Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gast...BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorit...Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.展开更多
BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk predic...BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.展开更多
BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related fa...BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.展开更多
Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content...Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.展开更多
BACKGROUND Ursodeoxycholic acid(UDCA)is the first-line therapeutic agent for primary biliary cholangitis(PBC).However,a subset of patients exhibit a suboptimal response to UDCA,and reliable predictive biomarkers remai...BACKGROUND Ursodeoxycholic acid(UDCA)is the first-line therapeutic agent for primary biliary cholangitis(PBC).However,a subset of patients exhibit a suboptimal response to UDCA,and reliable predictive biomarkers remain elusive.Studies have implicated plasma microRNAs(miRNAs)in the pathophysiological pro-gression of PBC,with certain miRNAs demonstrating potential as diagnostic and disease progression biomarkers.However,biomarkers capable of predicting the therapeutic efficacy of UDCA have not yet been identified.AIM To investigate differentially expressed miRNAs in PBC patients with divergent UDCA treatment responses and to explore potential biomarkers that predict treatment response in PBC.METHODS Plasma samples from treatment-naive PBC patients receiving≥1 year of standard UDCA treatment were collected.Efficacy was evaluated using the Paris I criteria.Patient samples were divided into discovery group(n=10)and validation group(n=30),with further stratification of patients into drug-resistant and drug-sensitive(DS)cohorts.Next-generation sequencing and quantitative real-time polymerase chain reaction were used to screen,functionally analyze,and validate the pre-treatment miRNA profiles of the treatment groups.RESULTS Forty-nine miRNAs were differentially expressed between the two groups before UDCA treatment(N=40).MiR-22-5p and miR-126-3p were highly expressed in the DS group before treatment(P<0.001),whereas miR-7706 exhibited a low expression(P=0.017).Post-treatment,miR-126-3p maintained low expression in the drug-resistant group(P=0.003),but showed elevated levels in the DS group(P<0.001).Logistic regression analysis identified miR-126-3p expression(odds ratio=34.32,95%confidence interval:1.95-605.40,P=0.016)as a significant factor influencing UDCA treatment response,while miR-22-5p(P=0.990)and miR-7706(P=0.157)showed no significant association.MiR-126-3p levels were negatively correlated with total bilirubin(r=-0.356,P=0.005)and immuno-globulin G levels(r=-0.311,P=0.015).The area under the receiver operating characteristic curve was 0.891(P=0.0003,95%confidence interval:0.772-1.000)with a sensitivity of 82.4%and a specificity of 84.6%.CONCLUSION Plasma miRNA expression profiles are heterogenous in patients with PBC with differential responses to UDCA therapy.MiR-126-3p demonstrates predictive potential for a suboptimal response to UDCA in patients with PBC.展开更多
BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumo...BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumor size and control micrometastases,has emerged as a crucial therapeutic strategy for locally advanced gastric cancer.Pathological complete response(pCR),characterized by the absence of viable tumor cells in the resected specimen after neoadjuvant treatment,is recognized as a strong predictor of favorable prognosis.However,the factors influencing the achievement of pCR remain incompletely understood.AIM To identify and analyze the predictive factors associated with achieving pCR after NAC in gastric cancer patients,thereby providing evidence-based guidance for clinical decision-making.METHODS A retrospective analysis was performed on 215 patients from Shandong Cancer Hospital and Tai’an Central Hospital with locally advanced gastric cancer who underwent NAC followed by radical surgery at our hospital between January 2015 and December 2023.Comprehensive clinical and pathological data were collected,including age,gender,tumor location,Lauren classification,clinical staging,chemotherapy regimens,number of chemotherapy cycles,and baseline hematological indicators.The baseline hematological indicators included neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio,albumin level,carcinoembryonic antigen(CEA),and carbohydrate antigen 19-9.Univariate and multivariate logistic regression analyses were employed to determine the independent predictive factors for pCR.RESULTS Among 215 gastric cancer patients,41(19.1%)achieved pCR after NAC.Multivariate analysis identified five independent predictive factors for pCR:Lauren intestinal type[odds ratio(OR)=3.28],lower clinical T stage(OR=2.75),CEA decrease≥70%after NAC(OR=3.42),pre-treatment NLR<2.5(OR=2.13),and≥4 chemotherapy cycles(OR=2.87).The fluorouracil,leucovorin,oxaliplatin,docetaxel regimen achieved the highest pCR rate(27.5%),and oxaliplatin-containing regimens were superior to cisplatin-containing regimens(22.3%vs 12.7%,P=0.034).Patients with both low NLR and platelet-to-lymphocyte ratio had the highest pCR rate(33.8%),while those with both high inflammatory markers had the lowest rate(10.7%).Earlier clinical stage disease(cT3N+vs cT4N+:28.6%vs 13.0%)and lower lymph node burden were associated with higher pCR rates.CONCLUSION The achievement of pCR after NAC in gastric cancer patients is closely associated with Lauren intestinal type,lower clinical T stage,a significant decrease in CEA after chemotherapy,low pre-treatment NLR,and an adequate number of chemotherapy cycles.展开更多
BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for I...BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies,with high recurrence rates after treatment.Identifying reliable biomarkers for predicting recurrence is essential for improving patient ou...BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies,with high recurrence rates after treatment.Identifying reliable biomarkers for predicting recurrence is essential for improving patient outcomes.Hepatitis B corerelated antigen(HBcrAg)has shown potential as a predictive marker for HCC recurrence.AIM To evaluate the association between HBcrAg levels and the risk of HCC recurrence.METHODS A systematic review was conducted following PRISMA guidelines.PubMed,EMBASE,Web of Science,and the Cochrane Library were searched without restrictions on date or language.Observational studies reporting hazard ratios(HRs)for HBcrAg as a predictor of HCC recurrence were included.Data extraction and quality assessment were performed independently by two reviewers.Statistical analyses used a random-effects model to account for heterogeneity(I²≥50%),and sensitivity analysis was performed to ensure the robustness of the results.RESULTS A total of 1339 articles were initially identified,and 17 studies were included in the final meta-analysis after screening.The pooled analysis showed a significant association between elevated HBcrAg levels and HCC recurrence(HR=4.42,95%confidence interval:3.43-5.41)with substantial heterogeneity(I²=92.6%).Subgroup analysis revealed higher pooled HRs in studies with≥500 participants(HR=4.18)and HBcrAg cut-offs≥4.0 LogU/mL(HR=5.29).Studies with≥10 years of follow-up showed a lower HR(2.89)compared to those with<10 years(3.27).Patients treated with nucleos(t)ide analogs had a pooled HR of 1.98,while those without nucleos(t)ide analog had a higher HR of 3.87.Sensitivity analysis confirmed the robustness of the results,with no significant publication bias detected.CONCLUSION This meta-analysis provides strong evidence that elevated HBcrAg levels are associated with an increased risk of HCC recurrence.HBcrAg may serve as a valuable biomarker for predicting recurrence,aiding personalized management and surveillance strategies for HCC patients.展开更多
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive system...BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.展开更多
BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset D...BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).展开更多
BACKGROUND Acute kidney injury(AKI)is a severe complication of acute pancreatitis(AP)associated with increased morbidity and mortality.Early prediction of AKI remains a clinical challenge owing to the limitations of t...BACKGROUND Acute kidney injury(AKI)is a severe complication of acute pancreatitis(AP)associated with increased morbidity and mortality.Early prediction of AKI remains a clinical challenge owing to the limitations of traditional biomarkers,such as serum creatinine.AIM To evaluate the concentration and predictive value of plasma neutrophil gelatinase-associated lipocalin(NGAL)in patients with AP and AKI.METHODS This cross-sectional descriptive study was conducted from October 2021 to June 2023 at Bach Mai Hospital.In total,219 patients were enrolled,including 51 patients with AP and AKI,168 patients with AP but without AKI,and 35 healthy controls.Plasma NGAL levels were measured and compared between groups.Receiver operating characteristic curve analysis was performed to determine the predictive value of NGAL levels for the severity of AKI and AP.RESULTS Among AP and AKI cases,47.1%were classified as Kidney Disease:Improving Global Outcomes stage 1,33.3%as stage 2,and 19.6%as stage 3.The AP with AKI group(570.9 ng/mL)had significantly higher median plasma NGAL concentrations than the AP without AKI group(400.6 ng/mL)and the healthy control group(234.3 ng/mL)(P<0.01).The NGAL levels increased proportionally with AKI severity.A plasma NGAL cutoff value of 504.29 ng/mL predicted AKI with 60.8%sensitivity and 68.4%specificity(area under the curve=0.684;P<0.001).A cutoff of 486.03 ng/mL predicted AP severity with 66.1%sensitivity and 66.4%specificity(area under the curve=0.651;P<0.005).NGAL positively correlated with international normalized ratio,urea,creatinine,lactate dehydrogenase,and lactate levels.CONCLUSION Plasma NGAL levels predicted both AKI development and disease severity.Therefore,NGAL should be considered a useful biomarker for the early evaluation of patients with AP.展开更多
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
文摘BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
文摘Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.
