Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in medicine.About 17.9 million people die from CVDs annually,accounting for 31%of all deaths worldwide.With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle,it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease.In this study,we used various patient datasets from a major hospital in the United States as prognostic factors for CVD.The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old.In this study,we present a data mining modeling approach to analyze the performance,classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning(ML)using the Orange data mining software.Various techniques are then used to classify the model parameters,such as k-nearest neighbors,support vector machine,random forest,artificial neural network(ANN),naïve bayes,logistic regression,stochastic gradient descent(SGD),and AdaBoost.To determine the number of clusters,various unsupervised ML clustering methods were used,such as k-means,hierarchical,and density-based spatial clustering of applications with noise clustering.The results showed that the best model performance analysis and classification accuracy were SGD and ANN,both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets.Based on the results of most clustering methods,such as k-means and hierarchical clustering,Cardiovascular Disease Prognostic datasets can be divided into two clusters.The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model.The more accurate the model,the better it can predict which patients are at risk for CVD.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Development of a prognostic scoring system for hepatic vena cava Budd-Chiari syndrome with hepatocellular carcinoma

Background:Hepatocellular carcinoma(HCC)is a serious complication of hepatic vena cava Budd-Chiari syndrome(HVC-BCS)that significantly reduces the survival time of patients.Our study aimed to analyze the prognostic factors influencing the survival of HVC-BCS patients with HCC and to develop a prognostic scoring system.Methods:The clinical and follow-up data of 64 HVC-BCS patients with HCC who received invasive treatment at the First Affiliated Hospital of Zhengzhou University between January 2015 and December 2019 were retrospectively analyzed.Kaplan-Meier curves and log-rank tests were used to analyze the survival curve of patients and the difference in prognoses between the groups.Univariate and multivariate Cox regression analyses were performed to analyze the influence of biochemical,tumor,and etiological characteristics on the total survival time of patients,and a new prognostic scoring system was developed according to the regression coefficients of the independent predictors in the statistical model.The prediction efficiency was evaluated using the time-dependent receiver operating characteristics curve and concordance index.Results:Multivariate analysis showed that serum albumin level<34 g/L[hazard ratio(HR)=4.207,95%confidence interval(CI):1.816-8.932,P=0.001],maximum tumor diameter>7 cm(HR=8.623,95%CI:3.771-19.715,P<0.001),and inferior vena cava stenosis(HR=3.612,95%CI:1.646-7.928,P=0.001)were independent predictors of survival.A prognostic scoring system was developed according to the above-mentioned independent predictors,and patients were classified into grades A,B,C and D.Significant differences in survival were found among the four groups.Conclusions:This study successfully developed a prognostic scoring system for HVC-BCS patients with HCC,which is helpful for clinical evaluation of patient prognosis.

Systemic Inflammation Response Index and weight loss as prognostic factors in metastatic pancreatic cancer: A concept study from the PANTHEIA-SEOM trial

BACKGROUND The prognostic value of the Systemic Inflammation Response Index(SIRI)in advanced pancreatic cancer is recognized,but its correlation with patients´nutritional status and outcomes remains unexplored.AIM To study the prognostic significance of SIRI and weight loss in metastatic pancreatic cancer.METHODS The PANTHEIA-Spanish Society of Medical Oncology(SEOM)study is a multicentric(16 Spanish hospitals),observational,longitudinal,non-interventional initiative,promoted by the SEOM Real World-Evidence work group.This pilot study sought to analyze the association between weight loss and inflammatory status as defined by SIRI.The cohort stems from a proof-of-concept pilot study conducted at one of the coordinating centers.Patients with pathologically confirmed metastatic pancreatic adenocarcinoma,treated from January 2020 to January 2023,were included.The index was calculated using the product of neutrophil and monocyte counts,divided by lymphocyte counts,obtained within 15 days before initiation chemotherapy.This study evaluated associations between overall survival(OS),SIRI and weight loss.RESULTS A total of 50 patients were included.66%of these patients were male and the median age was 66 years.Metastasis sites:36%liver,12%peritoneal carcinomatosis,10%lung,and 42%multiple locations.Regarding the first line palliative chemotherapy treatments:50%received gemcitabine plus nab-paclitaxel;28%,modified fluorouracil,leucovorin,irinotecan and oxaliplatin,and 16%were administered gemcitabine.42%had a weight loss>5%in the three months(mo)preceding diagnosis.21 patients with a SIRI≥2.3×10^(3)/L exhibited a trend towards a lower median OS compared to those with a SIRI<2.3×10^(3)/L(4 vs 18 mo;P<0.000).Among 21 patients with>5%weight loss before diagnosis,the median OS was 6 mo,in contrast to 19 mo for those who did not experience such weight loss(P=0.003).Patients with a weight loss>5%showed higher SIRI levels.This difference was statistically significant(P<0.000).For patients with a SIRI<2.3×10^(3)/L,those who did not lose>5%of their weight had an OS of 20 mo,compared to 11 mo for those who did(P<0.001).No association was found between carbohydrate antigen 19-9 levels≥1000 U/mL and weight loss.CONCLUSION A higher SIRI was correlated with decreased survival rates in patients with metastatic pancreatic cancer and associated with weight loss.An elevated SIRI is suggested as a predictor of survival,emphasizing the need for prospective validation in the upcoming PANTHEIA-SEOM study.

