BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a prom...BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a promising approach for developing predictive models that can identify high-risk patients and support early clinical intervention.AIM To construct machine learning-based risk prediction models for delayed wound healing after gastric cancer surgery to support clinical decision-making.METHODS We reviewed a total of 514 patients who underwent gastric cancer radical surgery under general anesthesia from January 1,2014 to December 30,2023.Seventy percent of the dataset was selected as the training set and 30%as the validation set.Decision trees,support vector machines,and logistic regression were used to construct a risk prediction model.The performance of the model was evaluated using accuracy,recall,precision,F1 index,and area under the receiver operating characteristic curve and decision curve.RESULTS This study included five variables:Sex,elderly,duration of abdominal drainage,preoperative white blood cell(WBC)count,and absolute value of neutrophils.These variables were selected based on their clinical relevance and statistical significance in predicting delayed wound healing.The results showed that the decision tree model outperformed the logistic regression and support vector machine models in both the training and validation sets.Specifically,the decision tree model achieved higher accuracy,F1 index,recall,and area under the curve(AUC)values.The support vector machine model also demonstrated better performance than logistic regression,with higher accuracy,recall,and F1 index,but a slightly lower AUC.The key variables of sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils were found to be strong predictors of delayed wound healing.Patients with longer duration of abdominal drainage had a significantly higher risk of delayed wound healing,with a risk ratio of 1.579 compared to those with shorter duration of abdominal drainage.Similarly,preoperative WBC count,sex,elderly,and absolute value of neutrophils were associated with a higher risk of delayed wound healing,highlighting the importance of these variables in the model.CONCLUSION The model is able to identify high-risk patients based on sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils can provide valuable insights for clinical decision-making.展开更多
Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC...Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC risk stratification among females.This study aims to develop and validate a score-based prediction model for early detection of HCC in female hepatitis B surface antigen(HBsAg)carriers.Methods:Participants were recruited from a multicenter prospective cohort engaged in liver cancer screening across China including seven high-risk rural areas and one additional high-risk rural area.The study involved 7080 females as the derivation cohort and 2069 as the validation cohort,with all participants aged 35-70 years and HBsAg positive.Laboratory tests and epidemiological surveys were conducted.Key predictor variables were identified through LASSO regression analysis,and score-based prediction models were developed based on Cox proportional hazards model.Model performance including discrimination and calibration was evaluated,and compared to existing prediction models and screening strategies.Results:After a median follow-up of 3.69 and 5.42 years,147 and 45 HCC cases were identified in the derivation and validation cohorts,respectively.The female HCC(HCCF)model incorporating five independent variables:age,α-fetoprotein(AFP),albumin,alanine aminotransferase,and platelet,showed excellent performance with an area under the receiver operating characteristic curve(AUC)of 0.82(95%CI:0.78-0.86).The HCCF-Enhanced model which included cirrhosis,achieved an AUC of 0.85(95%CI:0.81-0.89).Both models demonstrated superior predictive performance than existing models,with strong predictive accuracy in the validation cohort:AUCs of 0.83(95%CI:0.77-0.89)and 0.88(95%CI:0.83-0.92),respectively.The HCCF model,at a score threshold of 7,achieved the largest Youden’s index and identified 32.80%of high-risk individuals.When combined with ultrasonography(US),the model detected 37 additional cases,significantly improved screening sensitivity and accuracy compared to the traditional AFP plus US strategy.Conclusions:The developed HCCF models with good performance for HCC prediction in HBsAg-positive females significantly improve screening efficiency and provide an effective tool for surveillance,ultimately helping to optimize prevention and management strategies for HCC.展开更多
AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequenci...AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.展开更多