文摘BACKGROUND Sepsis is a severe complication in hospitalized patients with diabetic foot(DF),often associated with high morbidity and mortality.Despite its clinical significance,limited tools exist for early risk prediction.AIM To identify key risk factors and evaluate the predictive value of a nomogram model for sepsis in this population.METHODS This retrospective study included 216 patients with DF admitted from January 2022 to June 2024.Patients were classified into sepsis(n=31)and non-sepsis(n=185)groups.Baseline characteristics,clinical parameters,and laboratory data were analyzed.Independent risk factors were identified through multivariable logistic regression,and a nomogram model was developed and validated.The model's performance was assessed by its discrimination(AUC),calibration(Hosmer-Lemeshow test,calibration plots),and clinical utility[decision curve analysis(DCA)].RESULTS The multivariable analysis identified six independent predictors of sepsis:Diabetes duration,DF Texas grade,white blood cell count,glycated hemoglobin,Creactive protein,and albumin.A nomogram integrating these factors achieved excellent diagnostic performance,with an AUC of 0.908(95%CI:0.865-0.956)and robust internal validation(AUC:0.906).Calibration results showed strong agreement between predicted and observed probabilities(Hosmer-Lemeshow P=0.926).DCA demonstrated superior net benefit compared to extreme intervention scenarios,highlighting its clinical utility.CONCLUSION The nomogram prediction model,based on six key risk factors,demonstrates strong predictive value,calibration,and clinical utility for sepsis in patients with DF.This tool offers a practical approach for early risk stratification,enabling timely interventions and improved clinical management in this high-risk population.
基金Supported by 2024 Hospital-Level Research Start-up Fund,No.YK202426Suzhou Wujiang District"Science and Education for Health"Project,No.WWK202201Development Fund Project of the Affiliated Hospital of Xuzhou Medical University,No.XYFY202423.
文摘BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.
文摘Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
基金Supported by the National Key Research and Development Program of China,No.2019YFC0840704Beijing Municipal Science and Technology Program,No.Z201100005520047.