Prognostic nutritional index in predicting survival of patients with gastric or gastroesophageal junction adenocarcinoma: A systematic review

BACKGROUND Gastric cancer is the third most common cause of cancer related death worldwide.Surgery with or without chemotherapy is the most common approach with curative intent;however,the prognosis is poor as mortality rates remain high.Several indexes have been proposed in the past few years in order to estimate the survival of patients undergoing gastrectomy.The preoperative nutritional status of gastric cancer patients has recently gained attention as a factor that could affect the postoperative course and various indexes have been developed.The aim of this systematic review was to assess the role of the prognostic nutritional index(PNI)in predicting the survival of patients with gastric or gastroesophageal adenocarcinoma who underwent gastrectomy with curative intent.AIM To investigate the role of PNI in predicting the survival of patients with gastric or gastroesophageal junction adenocarcinoma.METHODS A thorough literature search of PubMed and the Cochrane library was performed for studies comparing the overall survival(OS)of patients with gastric or gastroesophageal cancer after surgical resection depending on the preoperative PNI value.The PRISMA algorithm was used in the screening process and finally 16 studies were included in this systematic review.The review protocol was registered in the International Prospective Register of Systematic Reviews(PRO) RESULTS Sixteen studies involving 14551 patients with gastric or esophagogastric junction adenocarcinoma undergoing open or laparoscopic or robotic gastrectomy with or without adjuvant chemotherapy were included in this systematic review.The patients were divided into high-and low-PNI groups according to cut-off values that were set according to previous reports or by using receiver operating characteristic curve analysis in each individual study.The 5-year OS of patients in the low-PNI groups ranged between 39%and 70.6%,while in the high-PNI groups,it ranged between 54.9%and 95.8%.In most of the included studies,patients with high preoperative PNI showed statistically significant better OS than the low PNI groups.In multivariate analyses,low PNI was repeatedly recognised as an independent prognostic factor for poor survival.CONCLUSION According to the present study,low preoperative PNI seems to be an indicator of poor OS of patients undergoing gastrectomy for gastric or gastroesophageal cancer.

Circulating tumor DNA as prognostic markers of relapsed breast cancer:a systematic review and meta-analysis