Objective This study aimed to develop a prediction model to assess the risk of sepsis-induced coagulopathy(SIC)in sepsis patients.Methods We conducted a retrospective study of septic patients admitted to the Intensive...Objective This study aimed to develop a prediction model to assess the risk of sepsis-induced coagulopathy(SIC)in sepsis patients.Methods We conducted a retrospective study of septic patients admitted to the Intensive Care Units of Shandong Provincial Hospital(Central Campus and East Campus),and Shenxian People’s Hospital from January 2019 to September 2024.We used Kaplan-Meier analysis to assess survival outcomes.LASSO regression identified predictive variables,and logistic regression was employed to analyze risk factors for pre-SIC.A nomogram prediction model was developed via R software and evaluated via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).Results Among 309 patients,236 were in the training set,and 73 were in the test set.The pre-SIC group had higher mortality(44.8%vs.21.3%)and disseminated intravascular coagulation(DIC)incidence(56.3%vs.29.1%)than the non-SIC group.LASSO regression identified lactate,coagulation index,creatinine,and SIC scores as predictors of pre-SIC.The nomogram model demonstrated good calibration,with an AUC of 0.766 in the development cohort and 0.776 in the validation cohort.DCA confirmed the model’s clinical utility.Conclusion SIC is associated with increased mortality,with pre-SIC further increasing the risk of death.The nomogram-based prediction model provides a reliable tool for early SIC identification,potentially improving sepsis management and outcomes.展开更多
BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with ment...BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.展开更多
This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk facto...This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.展开更多
Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pre...Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as...BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.展开更多
Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following P...Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following PRISMA guidelines,we searched eight databases(PubMed,Embase,Cochrane Library,Web of Science,CINAHL,CNKI,Wanfang,VIP)from inception to May 2025.Studies developing or validating DVT prediction models in elderly hip fracture patients were included.Two reviewers independently screened studies,extracted data,and assessed risk of bias and applicability using the PROBAST tool.Results:Eleven studies were included,all conducted in China between 2021 and 2025.Sample sizes ranged from 101 to 504 patients(total n=3,286).Models incorporated 3 to 9 predictors,with D-dimer,age,and time from injury to surgery being most common.All 11 studies(100%)were rated as high risk of bias,primarily due to small sample sizes,lack of validation,and inadequate missing data handling.Applicability concerns were low in 8 studies(72.7%).AUC values ranged from 0.648 to 0.967,with 10 studies(90.9%)reporting AUC>0.7.Meta-analysis identified time from injury to surgery(OR=4.63,95%CI:2.58–6.68),age(OR=1.99),D-dimer(OR=1.51),and Caprini score(OR=1.75)as significant predictors.Conclusion:Current DVT prediction models for elderly hip fracture patients demonstrate acceptable discrimination but are limited by high risk of bias and lack of external validation.Prospective,multicenter studies with rigorous validation are needed to develop clinically applicable models.展开更多
Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including...Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.展开更多
Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,...Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.展开更多
AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary ...AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary RRD treated at the Shenzhen Eye Hospital were retrospectively collected.Twenty-four potential influencing factors,including patient characteristics and surgical factors,were selected for analysis.Independent risk factors for secondary cataract were identified through univariate comparisons and multivariate logistic regression analysis.A risk prediction model was constructed and evaluated using receiver operating characteristic(ROC)curves,area under the ROC curve(AUC),calibration plots,and decision curve analysis(DCA)curves.RESULTS:The 386 cases(389 eyes)of patients who underwent PPV and had complete surgical records were ultimately included.Within a 2-year longitudinal observation,41.39%of patients developed cataract secondary to PPV.Logistic regression results identified a history of hypertension[odds ratio(OR)=1.78,95%CI:1.002–3.163,P=0.049],silicone oil tamponade(OR=3.667,95%CI:2.373–5.667,P=0.000),and lens thickness(OR=1.978,95%CI:1.129–3.464,P=0.017)as independent risk factors for cataract secondary to PPV.The constructed nomogram achieved AUC=0.6974.Calibration plots indicated good agreement between predicted and observed outcomes,while DCA curves demonstrated the model’s clinical utility.CONCLUSION:By incorporating a history of hypertension,vitreous substitute type,and lens thickness,this study constructs a prediction model with moderate discriminative ability.This model offers a valuable tool for clinicians to identify high-risk patients early,potentially allowing for more timely interventions and improved patient outcomes.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interac...The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.展开更多
