Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

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.

Optimizing prediction models for pancreatic fistula after pancreatectomy:Current status and future perspectives

Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.

Comparing gastrointestinal dysfunction score and acute gastrointestinal injury grade for predicting short-term mortality in critically ill patients

BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal(GI)function.The acute GI injury(AGI)grading system,established in 2012,is extensively utilized to evaluate GI dysfunction and forecast outcomes in clinical settings.In 2021,the GI dysfunction score(GIDS)was developed,building on the AGI grading system,to enhance the accuracy of GI dysfunction severity assessment,improve prognostic predictions,reduce subjectivity,and increase reproducibility.AIM To compare the predictive capabilities of GIDS and the AGI grading system for 28-day mortality in critically ill patients.METHODS A retrospective study was conducted at the general intensive care unit(ICU)of a regional university hospital.All data were collected during the first week of ICU admission.The primary outcome was 28-day mortality.Multivariable logistic regression analyzed whether GIDS and AGI grade were independent risk factors for 28-day mortality.The predictive abilities of GIDS and AGI grade were compared using the receiver operating characteristic curve,with DeLong’s test assessing differences between the curves’areas.RESULTS The incidence of AGI in the first week of ICU admission was 92.13%.There were 85 deaths(47.75%)within 28 days of ICU admission.There was no initial 24-hour difference in GIDS between the non-survival and survival groups.Both GIDS(OR 2.01,95%CI:1.25-3.24;P=0.004)and AGI grade(OR 1.94,95%CI:1.12-3.38;P=0.019)were independent predictors of 28-day mortality.No significant difference was found between the predictive accuracy of GIDS and AGI grade for 28-day mortality during the first week of ICU admission(Z=-0.26,P=0.794).CONCLUSION GIDS within the first 24 hours was an unreliable predictor of 28-day mortality.The predictive accuracy for 28-day mortality from both systems during the first week was comparable.

Validation of prognostic scores for predicting acute liver failure and in-hospital death in patients with dengue-induced severe hepatitis

BACKGROUND Acute liver failure(ALF)in dengue is rare but fatal.Early identification of patients who are at risk of ALF is the key strategy to improve survival.AIM To validate prognostic scores for predicting ALF and in-hospital mortality in dengue-induced severe hepatitis(DISH).METHODS We retrospectively reviewed 2532 dengue patients over a period of 16 years(2007-2022).Patients with DISH,defined as transaminases>10 times the normal reference level and DISH with subsequent ALF,were included.Univariate regre-ssion analysis was used to identify factors associated with outcomes.Youden’s index in conjunction with receiver operating characteristic(ROC)analysis was used to determine optimal cut-off values for prognostic scores in predicting ALF and in-hospital death.Area under the ROC(AUROC)curve values were compared using paired data nonparametric ROC curve estimation.RESULTS Of 193 DISH patients,20 developed ALF(0.79%),with a mortality rate of 60.0%.International normalized ratio,bilirubin,albumin,and creatinine were indepen-dent predictors associated with ALF and death.Prognostic scores showed excel-lent performance:Model for end-stage liver disease(MELD)score≥15 predicted ALF(AUROC 0.917,sensitivity 90.0%,specificity 88.4%)and≥18 predicted death(AUROC 0.823,sensitivity 86.9%,specificity 89.1%);easy albumin-bilirubin(ALBI)score≥-30 predicted ALF and death(ALF:AUROC 0.835,sensitivity80.0%,specificity 72.2%;death:AUROC 0.808,sensitivity 76.9%,specificity 69.3%);ALBI score≥-2 predicted ALF and death(ALF:AUROC 0.806,sensitivity 80.0%,specificity 77.4%;death:AUROC 0.799,sensitivity 76.9%,specificity 74.3%).Platelet-ALBI score also showed good performance in predicting ALF and death(AUROC=0.786 and 0.699,respectively).MELD and EZ-ALBI scores had similar performance in predicting ALF(Z=1.688,P=0.091)and death(Z=0.322,P=0.747).CONCLUSION MELD score is the best predictor of ALF and death in DISH patients.EZ-ALBI score,a simpler yet effective score,shows promise as an alternative prognostic tool in dengue patients.

Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography

BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.

Establishing prognostic models for intrahepatic cholangiocarcinoma based on immune cells

BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is a malignant liver tumor that is challenging to treat and manage and current prognostic models for the disease are inefficient or ineffective.Tumor-associated immune cells are critical for tumor development and progression.The main goal of this study was to establish models based on tumor-associated immune cells for predicting the overall survival of patients undergoing surgery for ICC.AIM To establish 1-year and 3-year prognostic models for ICC after surgical resection.METHODS Immunohistochemical staining was performed for CD4,CD8,CD20,pan-cytokeratin(CK),and CD68 in tumors and paired adjacent tissues from 141 patients with ICC who underwent curative surgery.Selection of variables was based on regression diagnostic procedures and goodness-of-fit tests(PH assumption).Clinical parameters and pathological diagnoses,combined with the distribution of immune cells in tumors and paired adjacent tissues,were utilized to establish 1-and 3-year prognostic models.RESULTS This is an important application of immune cells in the tumor microenvironment.CD4,CD8,CD20,and CK were included in the establishment of our prognostic model by stepwise selection,whereas CD68 was not significantly associated with the prognosis of ICC.By integrating clinical data associated with ICC,distinct prognostic models were derived for 1-and 3-year survival outcomes using variable selection.The 1-year prediction model yielded a C-index of 0.7695%confidence interval(95%CI):0.65-0.87 and the 3-year prediction model produced a C-index of 0.69(95%CI:0.65-0.73).Internal validation yielded a C-index of 0.761(95%CI:0.669-0.853)for the 1-year model and 0.693(95%CI:0.642-0.744)for the 3-year model.CONCLUSION We developed Cox regression models for 1-year and 3-year survival predictions of patients with ICC who underwent resection,which has positive implications for establishing a more comprehensive prognostic model for ICC based on tumor immune microenvironment and immune cell changes in the future.

