ObjectiveTo develop a full-process data platform of renal rehabilitation, diagnosis and quality control information. MethodsA hierarchical architectural design was proposed, adhering to clinical pathway models and standardized data protocols. The platform comprehensively covered assessment, intervention, follow-up and quality control for maintenance hemodialysis (MHD) patients. By integrating multidisciplinary resources and standardizing rehabilitation workflows, it delivered standardized and intelligent rehabilitation services. ResultsThe platform achieved standardized and intelligent management of rehabilitation services, effectively improved the physiological function, psychological state and quality of life convenience for MHD patients, while significantly reduced the economic and care burden on patients' families and society. ConclusionThe rehabilitation service model based on a full-process data platform may provide scientific and systematic support for MHD patients.

OBJECTIVE: To evaluate the effectiveness of Poster Fusion Cage combined with xenogeneic bone graft augmentation for bone defect management in distal radius fractures. METHODS: A retrospective analysis was conducted on 20 patients with bone defects complicating distal radius fractures who met the selection criteria and were treated between June 2022 and June 2024. The cohort comprised 2 males and 18 females, aged 54-87 years (mean, 63.3 years). Etiologies included falls in 17 cases, traffic accidents in 2 cases, and crush injury in 1 case. According to AO classification, there were 5 cases of type A, 8 cases of type B, and 7 cases of type C. The interval from injury to operation ranged from 2 to 10 days (mean, 5.8 days). All patients underwent volar plate fixation augmented with Poster Fusion Cage and demineralized xenogeneic bone matrix grafting. The operation time, intraoperative blood loss, fracture healing time, and postoperative complications were recorded. Radiographic parameters, including radial height, volar tilt, and ulnar deviation, were measured on standardized X-ray films obtained immediately postoperatively and at last follow-up, and whether secondary reduction loss occurred was judged. At last follow-up, wrist range of motion (extension, flexion, radial deviation, ulnar deviation, pronation, and supination) and grip strength (expressed as a percentage of the contralateral side) were measured. Wrist function was assessed using the Disabilities of the Arm, Shoulder, and Hand (DASH) score and Patient-Rated Wrist Evaluation (PRWE) score. RESULTS: The operation time was 70-200 minutes (mean, 116.4 minutes), and the intraoperative blood loss was 10-80 mL (mean, 36.5 mL). All surgical incisions healed by first intention, with no neurovascular complications documented. All patients were followed up 9-12 months (mean, 11.6 months). All fractures healed normally, with a healing time of 8-14 weeks (mean, 9.95 weeks). No significant difference was observed in radial height, volar tilt, or ulnar deviation between immediate postoperatively and last follow-up ( P>0.05). All fractures achieved satisfactory reduction, with no secondary loss of reduction or implant failure occurring during follow-up. At last follow-up, the range of motion of the affected wrist joint was 60°-65° (mean, 62.5°) in extension, 67°-75° (mean, 71.1°) in flexion, 18°-23° (mean, 20.4°) in radial deviation, 28°-33° (mean, 30.1°) in ulnar deviation, 69°-80° (mean, 74.7°) in pronation, and 69°-82° (mean, 75.6°) in supination. Grip strength recovered to 75%-85% (mean, 80%) of the contralateral side. Functional scores showed a DASH score of 5-15 (mean, 9.4) and PRWE score of 8.0-12.5 (mean, 10.2). CONCLUSION The combination of Poster Fusion Cage and xenogeneic bone graft augmentation provides a safe and effective treatment for bone defects in distal radius fractures.
Retrospective Studies ; Humans ; Male ; Female ; Middle Aged ; Aged ; Aged, 80 and over ; Treatment Outcome ; Wrist Fractures/surgery* ; Heterografts ; Transplantation, Heterologous/methods* ; Bone Transplantation/methods* ; Operative Time ; Blood Loss, Surgical ; Radius/surgery* ; Fracture Healing ; Time Factors ; Postoperative Complications/etiology* ; Range of Motion, Articular ; Follow-Up Studies ; Internal Fixators ; Fracture Fixation, Internal/methods* ; Combined Modality Therapy