This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfectio...This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.展开更多
Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction mode...Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.展开更多
Objective:To develop and validate a risk prediction model for catheter-related thrombosis(CRT)in pediatric patients with severe traumatic brain injury(sTBI).Methods:Using convenience sampling,216 pediatric patients wi...Objective:To develop and validate a risk prediction model for catheter-related thrombosis(CRT)in pediatric patients with severe traumatic brain injury(sTBI).Methods:Using convenience sampling,216 pediatric patients with sTBI admitted to the Surgical Intensive Care Unit of Kunming Children’s Hospital between June 2022 and May 2025 were enrolled and randomly divided into a training set of 151 cases and a validation set of 65 cases.Influencing factors were identified through univariate analysis and logistic regression analysis to construct the prediction model.The model’s discrimination and calibration were evaluated by the area under the receiver operating characteristic(ROC)curve(AUC)and the Hosmer–Lemeshow goodness-of-fit test.Results:Univariate analysis showed that admission GCS score,CVC insertion site,D-dimer level,and duration of mechanical ventilation were risk factors for CRT in children with sTBI(P<0.05).The logistic regression equation was constructed as follows:Logit(P)=2.74–1.95×GCS score+0.25×D-dimer(μg/mL)+0.02×duration of mechanical ventilation(h).Based on this model,the AUC was 0.87 in the training set and 0.88 in the validation set.The Hosmer–Lemeshow goodness-of-fit test indicated good agreement between the model’s calibration curve and the ideal curve.Conclusion:The developed prediction model demonstrates good predictive performance and can serve as a reference for the early clinical identification of CRT risk in pediatric patients with sTBI.展开更多
BACKGROUND Chronic hepatitis B(CHB)patients rarely achieve functional cure with initial pegylated interferon alpha-2b(Peg-IFNα-2b)therapy.Validated tools to guide retreatment candidates are lacking.We hypothesized th...BACKGROUND Chronic hepatitis B(CHB)patients rarely achieve functional cure with initial pegylated interferon alpha-2b(Peg-IFNα-2b)therapy.Validated tools to guide retreatment candidates are lacking.We hypothesized that clinical indicators predict hepatitis B surface antigen(HBsAg)clearance during retreatment.AIM To develop a prediction model for HBsAg clearance in Peg-IFNα-2b retreatment.METHODS In this retrospective cohort study,we enrolled 135 CHB/compensated cirrhosis patients receiving Peg-IFNα-2b retreatment after initial non-clearance at Tianjin University Central Hospital(2017-2025).Predictors were identified through univariate Cox,least absolute shrinkage and selection operator,and multivariate Cox regression.Model performance was assessed via receiver operating characteristic analysis and Harrell’s C-index,with risk stratification by X-tile optimization.RESULTS HBsAg clearance rate was 20.74%(28/135).Independent predictors included:Combination nucleos(t)ide analogue(NA)therapy during initial treatment[hazard ratio(HR)=0.276,95%confidence interval(CI):0.092-0.833],baseline HBsAg at retreatment(HR=0.571,95%CI:0.410-0.795),HBsAg decline after initial treatment(HR=2.050,95%CI:1.108-3.793),and treatment interval(HR=1.013/week,95%CI:1.008-1.018).The retreatment HBsAg clearance prediction score(RHCP-S)demonstrated area under the curve of 0.920(95%CI:0.863-0.946),sensitivity of 92.3%,specificity of 79.3%.Clearance rates differed significantly:RHCP-S challenge group(≤74 points):3.45%,RHCP-S probable group(74-110 points):29.63%,RHCP-S dominant group(≥110 points):80.95%(P<0.001).CONCLUSION The overall HBsAg clearance rate with Peg-IFNα-2b retreatment was 20.74%(28/135).The RHCP-S model identifies optimal retreatment candidates(≥110 points)with 80.95%clearance probability,associated with the absence of combination NA therapy during initial treatment,greater initial HBsAg decline,longer intervals,and lower retreatment HBsAg.展开更多
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金Supported by the Shandong Province Traditional Chinese Medicine Technology Project,No.Q-2023147the Weifang Health Commission Research Project,No.WFWSJK-2023-033+3 种基金the Weifang City Science and Technology Development Plan(Medical Category),No.2023YX057the Weifang Medical University 2022 Campus Level Education and Teaching Reform and Research Project,No.2022YB051Norman Bethune Public Welfare Foundation,No.ezmr2023-037Special Research Project on Optimized Management of Acute Pain,Wu Jieping Medical Foundation.