Objective: Circulating tumor DNA (ctDNA) is increasingly being used as a potential prognosis biomarker in patients of breast cancer. This review aims to assess the clinical value of ctDNA in outcome prediction in breast cancer patients throughout the whole treatment cycle. Methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and clinical trials.gov were searched from January 2016 to May 2022. Conference abstracts published in last three years were also included. The following search terms were used: ctDNA OR circulating tumor DNA AND breast cancer OR breast carcinoma. Only studies written in English languages were included. The following pre-specified criteria should be met for inclusion: (1) observational studies (prospective or retrospective), randomized control trials, case-control studies and case series studies;(2) patients with breast cancer;(3) ctDNA measurement;(4) clinical outcome data such as objective response rate (ORR), pathological complete response (pCR), relapse-free survival (RFS), overall survival (OS), and so on. The random-effect model was preferred considering the potential heterogeneity across studies. The primary outcomes included postoperative short-term outcomes (ORR and pCR) and postoperative long-term outcomes (RFS, OS, and relapse). Secondary outcomes focused on ctDNA detection rate. Results: A total of 30 studies, comprising of 19 cohort studies, 2 case-control studies and 9 case series studies were included. The baseline ctDNA was significantly negatively associated with ORR outcome (Relative Risk [RR] = 0.65, 95% confidence interval [CI]: 0.50–0.83), with lower ORR in the ctDNA-positive group than ctDNAnegative group. ctDNA during neoadjuvant therapy (NAT) treatment was significantly associated with pCR outcomes (Odds Ratio [OR] = 0.15, 95% CI: 0.04–0.54). The strong association between ctDNA and RFS or relapse outcome was significant across the whole treatment period, especially after the surgery (RFS: Hazard Ratio [HR] = 6.74, 95% CI: 3.73–12.17;relapse outcome: RR = 7.11, 95% CI: 3.05–16.53), although there was heterogeneity in these results. Pre-operative and post-operative ctDNA measurements were significantly associated with OS outcomes (pre-operative: HR = 2.03, 95% CI: 1.12–3.70;post-operative: HR = 6.03, 95% CI: 1.31–27.78). Conclusions: In this review, ctDNA measurements at different timepoints are correlated with evaluation indexes at different periods after treatment. The ctDNA can be used as an early potential postoperative prognosis biomarker in breast cancer, and also as a reference index to evaluate the therapeutic effect at different stages.

Systemic inflammation in colorectal cancer: Underlying factors,effects, and prognostic significance

Systemic inflammation is a marker of poor prognosis preoperatively present in around 20%-40%of colorectal cancer patients.The hallmarks of systemic inflammation include an increased production of proinflammatory cytokines and acute phase proteins that enter the circulation.While the low-level systemic inflammation is often clinically silent,its consequences are many and may ultimately lead to chronic cancer-associated wasting,cachexia.In this review,we discuss the pathogenesis of cancer-related systemic inflammation,explore the role of systemic inflammation in promoting cancer growth,escaping antitumor defense,and shifting metabolic pathways,and how these changes are related to less favorable outcome.

Prognostic value of preoperative prognostic nutritional index and its associations with systemic inflammatory response markers in patients with stage Ⅲ colon cancer

Background: The prognostic nutritional index(PNI) has been widely applied for predicting survival outcomes of patients with various malignant tumors. Although a low PNI predicts poor prognosis in patients with colorectal cancer after tumor resection, the prognostic value remains unknown in patients with stage Ⅲ colon cancer undergoing curative tumor resection followed by adjuvant chemotherapy. This study aimed to investigate the prognostic value of PNI in patients with stage III colon cancer.Methods: Medical records of 274 consecutive patients with stage Ⅲ colon cancer undergoing curative tumor resection followed by adjuvant chemotherapy with oxaliplatin and capecitabine between December 2007 and December2013 were reviewed. The optimal PNI cutoff value was determined using receiver operating characteristic(ROC) curve analysis. The associations of PNI with systemic inflammatory response markers, including lymphocyte-to-monocyte ratio(LMR), neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), and C-reactive protein(CRP)level, and clinicopathologic characteristics were assessed using the Chi square or Fisher's exact test. Correlation analysis was performed using Spearman's correlation coefficient. Disease-free survival(DFS) and overall survival(OS)stratified by PNI were analyzed using Kaplan-Meier method and log-rank test, and prognostic factors were identified by Cox regression analyses.Results: The preoperative PNI was positively correlated with LMR(r= 0.483, P < 0.001) and negatively correlated with NLR(r =-0.441, P < 0.001), PLR(r =-0.607, P < 0.001), and CRP level(r =-0.333, P < 0.001). A low PNI(≤49.22)was significantly associated with short OS and DFS in patients with stage ⅢC colon cancer but not in patients with stage ⅢA/ⅢB colon cancer.In addition, patients with a low PNI achieved a longer OS and DFS after being treated with6-8 cycles of adjuvant chemotherapy than did those with < 6 cycles. Multivariate analyses revealed that PNI was independently associated with DFS(hazard ratios 2.001; 95% confidence interval 1.157-3.462; P = 0.013).Conclusion: The present study identified preoperative PNI as a valuable predictor for survival outcomes in patients with stage Ⅲ colon cancer receiving curative tumor resection followed by adjuvant chemotherapy.

