approach seems to provide better predictability in deciding procedure strategy.展开更多
ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal f...ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.展开更多
BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there hav...BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.展开更多
文摘BACKGROUND Recurrence prediction of hepatocellular carcinoma(HCC)after thermal ablation represents a challenge that can impact patients'quality of life.Artificial intelligence(AI)-based radiomics models applied to various imaging modalities can improve recurrence prediction,therefore guiding therapeutic decisions.AIM To evaluate the effectiveness of AI-driven predictive models in predicting HCC recurrence.METHODS A systematic literature search in PubMed and Scopus was performed,and a total of ten studies were included in this systematic review.All studies included response prediction evaluation with AI models for patients who underwent thermal ablation for HCC.Deep learning and machine learning algorithms were utilized to evaluate the predictive performance and accuracy through metrics such as the area under the curve and concordance index.RESULTS The developed models demonstrated high accuracy in predicting local progression and recurrence,allowing a solid risk stratification.In particular,the integration of imaging data and clinical-laboratory variables optimized treatment selection,highlighting the superior ability of imaging models to predict therapeutic outcomes compared to clinical parameters alone.Furthermore,radiomic analysis of follow-up imaging enabled highly accurate detection of ablation site recurrence.CONCLUSION AI-driven predictive models based on multimodal radiomic analyses integrated with clinical data represent promising tools for predicting tumor recurrence after thermal ablation in HCC patients.
文摘BACKGROUND Gastric cancer(GC)has a poor prognosis,and the accurate prediction of patient survival remains a significant challenge in oncology.Machine learning(ML)has emerged as a promising tool for survival prediction,though concerns regarding model interpretability,reliance on retrospective data,and variability in performance persist.AIM To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.METHODS A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019.The most frequently used ML models were deep learning(37.5%),random forests(37.5%),support vector machines(31.25%),and ensemble methods(18.75%).The dataset sizes varied from 134 to 14177 patients,with nine studies incorporating external validation.RESULTS The reported area under the curve values were 0.669–0.980 for overall survival,0.920–0.960 for cancer-specific survival,and 0.710–0.856 for disease-free survival.These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.CONCLUSION Despite challenges concerning retrospective studies and a lack of interpretability,ML models show promise;prospective trials and multidimensional data integration are recommended for improving their clinical applicability.
基金supported by the China State Railway Group Co.,Ltd.Science and Technology Research and Development Program Project(Grant No.L2024G007)the Natural Science Foundation of Hunan Province(Grant No.2024JJ5427)+1 种基金the National Natural Science Foundation of China(Grant No.52478321,52078485)the Science and Technology Research and Development Program Project of China Railway Group Limited(Grant No.2021-Special-08,2022-Key-06&2023-Key-22).
文摘To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.
文摘Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?
文摘BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金The National Natural Science Foundation of China-Regional Science“Identification of novel drug targets for lung cancer via Mendelian randomization analysis based on blood proteomics”(62362062)The 2025 Xinjiang University Excellent Graduate Innovation Project“Research on identification of therapeutic targets and predictive factors for mental disorders based on proteomics”(XJDX2025YJS151)。
文摘Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
文摘Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金supported by grants from the National Nat-ural Science Foundation of China (81570587 and 81700557)the Guangdong Provincial Key Laboratory Construction Projection on Organ Donation and Transplant Immunology (2013A061401007 and 2017B030314018)+3 种基金Guangdong Provincial Natural Science Funds for Major Basic Science Culture Project (2015A030308010)Science and Technology Program of Guangzhou (201704020150)the Natural Science Foundations of Guangdong province (2016A030310141 and 2020A1515010091)Young Teachers Training Project of Sun Yat-sen University (K0401068) and the Guangdong Science and Technology Innovation Strategy (pdjh2022b0010 and pdjh2023a0002)。
文摘Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant,funded by the Korean government(MOTIE)(No.20193310100030)the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2018R1A2A1A05077886)。
文摘The lifespan of plasma-sprayed thermal barrier coating(TBC)systems is difficult to predict owing to the variety of microstructures and deterioration histories.In this study,we developed a novel TBC damage model to reflect deterioration histories;thus,it can be applied to various TBCs.Damage to TBCs is classifed into oxidation and mechanical damage;therefore,a detailed deterioration history can be reflected.In addition,by introducing a virtual S–N diagram,a life prediction model that can be applied to TBCs with various microstructures was established.We used the proposed damage and life prediction models in isothermal aging and thermal cycle tests with different aging cycles.The predicted lifespan of TBCs by using the proposed models was within 95%of the results obtained by performing actual tests in the temperature range of 1150–1350℃.