文摘BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a promising approach for developing predictive models that can identify high-risk patients and support early clinical intervention.AIM To construct machine learning-based risk prediction models for delayed wound healing after gastric cancer surgery to support clinical decision-making.METHODS We reviewed a total of 514 patients who underwent gastric cancer radical surgery under general anesthesia from January 1,2014 to December 30,2023.Seventy percent of the dataset was selected as the training set and 30%as the validation set.Decision trees,support vector machines,and logistic regression were used to construct a risk prediction model.The performance of the model was evaluated using accuracy,recall,precision,F1 index,and area under the receiver operating characteristic curve and decision curve.RESULTS This study included five variables:Sex,elderly,duration of abdominal drainage,preoperative white blood cell(WBC)count,and absolute value of neutrophils.These variables were selected based on their clinical relevance and statistical significance in predicting delayed wound healing.The results showed that the decision tree model outperformed the logistic regression and support vector machine models in both the training and validation sets.Specifically,the decision tree model achieved higher accuracy,F1 index,recall,and area under the curve(AUC)values.The support vector machine model also demonstrated better performance than logistic regression,with higher accuracy,recall,and F1 index,but a slightly lower AUC.The key variables of sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils were found to be strong predictors of delayed wound healing.Patients with longer duration of abdominal drainage had a significantly higher risk of delayed wound healing,with a risk ratio of 1.579 compared to those with shorter duration of abdominal drainage.Similarly,preoperative WBC count,sex,elderly,and absolute value of neutrophils were associated with a higher risk of delayed wound healing,highlighting the importance of these variables in the model.CONCLUSION The model is able to identify high-risk patients based on sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils can provide valuable insights for clinical decision-making.
基金supported by the Capital’s Funds for Health Improve-ment and Research(grant number:2024-1G-4023)。
文摘Background:Existing hepatocellular carcinoma(HCC)prediction models lack transferability and generalizability when applied to female populations,resulting in diminished performance and inadequate tools for accurate HCC risk stratification among females.This study aims to develop and validate a score-based prediction model for early detection of HCC in female hepatitis B surface antigen(HBsAg)carriers.Methods:Participants were recruited from a multicenter prospective cohort engaged in liver cancer screening across China including seven high-risk rural areas and one additional high-risk rural area.The study involved 7080 females as the derivation cohort and 2069 as the validation cohort,with all participants aged 35-70 years and HBsAg positive.Laboratory tests and epidemiological surveys were conducted.Key predictor variables were identified through LASSO regression analysis,and score-based prediction models were developed based on Cox proportional hazards model.Model performance including discrimination and calibration was evaluated,and compared to existing prediction models and screening strategies.Results:After a median follow-up of 3.69 and 5.42 years,147 and 45 HCC cases were identified in the derivation and validation cohorts,respectively.The female HCC(HCCF)model incorporating five independent variables:age,α-fetoprotein(AFP),albumin,alanine aminotransferase,and platelet,showed excellent performance with an area under the receiver operating characteristic curve(AUC)of 0.82(95%CI:0.78-0.86).The HCCF-Enhanced model which included cirrhosis,achieved an AUC of 0.85(95%CI:0.81-0.89).Both models demonstrated superior predictive performance than existing models,with strong predictive accuracy in the validation cohort:AUCs of 0.83(95%CI:0.77-0.89)and 0.88(95%CI:0.83-0.92),respectively.The HCCF model,at a score threshold of 7,achieved the largest Youden’s index and identified 32.80%of high-risk individuals.When combined with ultrasonography(US),the model detected 37 additional cases,significantly improved screening sensitivity and accuracy compared to the traditional AFP plus US strategy.Conclusions:The developed HCCF models with good performance for HCC prediction in HBsAg-positive females significantly improve screening efficiency and provide an effective tool for surveillance,ultimately helping to optimize prevention and management strategies for HCC.
基金Supported by the National Natural Science Foundation of China(No.82220108017,No.82141128,No.82101180)Beijing Natural Science Foundation(No.Z220012)+3 种基金The Capital Health Research and Development of Special(No.2020-1-2052)Science&Technology Project of Beijing Municipal Science&Technology Commission(No.Z201100005520045)Sanming Project of Medicine in Shenzhen(No.SZSM202311018)Beijing Science&Technology Development of TCM(No.BJZYYB-2023-17).
