BACKGROUND:The use of 3D-printed patient-specific instruments in opening wedge high tibial osteotomy has advantages such as shorter operative time,fewer fluoroscopic exposures,and higher correction accuracy.However,previous studies have reported issues such as significant damage to surrounding soft tissues and improper fixation of the plates. OBJECTIVE:To investigate the clinical efficacy of using 3D-printed patient-specific instruments combined with customized locking plate in opening wedge high tibial osteotomy for the treatment of knee osteoarthritis. METHODS:A total of 20 patients diagnosed with knee osteoarthritis were divided into the 3D group(n=10)and the conventional group(n=10)according to surgical methods.The 3D group underwent opening wedge high tibial osteotomy using 3D-printed patient-specific instruments combined with customized locking plate,while the conventional group underwent opening wedge high tibial osteotomy using conventional methods.The operative time,fluoroscopic exposures,incision length,pre-and postoperative hip-knee-ankle angle,medial proximal tibial angle,posterior tibial slope,the difference between the planned and actual correction angle,preoperative and 1,3,6 months postoperative knee range of motion and Lysholm score,and incidence of complications were analyzed and compared between the two groups. RESULTS AND CONCLUSION:(1)The operative time and fluoroscopic exposures were significantly shorter in the 3D group compared to the conventional group,with a statistically significant difference(P<0.001).(2)Both groups showed a significant improvement in postoperative hip-knee-ankle angle and medial proximal tibial angle compared to preoperative values,with a statistically significant difference(P<0.001),while there was no significant change in posterior tibial slope.In the 3D group,the postoperative hip-knee-ankle angle,medial proximal tibial angle,and posterior tibial slope differed from their respective preoperative planned values by(-0.22±0.72)°,(-0.20±0.73)°,and(0.23±0.37)°,but the differences were not statistically significant.The difference between the planned and actual correction angle of 3D group was significantly smaller than that of conventional group(P<0.05).(3)Both groups showed a gradual increase in knee range of motion and Lysholm scores after surgery(P<0.001).Compared to the conventional group,the 3D group had superior knee range of motion at 1 and 3 months postoperatively,as well as a higher Lysholm score at 1 month postoperatively,with statistically significant differences(P<0.05).There were no statistically significant differences in Lysholm score at 3 months and knee range of motion and Lysholm score at 6 months between the two groups(P>0.05).(4)Complications occurred in neither groups.(5)The above results indicate that both 3D-printed patient-specific instruments combined with customized locking plate and conventional methods have good clinical efficacy.However,the former has a shorter operative time,fewer fluoroscopic exposures,and faster postoperative recovery of knee joint function.Additionally,3D-printed patient-specific instruments can achieve preoperative planning accurately.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Objective To propose a lead attention network-based ST-T change recognition algorithm to detect ECG ST-T changes accurately.Methods Firstly,heartbeat signals were extracted through R-wave localization,and a 12-lead heartbeat matrix was generated by correlation-based screening and merging to realize data augmentation.Secondly,a lead attention module was constructed by combining depthwise convolution(DWConv)with the channel attention squeeze-and-excitation block(SE-block)structure to perceive the differences in ST-T status among electrocardiogram leads.Thirdly,the mapping output by two independent attention modules was fused and splicing with the original signal residual was carried out,so that attention information extraction and original information transfer were enhanced effectively.Finally,SE-ResNet was used as the backbone network to extract signal features to complete the classification and identification of ST-T changes.To validate the recognition performance of the proposed algorithm for ST-T changes in ECG,the 12-lead ECG data of 97 472 patients containing different ECG rhythms were collected for ablation and comparison experiments at the First Affiliated Hospital of Army Medical University.Results The proposed algorithm achieved an AUC of 0.965 with a sensitivity of 90.51%,specificity of 90.23%,positive predictive value of 89.24%and overall accuracy of 90.36%on an independent test set.Comparative analysis demonstrated superior performance to four benchmark architectures,including VGG16,ResNet18,MobileNetV3-Small and ShuffleNet,in terms of both classification accuracy and computational efficiency.Conclusion The algorithm designed can accurately detect ST-T changes and can be used for wearable ECG automatic analysis to assist in the early warning of cardiovascular diseases in both acute and chronic patients and highland residents.[Chinese Medical Equipment Journal,2025,46(7):1-11]