Prognostic impact of the red cell distribution width in esophageal cancer patients: A systematic review and meta-analysis

AIM To clarify the previous discrepant conclusions, we performed a meta-analysis to evaluate the prognostic value of red cell distribution width(RDW) in esophageal cancer(EC). METHODS We searched the PubM ed, EMBASE, Web of Science and Cochrane Library databases to identify clinical studies, followed by using STATA version 12.0 for statistical analysis. Studies that met the following criteria were considered eligible:(1) Studies including EC patients who underwent radical esophagectomy;(2) studies including patients with localized disease without distant metastasis;(3) studies including patients without preoperative neoadjuvant therapy;(4) studies including patients without previous antiinflammatory therapies and with available preoperative laboratory outcomes;(5) studies reporting association between the preoperative RDW and overall survival(OS)/disease-free survival(DFS)/cancer-specific survival(CSS); and(6) studies published in English.RESULTS A total of six articles, published between 2015 and 2017, fulfilled the selection criteria in the end. Statistical analysis showed that RDW was not associated with the prognosis of EC patients, irrespective of OS/CSS [hazard ratio(HR) = 1.27, 95% confidence interval(CI): 0.97-1.57, P = 0.000] or DFS(HR = 1.42, 95%CI: 0.96-1.88, P = 0.000). Subgroup analysis indicated that elevated RDW was significantly associated with worse OS/CSS of EC patients when RDW > 13%(HR = 1.45, 95%CI: 1.13-1.76, P = 0.000), when the patient number ≤ 400(HR = 1.45, 95%CI: 1.13-1.76, P = 0.000) and when the study type was retrospective(HR = 1.42, 95%CI : 1.16-1.69, P = 0.000).CONCLUSION Contrary to our general understanding, this meta-analysis revealed that RDW cannot serve as an indicator of poor prognosis in patients with EC. However, it may still be a useful predictor of unfavorable prognosis using an appropriate cut-off value.

Health management based on fusion prognostics for avionics systems

Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.

A new tumor-associated antigen prognostic scoring system for spontaneous ruptured hepatocellular carcinoma after partial hepatectomy

Objective:Spontaneous hepatocellular carcinoma(HCC)rupture can be fatal,and hepatic resection could achieve a favorable long-term survival among all strategies of tumor rupture.However,there is no available prognostic scoring system for patients with ruptured HCC who underwent partial hepatectomy.Methods:From January 2005 to May 2015,129 patients with spontaneous HCC rupture underwent partial hepatectomy.Preoperative clinical data were collected and analyzed.Independent risk factors affecting overall survival(OS)were used to develop the new scoring system.Harrell’s C statistics,Akaike information criterion(AIC),the relative likelihood,and the log likelihood ratio were calculated to measure the homogeneity and discriminatory ability of a prognostic system.Results:In the multivariable Cox regression analysis,three factors,including tumor size,preoperativeα-fetoprotein level,and alkaline phosphatase level,were chosen for the new tumor-associated antigen(TAA)prognostic scoring system.The 1-year OS rates were 88.1%,43.2%,and 30.2%for TAA scores of 0–5 points(low-risk group),6–9 points(moderate-risk group),and 10–13points(high-risk group),respectively.The TAA scoring system had superior homogeneity and discriminatory ability(Harrell’s C statistics,0.693 vs.0.627 and 0.634;AIC,794.79 vs.817.23 and 820.16;relative likelihood,both<0.001;and log likelihood ratio,45.21 vs.22.77 and 21.84)than the Barcelona Clinic Liver Cancer staging system and the Cancer of the Liver Italian Program in predicting OS.Similar results were found while predicting disease-free survival(DFS).Conclusions:The new prognostic scoring system is simple and effective in predicting both OS and DFS of patients with spontaneous ruptured HCC.

Prognostic significance of systemic immune-inflammation index in patients with intrahepatic cholangiocarcinoma undergoing hepatic resection