文摘AIM:To explore the relationship between matrix metalloproteinases(MMPs)expression levels in the tumor and the prognosis of uveal melanoma(UM)and to construct prognostic prediction models.METHODS:Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected.Based on the differential gene expression levels and their function,MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning.Tumor microenvironment(TME)analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.RESULTS:Eight MMPs were significantly different expression levels between normal and the tumor tissues.MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high-and low-risk groups.The prediction model based on the risk-score achieved an accuracy of approximately 80%at 1-,3-,and 5-year after diagnosis.Besides,a Nomogram prognostic prediction model which based on risk-score and pathological type(independent prognostic factors after Cox regression analysis)demonstrated good consistency between the predicted outcomes at 1-,3-,and 5-year after diagnosis and the actual prognosis of patients.TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages(TAMs)and regulatory T cells compared to the low-risk group.CONCLUSION:Based on MMP-2 and MMP-28 expression levels,our prediction model demonstrates accurate long-term prognosis prediction for UM patients.The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.
基金funded by the Shandong Provincial Natural Science Foundation(No.ZR2024MH008)Postdoctoral Innovation Program of Shandong Province(No.SDCX-ZG-202400043)Beijing iGandan Foundation(No.iGandanF-1082022-RGG007).
文摘Objective This study aimed to develop a prediction model to assess the risk of sepsis-induced coagulopathy(SIC)in sepsis patients.Methods We conducted a retrospective study of septic patients admitted to the Intensive Care Units of Shandong Provincial Hospital(Central Campus and East Campus),and Shenxian People’s Hospital from January 2019 to September 2024.We used Kaplan-Meier analysis to assess survival outcomes.LASSO regression identified predictive variables,and logistic regression was employed to analyze risk factors for pre-SIC.A nomogram prediction model was developed via R software and evaluated via receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).Results Among 309 patients,236 were in the training set,and 73 were in the test set.The pre-SIC group had higher mortality(44.8%vs.21.3%)and disseminated intravascular coagulation(DIC)incidence(56.3%vs.29.1%)than the non-SIC group.LASSO regression identified lactate,coagulation index,creatinine,and SIC scores as predictors of pre-SIC.The nomogram model demonstrated good calibration,with an AUC of 0.766 in the development cohort and 0.776 in the validation cohort.DCA confirmed the model’s clinical utility.Conclusion SIC is associated with increased mortality,with pre-SIC further increasing the risk of death.The nomogram-based prediction model provides a reliable tool for early SIC identification,potentially improving sepsis management and outcomes.
基金Supported by the 2024 Yiwu City Research Plan Project,No.24-3-102.
文摘BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.
文摘This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.
基金Supported by the Qihuang Scholars Program in 202114th Five-Year National Key R&D Program Project:2022YFC3500504。
文摘Objective:To construct a clinical prediction model of acupuncture treatment for diminished ovarian reserve(DOR)based on a machine learning algorithm to provide a clinical prediction of acupuncture for ameliorating pregnancy outcomes in DOR.Methods:We enrolled 377 DOR patients treated with acupuncture and with records of pregnancy outcomes(139 cases of pregnancy and 238 cases failed)exported from the International Patient Registry Platform of Acupuncture-moxibustion(IPRPAM).The predictive variables were determined using Spearman’s correlation analysis and feature engineering methods.The model was constructed by adopting logistic regression,naïve Bayes,random forest,support vector machine,extreme gradient boosting,the knearest neighbor algorithm,linear discriminant analysis,and neural network methods.The models were validated by the area under the curve(AUC),accuracy(ACC),and importance sequencing,and individual pregnancy prediction was conducted for the best-performing model.Results:The key factors determining pregnancy after acupuncture in patients with DOR were age,luteinizing hormone(LH)level after treatment,follicle-stimulating hormone(FSH)level after treatment,the ratio of FSH to LH(FSH/LH)after treatment,and history of acupuncture treatment.Random forest model ACC was 0.95,Fβwas 0.93,Logloss was 0.30,Logloss value was the lowest,the model variables exhibited the highest accuracy and precision.Conclusion:The random forest model for the effects of acupuncture on pregnancy outcomes in patients with DOR,constructed based on the IPRPAM,presents a favorable value for clinical application.