Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.

Objective To explore the association between walking exercise guided by 6 minute walking test(6MWT)and the incidences of 3-year major adverse cardiovascular event(MACE)in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 628 elderly ACS patients who undergoing PCI and obtaining success-ful coronary revascularization in our department from November 2018 to April 2019 were enrolled,and divided into 6MWT group(n=147)and control group(n=481)based on participa-ting in walking exercise guided by 6MWT or not.All of them were followed up for 3 years.The incidences of MACE[including coronary target vascular restenosis,acute myocardial infarction,heart failure,ischemic or hemorrhagic stroke]and all-cause death were observed.Univariate and multivariate Cox proportional analyses and Kaplan-Meier survival curve analysis were employed for data statistical analyses.Results At the end of follow-up,the incidences of target vascular restenosis(6.9％vs 2.0％,P=0.028),heart failure(3.7％vs 0％,P=0.036),stroke(3.7％vs 0％,P=0.036),and total MACE incidence(15.0％vs 4.1％,P=0.000)were statistically higher in the control group than the 6MWT group.Kaplan-Meier survival curve analysis showed that the cumulative incidence of MACE was significantly lower in the 6MWT group than the control group(Plog rank=0.001).Multivariate Cox regression analysis showed that not participating in walking exercise guided by 6MWT was an independent risk factor for occurrence of 3-year MACE(HR=3.102,95％CI:1.327-7.250,P=0.009).Conclusion Walking exercise guided by 6MWT reduces the incidence of 3-year MACE and improves the long-term prognosis of elderly ACS patients after PCI.

OBJECTIVE To investigate the prevalence of plasma vitamin B1 deficiency in patients with sepsis and analyze the relationship between plasma vitamin B1 levels and lactic acidosis.METHODS A total of 40 patients with sepsis and 42 patients with sepsis shock admitted to the intensive care unit(ICU)of General Hospital of Ningxia Medical University from Nov.2020 to Nov.2022 were selected as the study objects.Volunteers matched by age,gender and body mass index(BMI)were selected as the healthy group.Data on gender,age,BMI,under-lying diseases,sequential organ failure assessment(SOFA)score,acute physiology and chronic health evaluation(APACHE)Ⅱ score,infection site,duration of mechanical ventilation and ICU length of stay were collected.La-boratory tests including blood routine,electrolytes,blood lactate(Lac),alanine aminotransferase(ALT),aspar-tate aminotransferase(AST),blood urea nitrogen(BUN)and serum creatinine(Scr)were performed in both groups.Plasma samples from both disease group and the healthy group were collected.High-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS)was used to measure plasma thiamine(vitamin B1)levels in the disease group on the first,third and fifth days,respectively,as well as in the healthy group.RESULTS On the first day of admission,both the sepsis group and the septic shock group exhibited vitamin B1 de-ficiency.Specifically,6 cases of vitamin B1 deficiency were observed in the patients with sepsis,while 8 cases of vitamin B1 deficiency were noted in the patients with septic shock(P=0.640).By the third and fifth days,the proportions of vitamin B1 deficiency in the sepsis group were 22.50％and 42.50％,respectively,whereas in the septic shock group,these proportions were high at 26.19％and 54.76％,with a statistically significant difference(P＜0.05).On the fifth day,in the normal liver function group,vitamin B1 levels showed a negative correlation with lactate(r=-0.590,P=0.005),whereas in the abnormal liver function group,vitamin B1 levels were pos-itively correlated with lactate(r=0.678,P=0.017).The patients in the septic shock group had low K+and ALB levels[(2.60±0.42)mmol/L and(24.56±5.78)g/L,respectively]compared to those in the sepsis group(P＜0.05).CONCLUSIONS Plasma vitamin B1 deficiency is prevalent among patients with sepsis.Changes in plasma vitamin B1 levels are closely associated with blood lactate levels in these patients.