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective To explore the risk factors for myocardial injury in esophagogastric variceal bleeding(EGVB)patients with liver cirrhosis during hospitalization.Methods A case-control trial was conducted on 235 EGVB patients admitted to our hospital between May 2021 and July 2022.Their basic information,laboratory results and relevant data during hospitalization were collected.According to their myocardial enzyme profiles during hospitalization,they were divided into myocardial injury group(n=46)and non-myocardial injury group(n=189).Univariate regression analysis and clinical correlation analysis were used to preliminarily screen the risk factors for myocardial injury secondary to EGVB caused by liver cirrhosis.Then,multivariate logistic regression analysis was used to further screen the risk factors.A nomogram was constructed based on the selected risk factors and the occurrence of myocardial injury.Receiver operating characteristic(ROC)curve was plotted to analyze the independent predictive value of these factors alone or combined together.Calibration curve analysis and internal verification were utilized to evaluate the predictive performance of the nomogram model.Subgroup verification was performed in the myocardial infarction group.Results Univariate analysis revealed that statistical differences were observed in age,sex,hypertension,renal disease,underlying diseases,vomiting,leukocytosis,increased alanine aminotransferase(ALT)or aspartate aminotransferase(AST),albumin,red blood cell hematocrit(HCT),international normalized ratio(INR),endoscopy within 6 h after admission,and Child-Pugh(CP)class between the myocardial injury group and the non-myocardial injury group(P＜0.01).Multivariate logistic regression analysis showed that age(P=0.014,OR=1.153,95％CI:1.030～1.291),underlying diseases(P=0.005,OR=1.122,95％CI:1.032～2.437),and albumin(P=0.012,OR=0.449,95％CI:0.241～0.837)were independent risk factors for inhospital myocardial injury in EGVB patients with liver cirrhosis.The AUC value of the above indicators combined together for predicting myocardial injury was 0.902.Hosmer-Lemeshow test and calibration curve analysis indicated that the nomogram had good prediction consistency(Chi-square=12.88,P=0.615).Internal verification correctly distinguished 86.4％of verification objects.Subgroup analysis of myocardial injury patients showed that albumin was also an independent risk factor for in-hospital myocardial injury in this population(AUC=0.80).Conclusion Age,underlying diseases,and albumin level are independent risk factors for in-hospital myocardial injury in EGVB patients with liver cirrhosis.Albumin level can be used as an independent risk factor for predicting myocardial infarction.Combination of the above 3 indicators has a high diagnostic value in early identification and prevention of myocardial injury in this patient population.

Objective To compare the effects of different dose prediction models of sufentanil in elderly hip arthroplasty with ultrasound-guided bow tie fascia iliac block.Methods A total of 90 elderly patients who underwent ultrasound-guided bow tie fascia iliac during hip arthroplasty in our hospital from June 2018 to June 2022 were selected.The dose of sufentanil was determined by a linear regression(ALR),multiple linear regression(MLR)or deep belief networks(DBN).Patients were randomly divided into the ALR group,the MLR group and the DBN group,with 30 cases in each group.The analgesic effect,hemodynamic index,inflammatory factor level and surgical index were compared among all groups.Kaplan-Meier method was used to analyze the incidence of hip infection 2 weeks after surgery in each group.Results There was no significant difference in the gender,age,body weight,American Society of Anesthesiologists(ASA)classification or complications among all groups(P>0.05).The Harris score 24 hours after surgery and duration of sensory block in the DBN group were higher/longer than those in the ALR group,numerical rating scale(NRS)score 24 hours after surgery,mean arterial pressure(MAP)and heart rate(HR)at the beginning of surgery and 30 minutes after surgery,levels of interleukin-17(IL-17)and tumor necrosis factor-α(TNF-α)24 hours after surgery,onset time of sensory block,and number of patient controlled analgesia(PCA)compression were lower/earlier/less than those in the ALR group,the modified observer's assessment of alertness/sedation scale(OAA/S)score 24 hours after surgery was significantly lower than those in the ALR group and the MLR group,with statistically significant differences(P<0.05).Harris score 24 hours after surgery and duration of sensory block in the MLR group were higher/longer than those in the ALR group,modified OAA/S score 12 hours after surgery,MAP 30 minutes after surgery,levels of IL-17 and TNF-α 24 hours after surgery,onset time of sensory block,and the number of PCA compressions were lower/longer/less than those in the ALR group,with statistically significant differences(P<0.05).Kaplan-Meier analysis showed that there were significant differences in the incidences of hip infection 2 weeks after surgery among all groups(P<0.05),and there were significant differences in the incidences of hip infection 2 weeks after surgery between the ALR group and the DBN group(P<0.05).Conclusion The effectiveness of calculating the dose of sufentanil using the MLR and DBN in elderly hip arthroplasty with bow tie fascia iliac block is better than that of ALR,and the advantage of DBN is more obvious.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.