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is a prevalent metabolic disorder increasingly linked with hypertension,posing significant health risks.The need for a predictive model tailored for T2DM patients is evident,as current tools may not fully capture the unique risks in this population.This study hypothesizes that a nomogram incorporating specific risk factors will improve hypertension risk prediction in T2DM patients.AIM To develop and validate a nomogram prediction model for hypertension in T2DM patients.METHODS A retrospective observational study was conducted using data from 26850 T2DM patients from the Anhui Provincial Primary Medical and Health Information Management System(2022 to 2024).The study included patients aged 18 and above with available data on key variables.Exclusion criteria were type 1 diabetes,gestational diabetes,insufficient data,secondary hypertension,and abnormal liver and kidney function.The Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to construct the nomogram,which was validated on separate datasets.RESULTS The developed nomogram for T2DM patients incorporated age,low-density lipoprotein,body mass index,diabetes duration,and urine protein levels as key predictive factors.In the training dataset,the model demonstrated a high discriminative power with an area under the receiver operating characteristic curve(AUC)of 0.823,indicating strong predictive accuracy.The validation dataset confirmed these findings with an AUC of 0.812.The calibration curve analysis showed excellent agreement between predicted and observed outcomes,with absolute errors of 0.017 for the training set and 0.031 for the validation set.The Hosmer-Lemeshow test yielded non-significant results for both sets(χ^(2)=7.066,P=0.562 for training;χ^(2)=6.122,P=0.709 for validation),suggesting good model fit.CONCLUSION The nomogram effectively predicts hypertension risk in T2DM patients,offering a valuable tool for personalized risk assessment and guiding targeted interventions.This model provides a significant advancement in the management of T2DM and hypertension comorbidity.
文摘Objective:To systematically evaluate prediction models for postoperative deep vein thrombosis(DVT)in elderly hip fracture patients and assess their methodological quality and predictive performance.Methods:Following PRISMA guidelines,we searched eight databases(PubMed,Embase,Cochrane Library,Web of Science,CINAHL,CNKI,Wanfang,VIP)from inception to May 2025.Studies developing or validating DVT prediction models in elderly hip fracture patients were included.Two reviewers independently screened studies,extracted data,and assessed risk of bias and applicability using the PROBAST tool.Results:Eleven studies were included,all conducted in China between 2021 and 2025.Sample sizes ranged from 101 to 504 patients(total n=3,286).Models incorporated 3 to 9 predictors,with D-dimer,age,and time from injury to surgery being most common.All 11 studies(100%)were rated as high risk of bias,primarily due to small sample sizes,lack of validation,and inadequate missing data handling.Applicability concerns were low in 8 studies(72.7%).AUC values ranged from 0.648 to 0.967,with 10 studies(90.9%)reporting AUC>0.7.Meta-analysis identified time from injury to surgery(OR=4.63,95%CI:2.58–6.68),age(OR=1.99),D-dimer(OR=1.51),and Caprini score(OR=1.75)as significant predictors.Conclusion:Current DVT prediction models for elderly hip fracture patients demonstrate acceptable discrimination but are limited by high risk of bias and lack of external validation.Prospective,multicenter studies with rigorous validation are needed to develop clinically applicable models.
基金Supported by the Science Planning Project of Liaoning Province,No.2019JH2/10300031-05the National Natural Science Foundation of China,No.12171074.
文摘Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.