BACKGROUND The prognosis of intrahepatic cholangiocarcinoma(ICC)patients following surgical resection remains poor.It is necessary to investigate effective biomarkers or prognostic models for ICC patients.AIM To investigate the prognostic effect of systemic immune-inflammation index(SII)to predict long-term outcomes in ICC patients with undergoing hepatic resection.METHODS Consecutive ICC patients who underwent initial hepatectomy with curative intent from January 2009 to September 2017 were retrospectively reviewed.Receiver-operating characteristic(ROC)curves were used to determine the optimal cut-off values of SII.Kaplan-Meier curves and Cox proportional hazards regression were performed to evaluate the discriminative ability of preoperative SII in predicting overall survival(OS)and recurrence-free survival(RFS).RESULTS A total of 530 patients were included and randomly divided into derivation(n=265)and validation cohort(n=265).The optimal cut-off value for SII was 450.Ata median follow-up of 18 mo(range,1-115.4 mo),317(59.8%)patients died and381(71.9%)patients experienced tumor relapse.Low SII level was associated with better OS and RFS(both P<0.05).Multivariate analyses identified multiple tumors,node invasion and high SII level as independent risk factors for OS,while multiple tumors,node invasion and high SII level were identified as independent risk factors for RFS.Validation cohort confirmed the findings of derivation cohort.CONCLUSION The present study demonstrated the feasibility of preoperative SII as a prognostic indicator for ICC.Patients with increased SII level were associated with worse OS and earlier tumor recurrence.Elevated SII level was an independent risk factor for OS and RFS in patients with ICC after hepatectomy.In the future,the SII could help stratifying patients with ICC,thus guiding therapeutic choices,especially in immunotherapy.

Safety prognostic technology in complex petroleum engineering systems: progress, challenges and emerging trends

Oil and gas facilities used in the petroleum industry can be considered as complex dynamic systems in that they require different types of equipment with various causal relationships among components and process variables under monitoring.As the systems grow increasingly large,high speed,automated and intelligent,the nonlinear relations among these process variables and their effects on accidents are to be fully understood for both system reliability and safety assurance.Failures that occur during the process can both cause tremendous loss to the petroleum industry and compromise product quality and affect the environment.Therefore,failures should be detected as soon as possible,and the root causes need to be identified so that corrections can be made in time to avoid further loss,which relate to the safety prognostic technology.By investigation of the relationship of accident causing factors in complex systems,new progress into diagnosis and prognostic technology from international research institutions is reviewed,and research highlights from China University of Petroleum（Beijing） in this area are also presented.By analyzing the present domestic and overseas research situations,the current problems and future directions in the fundamental research and engineering applications are proposed.

A radiomics prognostic scoring system for predicting progression-free survival in patients with stageⅣnon-small cell lung cancer treated with platinum-based chemotherapy

Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemotherapy.Methods:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each patient.Then,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression analysis.Finally,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.Results:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of RPSS.The RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds promise for guiding personalized pre-therapy of stageⅣNSCLC.

Evaluation of prognostic factors and scoring system in colonic perforation

AIM: To study the significance of scoring systems assessing severity and prognostic factors in patients with colonic perforation. METHODS: A total of 26 patients (9 men, 17 women; mean age 72.7 ± 11.6 years) underwent emergency operation for colorectal perforation in our institution between 1993 and 2005. Several clinical factors were measured preoperatively and 24 h postoperatively. Acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ), Mannheim peritonitis index (MPI) and peritonitis index of Altona (PIA Ⅱ) scores were calculated preoperatively. RESULTS: Overall postoperative mortality rate was 23.1% (6 patients). Compared with survivors, non- survivors displayed low blood pressure, low serum protein and high serum creatinine preoperatively, and low blood pressure, low white blood cell count, low pH, low PaO2/FiO2, and high serum creatinine postoperatively. APACHE Ⅱ score was significantly lower in survivors than in non-survivors (10.4 ± 3.84 vs 19.3 ± 2.87, P = 0.00003). Non-survivors tended to display high MPI score and low PIA Ⅱ score, but no signif icant difference was identif ied. CONCLUSION: Pre- and postoperative blood pressure and serum creatinine level appear related to prognosis of colonic perforation. APACHE Ⅱ score is most associated with prognosis and scores ≥ 20 are associated with signif icantly increased mortality rate.