Objective To explore the clinical value of three-dimensional(3D)fracture mapping in improving the consistency of Arbeitsgemeinschaft für Osteosynthesefragen(AO)/the Orthopaedic Trauma Association(OTA)classification and optimizing preoperative surgical planning for Pilon fractures.Methods This single-center retrospective cohort study included 60 Pilon fracture patients admitted to the Trauma Emergency Center of the Third Hospital of Hebei Medical University between January 2022 and December 2024.All patients underwent preoperative computed tomography(CT)scans.Image standardization and expert manual segmentation/annotation of fracture lines and fragments were performed with 3D Slicer software.3D fracture lines extracted from gold-standard models were registered to a unified standard tibial model.A 3D probability heatmap was constructed by counting spatial fracture frequency,with high-incidence zones analyzed via spatial clustering algorithms.Three orthopedic surgeons independently completed AO/OTA classification and preoperative planning with the assistance of conventional CT only and CT with 3D fracture mapping.Accuracy,time consumption,inter-observer consistency(Cohen's κ),planning time,plan modification frequency,and subjective scores were evaluated.Results The 3D fracture heatmap revealed that fracture lines predominantly concentrated in the anterolateral and posteromedial regions of the distal tibia,with an average of(4.2±1.1)hotspots,a coverage rate of(78.3±5.6)%,and(3.5±1.0)clustering areas.With 3D fracture mapping assistance,classification accuracy was improved to(88.0±5.0)%compared to(75.0±8.0)%with conventional CT(P=0.001);classification time reduced to(10.4±2.5)min from(15.2±3.1)min(P<0.001);and Cohen's κ increased from 0.68±0.05 to 0.82±0.03(P=0.002).For preoperative planning,the average planning time was(15.8±3.2)min in the 3D mapping-assisted group,which was significantly shorter than that of conventional CT group(22.5±4.3)min(P<0.001);the number of plan modifications was(1.5±0.7)times,lower than that of conventional CT group(3.2±1.1)times(P<0.001),and the subjective score was 8.9±0.9,higher than that of conventional CT group(6.8±1.2)(P<0.001).Conclusion The 3D fracture mapping accurately characterizes spatial distribution patterns of Pilon fractures,significantly improves classification accuracy,inter-observer consistency,and preoperative planning efficiency,and thus holds substantial clinical value.

Objective To analyze the risk factors for catheter-related thrombosis(CRT)in the upper arm infusion port(UAP)and to construct a machine-learning prediction model.Methods A total of 6028 patients,who received UAP implantation at Shanghai Renji Hospital of China from February 2014 to February 2023,were enrolled in this study.The patients were divided into training set(n=4 219)and validation set(n=1 809).Six machine-learning prediction models,including Least Absolute Shrinkage and Selection Operator(LASSO)regression,random forest,decision tree,neural network,XGBoost and logistic,were constructed,and the model having best performance was selected as the optimal model.SHapely Additive exPlanations(SHAP)analysis was used to explain the neural network model,and DALEXtra package was used to explain the continuous variables.Results The neural network model was chosen as the final model.The variables,in order of the degree of importance from high to low,included sex,the diameter of catheter,catheter tip confirmation method,the length of catheter,inpatient or outpatient status,history of central venous catheter implantation,the length of subcutaneous tunnel,age,body mass index(BMI),primary tip displacement,and left or right venous approach.The learning curve,i.e.the area under curve(AUC)of the receiver operating characteristic(ROC)curve,for the training set was＞0.6,and the Delong testing and Bootstrap Methods Test showed that the neural network model performed well(P＜0.05).The Kolmogorov-Smirnov plot(KS plot)value was 0.313 5,indicating that the model had the good ability of discrimination.The clinical impact curve(CIC)assessment revealed that the model had good clinical value.Conclusion The machine-learning prediction model of upper arm infusion port with CRT has been successfully constructed.For minimizing the risk of CRT,it is recommended to prioritize the use of 5 F diameter catheters,adopt left-sided venous approach and positioning the tip of the catheter based on anatomical measurements,besides,the catheter length should be not shorter than 36.56 cm,and the subcutaneous tunnel length should not be less than 5 cm.The basic features associated with higher CRT risk include age of 50-65 years,BMI being between 18.69 kg/m2 and 20.81 kg/m2 or between 23.68 kg/m2 and 23.94 kg/m2 and male.

Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.

Objective:To screen the risk factors for non-complete procedural success of transvenous lead extraction(TLE),and to establish a prediction model based on the results and evaluate its predictive efficacy.Methods:A total of 1 029 patients who underwent TLE in Peking University People's Hospital from January 2014 to December 2020 were enrolled and divided into training set(n=720)and validation set(n=309)using the random number method.There were no statistically significant differences among the variables in the training set and the validation set.The training set was divided into the complete procedural success(CPS)group(n=664)and the non-CPS group(n=56).Univariate analysis was employed to screen the relevant indicators of non-CPS,followed by binary logistic regression analysis to identify the independent risk factors of non-CPS.Subsequently,a predictive model and nomogram were constructed.The receiver operating characteristic(ROC)curve analysis was applied to evaluate the ability of the model to distinguish non-CPS from TLE patients in the training set and validation set.The Hosmer-Lemeshow goodness-of-fit test was used to assess the consistency between the predicted risk and the actual risk of the model.Results:Univariate analysis showed that the relevant variables with P<0.1 including the age at the first implantation of the lead,the number of leads extracted,the oldest dwell time of lead extracted,the presence of abandoned leads,non-manual traction for lead extracted,the number of extracted leads>3,bilateral lead implantation,and the indications for TLE.The binary logistic regression analysis revealed that the presence of abandoned leads(OR=2.252,95%CI:1.111-4.564,P=0.024),the oldest dwell time of the extracted leads(OR=1.009,95%CI:1.005-1.012,P<0.001),and the number of extracted leads>3(OR=3.177,95%CI:1.306-7.733,P=0.011)were independent risk factors for non-CPS of TLE.ROC curve analysis revealed that the area under the ROC curve(AUC)of the training set was 0.80(95%CI:0.75-0.85,P<0.001).The AUC of the validation set was 0.81(95%CI:0.72-0.90,P<0.001).The Hosmer-Lemeshow goodness-of-fit test indicated that the P values of both the training set(P=0.089)and the validation set(P=0.136)were greater than 0.05.Conclusions:The presence of abandoned leads,the oldest dwell time of lead extracted,and the number of extracted leads>3 are independent risk factors for non-CPS in patients undergoing TLE.The nomogram model based on the above factors has satisfactory predictive ability.

Objective To explore the association between walking exercise guided by 6 minute walking test(6MWT)and the incidences of 3-year major adverse cardiovascular event(MACE)in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 628 elderly ACS patients who undergoing PCI and obtaining success-ful coronary revascularization in our department from November 2018 to April 2019 were enrolled,and divided into 6MWT group(n=147)and control group(n=481)based on participa-ting in walking exercise guided by 6MWT or not.All of them were followed up for 3 years.The incidences of MACE[including coronary target vascular restenosis,acute myocardial infarction,heart failure,ischemic or hemorrhagic stroke]and all-cause death were observed.Univariate and multivariate Cox proportional analyses and Kaplan-Meier survival curve analysis were employed for data statistical analyses.Results At the end of follow-up,the incidences of target vascular restenosis(6.9％vs 2.0％,P=0.028),heart failure(3.7％vs 0％,P=0.036),stroke(3.7％vs 0％,P=0.036),and total MACE incidence(15.0％vs 4.1％,P=0.000)were statistically higher in the control group than the 6MWT group.Kaplan-Meier survival curve analysis showed that the cumulative incidence of MACE was significantly lower in the 6MWT group than the control group(Plog rank=0.001).Multivariate Cox regression analysis showed that not participating in walking exercise guided by 6MWT was an independent risk factor for occurrence of 3-year MACE(HR=3.102,95％CI:1.327-7.250,P=0.009).Conclusion Walking exercise guided by 6MWT reduces the incidence of 3-year MACE and improves the long-term prognosis of elderly ACS patients after PCI.