Objective:To develop radiomics score (RS) based on 18F-FDG PET/CT, and construct the machine learning model combining clinical and other relevant factors for personalized prediction of 2-year event-free survival (2-EFS) in patients with diffuse large B-cell lymphoma (DLBCL), and to perform interpretability analysis of the model. Methods:A total of 91 patients (49 males, 42 females; age (57.8±12.8) years) with pathologically confirmed DLBCL from December 2017 to December 2020 at the First Hospital of Shanxi Medical University were retrospectively analyzed. According to the ratio of 7∶3, patients were randomly divided into training set ( n=63) and test set ( n=28), and divided into non-progression group and progression group according to the follow-up results. The whole-body PET semi-quantitative parameters were calculated from the PET/CT images before treatment, and 328 radiomics features were extracted from the largest target lesions of patients. The least absolute shrinkage and selection operator (LASSO) was used to develop the RS. Clinical and PET characteristic difference analysis was performed through χ2 test and Mann-Whitney U test. Extreme gradient boosting (XGBoost) models were constructed based on clinical, PET radiomics features and RS, and the prediction efficiency of each model was evaluated by ROC AUC. The model interpretability was analyzed by using shapely additive explanation (SHAP). Results:Of all patients, 32 had disease progression and 59 did not. There were no significant differences in baseline characteristics between the training set and the test set ( χ2 values: 0.06-1.84, U values: 665.00-763.00, all P>0.05). The comparison between the progression group and non-progression group in the training set showed statistical differences in the international prognostic index (IPI) score ( χ2=4.87, P=0.027), myelocytomatosis viral oncogene (MYC) protein expression ( χ2=4.29, P=0.038), and metabolic tumor volume (MTV; U=307.00, P=0.038). Seven radiomics features were screened by LASSO. Among XGBoost models with different feature combinations, IPI score, MYC protein expression, MTV combined with RS had the highest predictive efficiency (training set: AUC=0.73; test set: AUC=0.70). Through SHAP analysis, RS was the most predictive feature in the optimal model. Conclusion:The machine learning integrated model of IPI score, MYC protein expression and MTV combined with RS can effectively predict the prognosis of DLBCL patients, and baseline 18F-FDG PET/CT radiomics can be used as a potential means to evaluate the prognosis of DLBCL patients.

Objective: To investigate the characteristics of hospitalized injury cases in Huangpu District, Guangzhou City in 2022, so as to provide evidence for optimizing injury prevention interventions. Methods: Data on hospitalized injury cases admitted between January to December 2022 were collected through the hospitalization registry system from 17 healthcare institutions in Huangpu District. The population distribution characteristics, causes of injury, injury sites, duration of hospital stay, and hospitalization costs were descriptively analyzed. Results: A total of 6 729 hospitalized injury cases were reported in Huangpu District in 2022, including 4 277 males and 2 452 females, with a male-to-female ratio of 1.74∶1. The average age was (49.57±19.82) years, with 2 064 cases (30.67%) aged 45 to <60 years and 1 921 cases (28.55%) aged ≥60 years. The median length of hospitalization was 9.00 (interquartile range, 11.00) days, with median hospitalization costs of 15 968.93 (interquartile range, 25 786.69) yuan. In the months of June to August, there were more cases of injury hospitalization,with 1 904 cases accounting for 28.30%. The top three causes of injury were falls (2 895 cases, 43.02%), transportation accidents (1 247 cases, 18.53%) and exposure to inanimate mechanical forces (1 104 cases, 16.41%). The top three injured sites were lower limb injuries (1 850 cases, 27.49%), upper limb injuries (1 596 cases, 23.72%) and other sites (1 178 cases, 17.51%). The three leading causes of injury with longest hospitalization duration were burns and scalds, transport accidents and falls, with the median duration being 12.00 (interquartile range, 8.00) days, 10.00 (interquartile range, 13.00) days and 10.00 (interquartile range, 11.00) days, respectively. The top three injury sites associated with the longest hospitalization duration were others, lower limb injuries, and head and neck injuries, with the median duration being 11.00 (interquartile range, 13.00) days, 11.00 (interquartile range, 11.00) days, and 10.00 (interquartile range, 12.00) days, respectively. The causes of injury associated with higher hospitalization costs were falls and transportation accidents, with the median hospitalization cost being 23 550.13 (interquartile range, 30 087.76) yuan for falls and 20 301.94 (interquartile range, 30 589.86) yuan for transportation accidents. The injury sites associated with higher hospitalization costs were lower limb injuries and upper limb injuries, with the median hospitalization cost being 24 257.32 (interquartile range, 34 145.54) yuan for lower limb injuries and 16 506.33 (interquartile range, 20 052.27) yuan for upper limb injuries. Conclusions In Huangpu District, hospitalized injury mainly occurred among males and individuals aged ≥45 years, with the higher incidence observed between June and August. Fall was the primary cause of injury, while lower limb injuries was the main injury sites. The injury resulted in substantially higher hospitalization costs.