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.

Machine learning as a tool predicting short-term postoperative complications in Crohn’s disease patients undergoing intestinal resection: What frontiers?

The recent study,“Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study”invest-igated the predictive efficacy of a machine learning model for major postoperative complications within 30 days of surgery in Crohn’s disease(CD)patients.Em-ploying a random forest analysis and Shapley Additive Explanations,the study prioritizes factors such as preoperative nutritional status,operative time,and CD activity index.Despite the retrospective design’s limitations,the model’s robu-stness,with area under the curve values surpassing 0.8,highlights its clinical potential.The findings align with literature supporting preoperative nutritional therapy in inflammatory bowel diseases,emphasizing the importance of compre-hensive assessment and optimization.While a significant advancement,further research is crucial for refining preoperative strategies in CD patients.

Evaluation of preoperative blood markers for predicting intraabdominal infection during colorectal cancer resection:A commentary on recent findings

This commentary evaluates the study by Liu et al.This study investigates the predictive utility of the neutrophil-lymphocyte ratio,platelet-lymphocyte ratio,systemic immune-inflammation index,and carcinoembryonic antigen levels for post-operative intra-abdominal infection following colorectal cancer(CRC)surge-ry.The study highlights the critical need for analyzing diverse patient demogra-phics and delves into the potential impact of various confounding factors on the predictive accuracy of these markers.Additionally,the commentary advocates for the initiation of prospective studies aimed at validating and enhancing the clinical utility of these biomarkers in the context of CRC treatment.The commentary aims to underscore the importance of broadening the research framework to include a wider patient demographic and more comprehensive factor analyses,thereby enriching the predictive model's applicability and relevance in clinical settings.

Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients

BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)patients was limited.AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.METHODS A total of 286 patients from the Surveillance,Epidemiology,and End Results database were divided into the training set and validation set at a ratio of 8:2.92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set.Cox regression analysis was used to explore the relationship between LNR and disease-specific survival(DSS)of gastric NEN patients.Random survival forest(RSF)algorithm and Cox proportional hazards(CoxPH)analysis were applied to develop models to predict DSS respectively,and compared with the 8th edition American Joint Committee on Cancer(AJCC)tumornode-metastasis(TNM)staging.RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death.The RSF model exhibited the best performance in predicting DSS,with the C-index in the test set being 0.769[95%confidence interval(CI):0.691-0.846]outperforming the CoxPH model(0.744,95%CI:0.665-0.822)and the 8th edition AJCC TNM staging(0.723,95%CI:0.613-0.833).The calibration curves and decision curve analysis(DCA)demonstrated the RSF model had good calibration and clinical benefits.Furthermore,the RSF model could perform risk stratification and individual prognosis prediction effectively.CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients.The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set,showing potential in clinical practice.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography

BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.

Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning

BACKGROUND Roux-en-Y gastric bypass(RYGB)is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity.It aids in significant weight loss and improves obesity-related medical conditions.Despite its effectiveness,postoperative care still has challenges.Clinical evidence shows that venous thromboembolism(VTE)is a leading cause of 30-d morbidity and mortality after RYGB.Therefore,a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates.AIM To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB.METHODS Using the 2016–2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program,data from 6526 patients(body mass index≥40 kg/m^(2))who underwent RYGB were analyzed.A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB.The resultant risk scores were derived from the coefficients of statistically significant variables.The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation.RESULTS Of the 26 initial variables,six predictors were identified.These included a history of chronic obstructive pulmonary disease with a regression coefficient(Coef)of 2.54(P<0.001),length of stay(Coef 0.08,P<0.001),prior deep venous thrombosis(Coef 1.61,P<0.001),hemoglobin A1c>7%(Coef 1.19,P<0.001),venous stasis history(Coef 1.43,P<0.001),and preoperative anticoagulation use(Coef 1.24,P<0.001).These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB.The risk model's area under the curve(AUC)was 0.79[95%confidence interval(CI):0.63-0.81],showing good discriminatory power,achieving a sensitivity of 0.60 and a specificity of 0.91.Without training,the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63(95%CI:0.62-0.64)and endoscopic sleeve gastroplasty with an AUC of 0.76(95%CI:0.75-0.78).CONCLUSION This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients,offering insights into factors that heighten the risk of VTE events.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.

Development of a new Cox model for predicting long-term survival in hepatitis cirrhosis patients underwent transjugular intrahepatic portosystemic shunts

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.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

A machine learning approach for predictings stroke

Background:Stroke is one of the most dangerous and life-threatening disease as it can cause lasting brain damage,long-term disability,or even death.The early detection of warning signs of a stroke can help save the life of a patient.In this paper,we adopted machine learning approaches to predict strokes and identify the three most important factors that are associated with strokes.Methods:This study used an open-access stroke prediction dataset.We developed 11 machine learning models and compare the results to those found in prior studies.Results:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other studies.Machine learning models,particularly the random forest algorithm,can accurately predict the risk of stroke and support medical decision making.Conclusion:Our findings can be applied to design clinical prediction systems at the point of care.

Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.