Prognostic value of preoperative immune-nutritional scoring systems in remnant gastric cancer patients undergoing surgery

BACKGROUND Remnant gastric cancer(GC)is defined as GC that occurs five years or more after gastrectomy.Systematically evaluating the preoperative immune and nutritional status of patients and analyzing its prognostic impact on postoperative remnant gastric cancer(RGC)patients are crucial.A simple scoring system that combines multiple immune or nutritional indicators to identify nutritional or immune status before surgery is necessary.AIM To evaluate the value of preoperative immune-nutritional scoring systems in predicting the prognosis of patients with RGC.METHODS The clinical data of 54 patients with RGC were collected and analyzed retrospectively.Prognostic nutritional index(PNI),controlled nutritional status(CONUT),and Naples prognostic score(NPS)were calculated by preoperative blood indicators,including absolute lymphocyte count,lymphocyte to monocyte ratio,neutrophil to lymphocyte ratio,serum albumin,and serum total cholesterol.Patients with RGC were divided into groups according to the immune-nutritional risk.The relationship between the three preoperative immune-nutritional scores and clinical characteristics was analyzed.Cox regression and Kaplan–Meier analysis was performed to analyze the difference in overall survival(OS)rate between various immune-nutritional score groups.RESULTS The median age of this cohort was 70.5 years(ranging from 39 to 87 years).No significant correlation was found between most pathological features and immune-nutritional status(P>0.05).Patients with a PNI score<45,CONUT score or NPS score≥3 were considered to be at high immune-nutritional risk.The areas under the receiver operating characteristic curves of PNI,CONUT,and NPS systems for predicting postoperative survival were 0.611[95%confidence interval(CI):0.460–0.763;P=0.161],0.635(95%CI:0.485–0.784;P=0.090),and 0.707(95%CI:0.566–0.848;P=0.009),respectively.Cox regression analysis showed that the three immunenutritional scoring systems were significantly correlated with OS(PNI:P=0.002;CONUT:P=0.039;NPS:P<0.001).Survival analysis revealed a significant difference in OS between different immune-nutritional groups(PNI:75 mo vs 42 mo,P=0.001;CONUT:69 mo vs 48 mo,P=0.033;NPS:77 mo vs 40 mo,P<0.001).CONCLUSION These preoperative immune-nutritional scores are reliable multidimensional prognostic scoring systems for predicting the prognosis of patients with RGC,in which the NPS system has relatively effective predictive performance.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

Prognostic factors and survival after surgical resection of pancreatic neuroendocrine tumor with validation of established and modified staging systems

Background: Pancreatic neuroendocrine tumors(PNETs) display wide heterogeneity with highly variable prognosis. This study aimed to identify variables related to survival after surgical resection of PNET.Methods: A total of 143 patients were identified from a prospectively maintained database. Patient characteristics were analyzed and prognostic factors for overall survival and progression-free survival were evaluated. The WHO, ENETS and AJCC scoring systems were applied to the cohort, and their ability to predict patient outcomes were compared.Results: Multivariate analysis found that female gender, lymph node metastases and increasing WHO2010 grade to be independently associated with reduced overall survival(P < 0.05). Patients requiring multi-visceral resection or debulking surgery found to be associated with shortest survival. ROC analysis found the ENETS and AJCC scoring systems to be similarly predictive of 5-year overall survival. Modified Ki67 significantly improved its accuracy in predicting 5-year overall survival(AUROC: 0.699 vs 0.605;P < 0.01).Conclusions: Multi-visceral or debulking surgery is associated with poor outcomes. There seems to be no significant difference between enucleation and anatomical segmental resection. Available scoring systems have reasonable accuracy in stratifying disease severity, with no system identified as being superior.Prognostic stratification with modified grading systems needs further validation before applied in clinical practice.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND Pyroptosis impacts the development of malignant tumors,yet its role in colorectal cancer(CRC)prognosis remains uncertain.AIM To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.METHODS Gene expression data were obtained from The Cancer Genome Atlas(TCGA)and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus(GEO).Pyroptosis-related gene expression in cell clusters was analyzed,and enrichment analysis was conducted.A pyroptosis-related risk model was developed using the LASSO regression algorithm,with prediction accuracy assessed through K-M and receiver operating characteristic analyses.A nomo-gram predicting survival was created,and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations.Finally,the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.RESULTS An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B,SDHB,BST2,UBE2D2,GJA1,AIM2,PDCD6IP,and SEZ6L2(P<0.05).Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis(P<0.05).Patients with higher risk scores demonstrated increased death risk and reduced overall survival(P<0.05).Significant differences in immune infiltration were observed between low-and high-risk groups,correlating with pyroptosis-related gene expression.CONCLUSION We developed a pyroptosis-related prognostic model for CRC,affirming its correlation with immune infiltration.This model may prove useful for CRC prognostic evaluation.