Objective To evaluate the value of biparametric MRI(bpMRI)radiomics for predicting postoperation Gleason score(GS)upgrade of prostate cancer(PCa).Methods Totally 344 PCa patients who underwent radical prostatectomy(RP)were retrospectively enrolled and divided into training set(n=241)and test set(n=103)at a ratio of 7∶3.T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)map radiomics signatures were constructed based on preoperative bpMRI,respectively,then logistic regression(LR)algorithm was used to establish bpMRI radiomics model.Univariate and multivariate logistic regression analyses were performed to screen independent risk factors for postoperation GS upgrade of PCa,and a clinical model was constructed.Then a clinical-radiomics combined model was established based on clinical model and bpMRI radiomics model.Receiver operating characteristic curves were drawn,the area under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting postoperation GS upgrade of PCa.Results Elevated preoperative prostate imaging reporting and data system(PI-RADS)score and reduced biopsy Gleason grade group(GG)were both independent risk factors of postoperation GS upgrade of PCa(both P＜0.05).The AUC of bpMRI radiomics model and clinical-radiomics combined model for predicting postoperation GS upgrade of PCa were higher than that of single-sequence radiomics signatures and clinical model(all P＜0.05),while no significant difference was found between the former two(P＞0.05).The clinical-radiomics combined model demonstrated good efficacy for predicting postoperation GS upgrade of PCa with different biopsy GG before operation,with AUC ranging from 0.835 to 0.949 in training set and 0.803 to 0.948 in test set.Conclusion bpMRI radiomics model could effectively predict postoperation GS upgrade of PCa.