基金supported by Guangdong Medical Science and Technology Research Fund Project(No.A2022458)Guangdong Provincial People's Medical Climbing Plan(Nursing Research Project)(No.DFJH2020011)。
文摘Background1 Currently,there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF).This study aimed to investigate the frailty status of hospitalized CHF patients,identify independent risk factors significantly associated with frailty,and construct an effective risk prediction model.The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients.Methodss Using convenience sampling,we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024.General demographic data were collected via questionnaires,alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL)scale.Clinical data were obtained by reviewing medical records.Participants were categorized into a frail group(n=65)and a non-frail group(n=119)based on frailty status.Clinical risk factors were compared between groups.Multivariate logistic regression was used to identify independent risk factors.A prediction model was constructed,and a receiver operating characteristic(ROC)curve was plotted to evaluate its predictive value.Results A total of 184 hospitalized CHF patients were included,with 65(35.33%)exhibiting frailty.Multivariate logistic regression analysis showed that independent risk factors for frailty included:age,ADL score,N-terminal pro-brain natriuretic peptide(NT-pro-BNP),left ventricular ejection fraction(LVEF),New York Heart Association(NYHA)class II/IV,≥3 comorbidities,comorbid diabetes mellitus(DM),comorbid valvular heart disease(VHD),smoking history,hemoglobin(Hb),albumin,high-density lipoprotein cholesterol(HDL-C),low-density lipoprotein cholesterol(LDL-C),creatinine(Cr),and blood urea nitrogen(BUN).The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built.The prediction model showed quite strong predictive performance.Its area under the ROC curve was 0.904(95%CI:0.857-0.951),with a sensitivity of98.5%and a specificity of 85.7%.ConclusionssThe frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration.It provided substantial reference value for clinical management of CHF,offering a basis for early assessment,risk stratification,and targeted interventions to prevent frailty by identifying high-risk patients.
基金Supported by the Shenzhen Science and Technology Program(No.JCYJ20220818103207015)the SanMing Project of Medicine in Shenzhen(No.SZSM202311012).
文摘AIM:To establish a risk prediction model for secondary cataract within 2y after pars plana vitrectomy(PPV)in patients with primary rhegmatogenous retinal detachment(RRD).METHODS:Clinical data of patients with primary RRD treated at the Shenzhen Eye Hospital were retrospectively collected.Twenty-four potential influencing factors,including patient characteristics and surgical factors,were selected for analysis.Independent risk factors for secondary cataract were identified through univariate comparisons and multivariate logistic regression analysis.A risk prediction model was constructed and evaluated using receiver operating characteristic(ROC)curves,area under the ROC curve(AUC),calibration plots,and decision curve analysis(DCA)curves.RESULTS:The 386 cases(389 eyes)of patients who underwent PPV and had complete surgical records were ultimately included.Within a 2-year longitudinal observation,41.39%of patients developed cataract secondary to PPV.Logistic regression results identified a history of hypertension[odds ratio(OR)=1.78,95%CI:1.002–3.163,P=0.049],silicone oil tamponade(OR=3.667,95%CI:2.373–5.667,P=0.000),and lens thickness(OR=1.978,95%CI:1.129–3.464,P=0.017)as independent risk factors for cataract secondary to PPV.The constructed nomogram achieved AUC=0.6974.Calibration plots indicated good agreement between predicted and observed outcomes,while DCA curves demonstrated the model’s clinical utility.CONCLUSION:By incorporating a history of hypertension,vitreous substitute type,and lens thickness,this study constructs a prediction model with moderate discriminative ability.This model offers a valuable tool for clinicians to identify high-risk patients early,potentially allowing for more timely interventions and improved patient outcomes.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
基金supported by JUST Start-up Fund for Science Research,the Jiangsu Natural Science Foundation(Grant No.BK20210885).
文摘The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.
文摘This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.
文摘Background: Pancreatic cancer is one of the most lethal malignancies, with postoperative recurrence severely affecting patient survival and prognosis. This study aims to develop and validate a clinical prediction model for postoperative recurrence in pancreatic cancer patients, incorporating multiple preoperative, intraoperative, and postoperative factors to assist clinical decision-making. Methods: A retrospective study was conducted on 216 patients who underwent surgical treatment for pancreatic malignancy at the First Affiliated Hospital of Chongqing Medical University between January 2015 and January 2023. An independent external validation cohort of 76 patients from the Second Affiliated Hospital of Chongqing Medical University was used to validate the model. Seven independent risk factors for postoperative recurrence were identified through univariate and multivariate Cox regression analyses. The model’s performance was evaluated using the concordance index (C-index) and ROC curves, and its accuracy and clinical value were assessed using calibration curves and decision curve analysis (DCA). Results: The predictive model demonstrated good discriminatory power, with a C-index of 0.72 in the training cohort and 0.66 in the validation cohort. The ROC curves for predicting recurrence at 3, 6, and 12 months postoperatively showed AUC values ranging from 0.72 to 0.83, indicating strong predictive value. Calibration curves and DCA confirmed the model’s accuracy and clinical utility. Conclusion: This study successfully developed and validated a clinical prediction model that incorporates seven independent risk factors for postoperative recurrence in pancreatic cancer. The model provides a useful tool for predicting recurrence risk, aiding in the identification of high-risk patients, and informing clinical decision-making.