文摘BACKGROUND Ursodeoxycholic acid(UDCA)is the first-line therapeutic agent for primary biliary cholangitis(PBC).However,a subset of patients exhibit a suboptimal response to UDCA,and reliable predictive biomarkers remain elusive.Studies have implicated plasma microRNAs(miRNAs)in the pathophysiological pro-gression of PBC,with certain miRNAs demonstrating potential as diagnostic and disease progression biomarkers.However,biomarkers capable of predicting the therapeutic efficacy of UDCA have not yet been identified.AIM To investigate differentially expressed miRNAs in PBC patients with divergent UDCA treatment responses and to explore potential biomarkers that predict treatment response in PBC.METHODS Plasma samples from treatment-naive PBC patients receiving≥1 year of standard UDCA treatment were collected.Efficacy was evaluated using the Paris I criteria.Patient samples were divided into discovery group(n=10)and validation group(n=30),with further stratification of patients into drug-resistant and drug-sensitive(DS)cohorts.Next-generation sequencing and quantitative real-time polymerase chain reaction were used to screen,functionally analyze,and validate the pre-treatment miRNA profiles of the treatment groups.RESULTS Forty-nine miRNAs were differentially expressed between the two groups before UDCA treatment(N=40).MiR-22-5p and miR-126-3p were highly expressed in the DS group before treatment(P<0.001),whereas miR-7706 exhibited a low expression(P=0.017).Post-treatment,miR-126-3p maintained low expression in the drug-resistant group(P=0.003),but showed elevated levels in the DS group(P<0.001).Logistic regression analysis identified miR-126-3p expression(odds ratio=34.32,95%confidence interval:1.95-605.40,P=0.016)as a significant factor influencing UDCA treatment response,while miR-22-5p(P=0.990)and miR-7706(P=0.157)showed no significant association.MiR-126-3p levels were negatively correlated with total bilirubin(r=-0.356,P=0.005)and immuno-globulin G levels(r=-0.311,P=0.015).The area under the receiver operating characteristic curve was 0.891(P=0.0003,95%confidence interval:0.772-1.000)with a sensitivity of 82.4%and a specificity of 84.6%.CONCLUSION Plasma miRNA expression profiles are heterogenous in patients with PBC with differential responses to UDCA therapy.MiR-126-3p demonstrates predictive potential for a suboptimal response to UDCA in patients with PBC.
文摘BACKGROUND Gastric cancer is a malignant tumor with high morbidity and mortality worldwide.Neoadjuvant chemotherapy(NAC),defined as chemotherapy administered before the primary treatment(usually surgery)to reduce tumor size and control micrometastases,has emerged as a crucial therapeutic strategy for locally advanced gastric cancer.Pathological complete response(pCR),characterized by the absence of viable tumor cells in the resected specimen after neoadjuvant treatment,is recognized as a strong predictor of favorable prognosis.However,the factors influencing the achievement of pCR remain incompletely understood.AIM To identify and analyze the predictive factors associated with achieving pCR after NAC in gastric cancer patients,thereby providing evidence-based guidance for clinical decision-making.METHODS A retrospective analysis was performed on 215 patients from Shandong Cancer Hospital and Tai’an Central Hospital with locally advanced gastric cancer who underwent NAC followed by radical surgery at our hospital between January 2015 and December 2023.Comprehensive clinical and pathological data were collected,including age,gender,tumor location,Lauren classification,clinical staging,chemotherapy regimens,number of chemotherapy cycles,and baseline hematological indicators.The baseline hematological indicators included neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio,albumin level,carcinoembryonic antigen(CEA),and carbohydrate antigen 19-9.Univariate and multivariate logistic regression analyses were employed to determine the independent predictive factors for pCR.RESULTS Among 215 gastric cancer patients,41(19.1%)achieved pCR after NAC.Multivariate analysis identified five independent predictive factors for pCR:Lauren intestinal type[odds ratio(OR)=3.28],lower clinical T stage(OR=2.75),CEA decrease≥70%after NAC(OR=3.42),pre-treatment NLR<2.5(OR=2.13),and≥4 chemotherapy cycles(OR=2.87).The fluorouracil,leucovorin,oxaliplatin,docetaxel regimen achieved the highest pCR rate(27.5%),and oxaliplatin-containing regimens were superior to cisplatin-containing regimens(22.3%vs 12.7%,P=0.034).Patients with both low NLR and platelet-to-lymphocyte ratio had the highest pCR rate(33.8%),while those with both high inflammatory markers had the lowest rate(10.7%).Earlier clinical stage disease(cT3N+vs cT4N+:28.6%vs 13.0%)and lower lymph node burden were associated with higher pCR rates.CONCLUSION The achievement of pCR after NAC in gastric cancer patients is closely associated with Lauren intestinal type,lower clinical T stage,a significant decrease in CEA after chemotherapy,low pre-treatment NLR,and an adequate number of chemotherapy cycles.