Objective:To investigate the association between frailty and prognosis of elderly patients in the emergency department, and to validate frailty screening tools suitable for the emergency department.Methods:This was a prospective cohort study. Clinical data of elderly patients over 60 years old treated in the emergency department of Beijing Bo'Ai Hospital from January to December 2021 were collected. The Frailty Screening Questionnaire (FSQ), FRAIL Scale (FRAIL) and Clinical Frailty Scale (CFS) were used to score patients, and patients were divided into frail or non-frail group according to the criteria of the above three scales. Twelve-month all-cause mortality was the primary endpoint, dependence and re-admission to the emergency department within 12 months were secondary outcomes. Receiver operating characteristic curves were used to evaluate the ability of the FSQ, FRAIL and CFS scores to predict the primary and secondary endpoints, and the areas under the curve (AUC) were calculated and compared. Survival analysis was performed using Cox hazard proportional regression model, and relative risk was expressed as hazard ratio ( HR) and 95% CI. Results:A total of 406 patients were included in the study. The AUCs (95% CI) of FSQ, FRAIL and CFS scores for predicting 12-month all-cause mortality were 0.879 (0.844-0.909), 0.838 (0.798-0.872), 0.906 (0.873-0.933), respectively (all P<0.001). The AUCs of 3 scores for predicting secondary endpoints ranged from 0.820 to 0.889 (all P<0.001). Pairwise comparisons of the AUCs showed that the CFS was superior to one or both of the other frailty screening scales in predicting 12-month all-cause mortality and dependence except for re-admission to emergency room within 12 months after discharge (all P<0.05). Cox regression analysis revealed that, after adjusting for sex, age, body mass index and comorbidities, frailty as defined by the FSQ, FRAIL, and CFS scales was independently associated with 12-month all-cause mortality, with the HRadj of 3.267 (95% CI: 2.406-4.435), 2.465 (95% CI: 1.819-3.341), 3.523 (95% CI: 2.648-4.687), respectively (all P<0.001). Conclusions:FSQ, FRAIL and CFS scores can predict adverse outcomes, the CFS is a practical frailty screening tool in the emergency department, and frailty screening can improve the risk stratification of older patients.

Objective To evaluate the value of biparametric MRI(bpMRI)radiomics for predicting postoperation Gleason score(GS)upgrade of prostate cancer(PCa).Methods Totally 344 PCa patients who underwent radical prostatectomy(RP)were retrospectively enrolled and divided into training set(n=241)and test set(n=103)at a ratio of 7∶3.T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)map radiomics signatures were constructed based on preoperative bpMRI,respectively,then logistic regression(LR)algorithm was used to establish bpMRI radiomics model.Univariate and multivariate logistic regression analyses were performed to screen independent risk factors for postoperation GS upgrade of PCa,and a clinical model was constructed.Then a clinical-radiomics combined model was established based on clinical model and bpMRI radiomics model.Receiver operating characteristic curves were drawn,the area under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting postoperation GS upgrade of PCa.Results Elevated preoperative prostate imaging reporting and data system(PI-RADS)score and reduced biopsy Gleason grade group(GG)were both independent risk factors of postoperation GS upgrade of PCa(both P＜0.05).The AUC of bpMRI radiomics model and clinical-radiomics combined model for predicting postoperation GS upgrade of PCa were higher than that of single-sequence radiomics signatures and clinical model(all P＜0.05),while no significant difference was found between the former two(P＞0.05).The clinical-radiomics combined model demonstrated good efficacy for predicting postoperation GS upgrade of PCa with different biopsy GG before operation,with AUC ranging from 0.835 to 0.949 in training set and 0.803 to 0.948 in test set.Conclusion bpMRI radiomics model could effectively predict postoperation GS upgrade of PCa.

