Portal vein thrombosis （PVT） involves the main portal vein and its tributaries， and acute PVT can cause intestinal ischemia and necrosis， while chronic PVT can cause cavernous transformation of the portal vein and portal hypertension-related complications （such as ascites and gastroesophageal variceal bleeding）. Liver cirrhosis is the main risk factor for PVT. the Classification of PVT provides a basis for clinical diagnosis. Ultrasound is the preferred method for screening， and contrast-enhanced computed tomography is the gold standard for diagnosis， while magnetic resonance imaging/magnetic resonance venography can help to identify acute or chronic thrombosis. Treatment emphasizes individualized strategies： anticoagulant therapy is the first-line therapy for acute PVT， and direct oral anticoagulants have shown great potential in clinical practice； thrombolytic therapy is appropriate for severe acute PVT， and it is needed to strictly control the risk of bleeding； transjugular intrahepatic portosystemic shunt is an important method for the diagnosis and treatment of PVT-related ascites and gastroesophageal variceal bleeding； surgical operation can be used for the treatment of patients with no response to pharmacotherapy or those with serious complications such as intestinal necrosis. Future diagnosis and treatment of PVT should be based on multidisciplinary collaboration， focusing on the optimization of individualized regimens， balancing efficacy and safety， and continuously leveraging technological advances to improve clinical practice.

Objective To investigate the relationship between preoperative serum high mobility group protein 1(HMGB1),macrophage inflammatory protein-1α(MIP-1α),osteopontin(OPN)and the prognosis of patients with basal ganglia intracerebral hemorrhage after neuroendoscopic surgery.Methods From March 2022 to March 2024,98 patients with cerebral hemorrhage in basal ganglia treated by neuroendoscopic removal were selected.Serum HMGB1,MIP-1 a and OPN were detected before operation,and they were followed up for 1 month after operation.Cox regression analysis was used to analyze the risk factors affecting the prognosis of patients with cerebral hemorrhage in basal ganglia.Kaplan-Meier survival curve and Log-rank test were used to analyze the survival rate of patients with cerebral hemorrhage in basal ganglia.Results Among the 98 patients with cerebral hemorrhage in basal ganglia,21 patients died within 1 month after neuroendoscopic removal,and the mortality rate was 21.43％.The proportion of patients with midline shift ≥ 10 mm(76.19％)and hematoma volume[(51.18±7.62)ml]in the death group were higher than those in the survival group[14.29％and(31.93±5.66)ml],and the hematoma clearance rate[(78.13±5.86)％]was lower than that in the survival group[(90.58±6.29)％](P＜0.05).The preoperative levels of HMGB1,MIP-1 α and OPN in the death group were(12.44±2.26)ng/ml,(417.25±134.12)pg/ml and(12.32±3.19)ng/ml,respectively.The survival groups were(7.52±2.37)ng/ml,(200.35±31.36)pg/ml and(6.29±2.37)ng/ml,respectively.There was a statistically significant difference between the two groups(P＜0.05).Cox regression analysis showed that preoperative high HMGB1(HR=1.629,95％CI:1.274-2.083),high MIP-1α(HR=2.875,95％CI:1.384-5.972),high OPN(HR=1.429,95％CI:1.073-1.093)were risk factors for death within 1 month after neuroendoscopic removal of basal ganglia intracerebral hemorrhage(P＜0.05).Survival curve analysis showed that the survival rate of high HMGB1,high MIP-1α and high OPN at 1 month after operation was significantly lower than that of low HMGB1,low MIP-1α and low OPN(Long-rank x2=7.539,6.028,8.220,P＜0.05).Conclusion The prognosis of patients with basal ganglia intracerebral hemorrhage with high HMGB1,high MIP-1α and high OPN before neuroendoscopic surgery was poor.Preoperative HMGB1,MIP-1α and OPN may be used as indicators for postoperative evaluation of neuroendoscopic removal in patients with basal ganglia intracerebral hemorrhage.

Intraoperative cell salvage (IOCS) has been widely applied as an important blood conservation measure in surgical operations. However, there is currently a lack of clinical practice guidelines for the implementation of IOCS in patients with malignant tumors. This report aims to provide clinicians with recommendations on the use of IOCS in patients with malignant tumors based on the review and assessment of the existed evidence. Data were derived from databases such as PubMed, Embase, the Cochrane Library and Wanfang. The guideline development team formulated recommendations based on the quality of evidence, balance of benefits and harms, patient preferences, and health economic assessments. This study constructed seven major clinical questions. The main conclusions of this guideline are as follows: 1) Compared with no perioperative allogeneic blood transfusion (NPABT), perioperative allogeneic blood transfusion (PABT) leads to a more unfavorable prognosis in cancer patients (Recommended); 2) Compared with the transfusion of allogeneic blood or no transfusion, IOCS does not lead to a more unfavorable prognosis in cancer patients (Recommended); 3) The implementation of IOCS in cancer patients is economically feasible (Recommended); 4) Leukocyte depletion filters (LDF) should be used when implementing IOCS in cancer patients (Strongly Recommended); 5) Irradiation treatment of autologous blood to be reinfused can be used when implementing IOCS in cancer patients (Recommended); 6) A careful assessment of the condition of cancer patients (meeting indications and excluding contraindications) should be conducted before implementing IOCS (Strongly Recommended); 7) Informed consent from cancer patients should be obtained when implementing IOCS, with a thorough pre-assessment of the patient's condition and the likelihood of blood loss, adherence to standardized internally audited management procedures, meeting corresponding conditions, and obtaining corresponding qualifications (Recommended). In brief, current evidence indicates that IOCS can be implemented for some malignant tumor patients who need allogeneic blood transfusion after physician full evaluation, and LDF or irradiation should be used during the implementation process.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