基金Health Science Research Project of Kunming Health Committee,Yunnan Province(Project No.:2023-14-04-008)。
文摘Objective:To develop and validate a risk prediction model for catheter-related thrombosis(CRT)in pediatric patients with severe traumatic brain injury(sTBI).Methods:Using convenience sampling,216 pediatric patients with sTBI admitted to the Surgical Intensive Care Unit of Kunming Children’s Hospital between June 2022 and May 2025 were enrolled and randomly divided into a training set of 151 cases and a validation set of 65 cases.Influencing factors were identified through univariate analysis and logistic regression analysis to construct the prediction model.The model’s discrimination and calibration were evaluated by the area under the receiver operating characteristic(ROC)curve(AUC)and the Hosmer–Lemeshow goodness-of-fit test.Results:Univariate analysis showed that admission GCS score,CVC insertion site,D-dimer level,and duration of mechanical ventilation were risk factors for CRT in children with sTBI(P<0.05).The logistic regression equation was constructed as follows:Logit(P)=2.74–1.95×GCS score+0.25×D-dimer(μg/mL)+0.02×duration of mechanical ventilation(h).Based on this model,the AUC was 0.87 in the training set and 0.88 in the validation set.The Hosmer–Lemeshow goodness-of-fit test indicated good agreement between the model’s calibration curve and the ideal curve.Conclusion:The developed prediction model demonstrates good predictive performance and can serve as a reference for the early clinical identification of CRT risk in pediatric patients with sTBI.
基金Supported by the Tianjin Health Research Project(Key Project),No.TJWJ2024ZD004Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-034A.
文摘BACKGROUND Chronic hepatitis B(CHB)patients rarely achieve functional cure with initial pegylated interferon alpha-2b(Peg-IFNα-2b)therapy.Validated tools to guide retreatment candidates are lacking.We hypothesized that clinical indicators predict hepatitis B surface antigen(HBsAg)clearance during retreatment.AIM To develop a prediction model for HBsAg clearance in Peg-IFNα-2b retreatment.METHODS In this retrospective cohort study,we enrolled 135 CHB/compensated cirrhosis patients receiving Peg-IFNα-2b retreatment after initial non-clearance at Tianjin University Central Hospital(2017-2025).Predictors were identified through univariate Cox,least absolute shrinkage and selection operator,and multivariate Cox regression.Model performance was assessed via receiver operating characteristic analysis and Harrell’s C-index,with risk stratification by X-tile optimization.RESULTS HBsAg clearance rate was 20.74%(28/135).Independent predictors included:Combination nucleos(t)ide analogue(NA)therapy during initial treatment[hazard ratio(HR)=0.276,95%confidence interval(CI):0.092-0.833],baseline HBsAg at retreatment(HR=0.571,95%CI:0.410-0.795),HBsAg decline after initial treatment(HR=2.050,95%CI:1.108-3.793),and treatment interval(HR=1.013/week,95%CI:1.008-1.018).The retreatment HBsAg clearance prediction score(RHCP-S)demonstrated area under the curve of 0.920(95%CI:0.863-0.946),sensitivity of 92.3%,specificity of 79.3%.Clearance rates differed significantly:RHCP-S challenge group(≤74 points):3.45%,RHCP-S probable group(74-110 points):29.63%,RHCP-S dominant group(≥110 points):80.95%(P<0.001).CONCLUSION The overall HBsAg clearance rate with Peg-IFNα-2b retreatment was 20.74%(28/135).The RHCP-S model identifies optimal retreatment candidates(≥110 points)with 80.95%clearance probability,associated with the absence of combination NA therapy during initial treatment,greater initial HBsAg decline,longer intervals,and lower retreatment HBsAg.