Objective:To develop radiomics score (RS) based on 18F-FDG PET/CT, and construct the machine learning model combining clinical and other relevant factors for personalized prediction of 2-year event-free survival (2-EFS) in patients with diffuse large B-cell lymphoma (DLBCL), and to perform interpretability analysis of the model. Methods:A total of 91 patients (49 males, 42 females; age (57.8±12.8) years) with pathologically confirmed DLBCL from December 2017 to December 2020 at the First Hospital of Shanxi Medical University were retrospectively analyzed. According to the ratio of 7∶3, patients were randomly divided into training set ( n=63) and test set ( n=28), and divided into non-progression group and progression group according to the follow-up results. The whole-body PET semi-quantitative parameters were calculated from the PET/CT images before treatment, and 328 radiomics features were extracted from the largest target lesions of patients. The least absolute shrinkage and selection operator (LASSO) was used to develop the RS. Clinical and PET characteristic difference analysis was performed through χ2 test and Mann-Whitney U test. Extreme gradient boosting (XGBoost) models were constructed based on clinical, PET radiomics features and RS, and the prediction efficiency of each model was evaluated by ROC AUC. The model interpretability was analyzed by using shapely additive explanation (SHAP). Results:Of all patients, 32 had disease progression and 59 did not. There were no significant differences in baseline characteristics between the training set and the test set ( χ2 values: 0.06-1.84, U values: 665.00-763.00, all P>0.05). The comparison between the progression group and non-progression group in the training set showed statistical differences in the international prognostic index (IPI) score ( χ2=4.87, P=0.027), myelocytomatosis viral oncogene (MYC) protein expression ( χ2=4.29, P=0.038), and metabolic tumor volume (MTV; U=307.00, P=0.038). Seven radiomics features were screened by LASSO. Among XGBoost models with different feature combinations, IPI score, MYC protein expression, MTV combined with RS had the highest predictive efficiency (training set: AUC=0.73; test set: AUC=0.70). Through SHAP analysis, RS was the most predictive feature in the optimal model. Conclusion:The machine learning integrated model of IPI score, MYC protein expression and MTV combined with RS can effectively predict the prognosis of DLBCL patients, and baseline 18F-FDG PET/CT radiomics can be used as a potential means to evaluate the prognosis of DLBCL patients.

Objective To investigate the value of radiomics nomogram model based on enhanced CT for predicting postoperative pancreatic fistula(POPF)in patients undergoing pancreaticoduodenectomy(PD).Methods The clinical and abdominal imaging data of 82 patients who underwent PD were analyzed retrospectively.They were divided into training set and validation set at a ratio of 3∶2.In the venous phase of CT,the maximum slice of the pancreatic duct was selected to delineate the region of interest(ROI),and the features were extracted and screened to construct the radiomics model.The independent risk factors associated with POPF were screened by univariate and multivariable logistic regression analyses to construct the clinical model.A nomogram model was created by combining Radiomics score(Radscore)and clinical risk factors.The diagnostic performance of the model was verified by the vali-dation set.Results The nomogram model demonstrated significant predictive power,with an area under the curve(AUC)of 0.862 and 0.806 in the training set and validation set,respectively.Conclusion The nomogram model exhibits excellent predictive performance and outperforms the clinical model and radiomics model in predicting POPF,which can provide important guidance for clinical deci-sion-making.

Objective:To analyze the allocation of general practitioners per 10 000 population in China and its influencing factors, so as to provide reference for optimizing the allocation of general practitioners resources in China.Methods:The number of general practitioners per 10 000 population and other relevant data of 31 provinces (except Hong Kong, Macao and Taiwan) in China from 2012 to 2021 were collected from China Statistical Yearbook, China Health Statistics Yearbook, China Health and Family Planning Statistical Yearbook. The geographical area data of 31 provinces (except Hong Kong, Macao and Taiwan) from 2012 to 2021 were retrieved from the website of the Central People ′s Government. The influencing factors of the number of general practitioners per 10 000 population were preset by literature analysis and unstructured interview. Taking the number of general practitioners per 10 000 population as the dependent variable, the correlation between it and the preset influencing factors was analyzed by panel data regression. Results:From 2012 to 2021, the average annual growth rate of the number of general practitioners per 10 000 population in each province of China was greater than 0. The factors affecting the number of general practitioners per 10 000 population included the number of people per square kilometer ( r=3.818, P<0.01), the number of beds in medical and health institutions per capita ( r=2.135, P<0.01), the proportion of the elderly population aged 65 and above ( r=0.180, P<0.01), and the proportion of total expenditure in medical and health institutions to gross domestic product ( r=0.080, P<0.01). Conclusions:The development trend of general practitioners resources allocation in China is good, but the government needs to integrate the population agglomeration and aging trend, the allocation of medical and health resources, the investment support for the development of general practitioners and other influencing factors, optimize the allocation of general practitioners resources according to local conditions, and strengthen policy support for areas and links where the allocation of general practitioners resources is relatively weak.