基金supported by the National Natural Science Foundation of China (82072159)。
文摘BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common malignancies,with high recurrence rates after treatment.Identifying reliable biomarkers for predicting recurrence is essential for improving patient outcomes.Hepatitis B corerelated antigen(HBcrAg)has shown potential as a predictive marker for HCC recurrence.AIM To evaluate the association between HBcrAg levels and the risk of HCC recurrence.METHODS A systematic review was conducted following PRISMA guidelines.PubMed,EMBASE,Web of Science,and the Cochrane Library were searched without restrictions on date or language.Observational studies reporting hazard ratios(HRs)for HBcrAg as a predictor of HCC recurrence were included.Data extraction and quality assessment were performed independently by two reviewers.Statistical analyses used a random-effects model to account for heterogeneity(I²≥50%),and sensitivity analysis was performed to ensure the robustness of the results.RESULTS A total of 1339 articles were initially identified,and 17 studies were included in the final meta-analysis after screening.The pooled analysis showed a significant association between elevated HBcrAg levels and HCC recurrence(HR=4.42,95%confidence interval:3.43-5.41)with substantial heterogeneity(I²=92.6%).Subgroup analysis revealed higher pooled HRs in studies with≥500 participants(HR=4.18)and HBcrAg cut-offs≥4.0 LogU/mL(HR=5.29).Studies with≥10 years of follow-up showed a lower HR(2.89)compared to those with<10 years(3.27).Patients treated with nucleos(t)ide analogs had a pooled HR of 1.98,while those without nucleos(t)ide analog had a higher HR of 3.87.Sensitivity analysis confirmed the robustness of the results,with no significant publication bias detected.CONCLUSION This meta-analysis provides strong evidence that elevated HBcrAg levels are associated with an increased risk of HCC recurrence.HBcrAg may serve as a valuable biomarker for predicting recurrence,aiding personalized management and surveillance strategies for HCC patients.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2024-4-4135.
文摘BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.
基金Supported by National Natural Science Foundation of China,No.81972947Academic Promotion Programme of Shandong First Medical University,No.2019LJ005.
文摘BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).
文摘BACKGROUND Acute kidney injury(AKI)is a severe complication of acute pancreatitis(AP)associated with increased morbidity and mortality.Early prediction of AKI remains a clinical challenge owing to the limitations of traditional biomarkers,such as serum creatinine.AIM To evaluate the concentration and predictive value of plasma neutrophil gelatinase-associated lipocalin(NGAL)in patients with AP and AKI.METHODS This cross-sectional descriptive study was conducted from October 2021 to June 2023 at Bach Mai Hospital.In total,219 patients were enrolled,including 51 patients with AP and AKI,168 patients with AP but without AKI,and 35 healthy controls.Plasma NGAL levels were measured and compared between groups.Receiver operating characteristic curve analysis was performed to determine the predictive value of NGAL levels for the severity of AKI and AP.RESULTS Among AP and AKI cases,47.1%were classified as Kidney Disease:Improving Global Outcomes stage 1,33.3%as stage 2,and 19.6%as stage 3.The AP with AKI group(570.9 ng/mL)had significantly higher median plasma NGAL concentrations than the AP without AKI group(400.6 ng/mL)and the healthy control group(234.3 ng/mL)(P<0.01).The NGAL levels increased proportionally with AKI severity.A plasma NGAL cutoff value of 504.29 ng/mL predicted AKI with 60.8%sensitivity and 68.4%specificity(area under the curve=0.684;P<0.001).A cutoff of 486.03 ng/mL predicted AP severity with 66.1%sensitivity and 66.4%specificity(area under the curve=0.651;P<0.005).NGAL positively correlated with international normalized ratio,urea,creatinine,lactate dehydrogenase,and lactate levels.CONCLUSION Plasma NGAL levels predicted both AKI development and disease severity.Therefore,NGAL should be considered a useful biomarker for the early evaluation of patients with AP.