文摘Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.
基金We are grateful to the nonprofit organization Epidemiological and Clinical Research Information Network(ECRIN)for providing their valuable support with this work.
文摘Many studies investigating postoperative pancreatic fistula(POPF)after gastrectomy,including studies measuring drain amylase content(D-AMY)as a predictive factor have been reported.This article reviews previous studies and looks to the future of measuring D-AMY in patients after gastrectomy.The causes of pancreatic fluid leakage are;the parenchymal and/or thermal injury to the pancreas,and blunt injury to the pancreas by compression and retraction.Measurement of D-AMY to predict POPF has become common in clinical practice after pancreatic surgery and was later extended to the gastric surgery.Several studies have reported associations between D-AMY and POPF after gastrectomy,and the high value of D-AMY on postoperative day(POD)1 was an independent risk factor.To improve both sensitivity and specificity,attempts have been made to enhance the predictive accuracy of factors on POD 1 as well as on POD 3 as combined markers.Although several studies have shown a high predictive ability of POPF,it has not necessarily been exploited in clinical practice.Many problems remain unresolved;ideal timing for measurement,optimal cut-off value,and means of intervention after prediction.Prospective clinical trial could be imperative in order to develop D-AMY measurement in common clinical practice for gastric surgery.
基金Supported by the National Natural Science Foundation of China(61603242)Opening Project of Collaborative Innovation Center for Economics Crime Investigation and Prevention Technology(JXJZXTCX-030)+1 种基金the Scientific Research Fund of Zhaoqing Univeristy(201734)Innovative Guidance Fund of Zhaoqing City(201704030409)
文摘One way to improve practicability of automatic program repair（APR） techniques is to build prediction models which can predict whether an application of a APR technique on a bug is effective or not. Existing prediction models have some limitations. First, the prediction models are built with hand crafted features which usually fail to capture the semantic characteristics of program repair task. Second, the performance of the prediction models is only evaluated on Genprog, a genetic-programming based APR technique. This paper develops prediction models, i.e., random forest prediction models for SPR, another kind of generate-and-validate APR technique, which can distinguish ineffective repair instances from effective repair instances. Rather than handcrafted features, we use features automatically learned by deep belief network（DBN） to train the prediction models. The empirical results show that compared to the baseline models, that is, all effective models, our proposed models can at least improve the F1 by 9% and AUC（area under the receiver operating characteristics curve） by 19%. At the same time, the prediction model using learned features at least outperforms the one using hand-crafted features in terms of F1 by 11%.
文摘According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mean square（LMS） adaptive filter is applied to estimate the future track of the target. The structure of this filter is simple and the calculation amount is small. It is therefore suitable to being used in real-time control system. Testing results have proved that the control method can improve the tracking precision for maneuvering targets obviously.
文摘A visual software system has been developed for predicting and analyzing the shape of solder joints in surface mount technology (SMT). The formation of the solder joint is numerically simulated through Surface Evolver program and the calculation is automated with an additional controller. A preprocessor is developed in which process parameters determining the shape of solder joints can be input visually and transferred into Evolver program automatically. A postprocessor is built to analyze the global three dimensional shape and cross section profiles of solder fillets in multiple windows. Also, the application for predicting the solder joint shape of RC chip component is conducted with the PSJS system.
文摘Successful revascularization of chronic total occlusion (CTO) by percutaneous?coronary intervention (PCI) is associated with reduced major adverse cardiovascular events (MACEs) compared with CTO PCI failure. The developments of new strategies and new devices have improved the success rate of CTO PCI. However, the complexity of CTO lesions, clinical characteristics of patients and operator experience highly determine the successful revascularization. Using search items,?“chronic total occluion”,?“percutaneous coronary intervention”,?“scoring systems”,?“predictablity”.?We searched Pubmed, ScienceDirect, Web of Science, Cochrane Library, and CNKI. We found six clinically used scoring systems from 2011 to 2018. They included J-CTO score, CT-RECTOR score, CL score, PROGRESS CTO score, ORA score, and Ellis score. All parameters of each scoring systems have been systematically reviewed. The patients with higher score have found to have?adecreased?probability of CTO recanalization. Ellis score that mainly focused on ambiguous proximal cap and hybrid approach seems to provide better predictability in deciding procedure strategy.
文摘ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.
基金Supported by the Talent Training Plan during the"14th Five-Year Plan"period of Beijing Shijitan Hospital Affiliated to Capital Medical University,No.2023LJRCLFQ.
文摘BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.