Objective To investigate the relationship between preoperative serum high mobility group protein 1(HMGB1),macrophage inflammatory protein-1α(MIP-1α),osteopontin(OPN)and the prognosis of patients with basal ganglia intracerebral hemorrhage after neuroendoscopic surgery.Methods From March 2022 to March 2024,98 patients with cerebral hemorrhage in basal ganglia treated by neuroendoscopic removal were selected.Serum HMGB1,MIP-1 a and OPN were detected before operation,and they were followed up for 1 month after operation.Cox regression analysis was used to analyze the risk factors affecting the prognosis of patients with cerebral hemorrhage in basal ganglia.Kaplan-Meier survival curve and Log-rank test were used to analyze the survival rate of patients with cerebral hemorrhage in basal ganglia.Results Among the 98 patients with cerebral hemorrhage in basal ganglia,21 patients died within 1 month after neuroendoscopic removal,and the mortality rate was 21.43％.The proportion of patients with midline shift ≥ 10 mm(76.19％)and hematoma volume[(51.18±7.62)ml]in the death group were higher than those in the survival group[14.29％and(31.93±5.66)ml],and the hematoma clearance rate[(78.13±5.86)％]was lower than that in the survival group[(90.58±6.29)％](P＜0.05).The preoperative levels of HMGB1,MIP-1 α and OPN in the death group were(12.44±2.26)ng/ml,(417.25±134.12)pg/ml and(12.32±3.19)ng/ml,respectively.The survival groups were(7.52±2.37)ng/ml,(200.35±31.36)pg/ml and(6.29±2.37)ng/ml,respectively.There was a statistically significant difference between the two groups(P＜0.05).Cox regression analysis showed that preoperative high HMGB1(HR=1.629,95％CI:1.274-2.083),high MIP-1α(HR=2.875,95％CI:1.384-5.972),high OPN(HR=1.429,95％CI:1.073-1.093)were risk factors for death within 1 month after neuroendoscopic removal of basal ganglia intracerebral hemorrhage(P＜0.05).Survival curve analysis showed that the survival rate of high HMGB1,high MIP-1α and high OPN at 1 month after operation was significantly lower than that of low HMGB1,low MIP-1α and low OPN(Long-rank x2=7.539,6.028,8.220,P＜0.05).Conclusion The prognosis of patients with basal ganglia intracerebral hemorrhage with high HMGB1,high MIP-1α and high OPN before neuroendoscopic surgery was poor.Preoperative HMGB1,MIP-1α and OPN may be used as indicators for postoperative evaluation of neuroendoscopic removal in patients with basal ganglia intracerebral hemorrhage.

Purpose::Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.Methods::This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q 1, Q 3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. Results::According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval ( CI): 2.08 -25.42, p = 0.002), 2.85 (95% CI: 1.11 -7.31, p = 0.030), 2.62 (95% CI: 1.12 -6.13, p = 0.027), 2.44 (95% CI: 1.25 -4.76, p = 0.009), and 1.5 (95% CI: 1.10 -2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ 2= 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ 2= 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Conclusion::Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Humans ; Child ; Dancing ; Spinal Cord Injuries ; Spinal Cord

Objective To compare the value of Child-Pugh score, Model for End-Stage Liver Disease (MELD) score, MELD combined with serum sodium concentration (MELD-Na) score, CLIF Consortium Acute Decompensation (CLIF-C AD) score, and Freiburg index of post-transjugular intrahepatic portosystemic shunt (TIPS) survival (FIPS) score in predicting the survival of patients undergoing TIPS. Methods A retrospective analysis was performed for the clinical data of 447 patients with liver cirrhosis who underwent TIPS in several hospitals in southwest China, among whom there were 306 patients in the survival group and 62 in the death group. The scores of the above five models were calculated, and a survival analysis was performed based on these models. The independent samples t -test was used for comparison of normally distributed continuous data between groups, and the non-parametric Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the Pearson chi-square test was used for comparison of categorical data between groups; a multivariate Cox regression analysis was used for correction analysis of known influencing factors with statistical significance which were not included in the scoring models; the Kaplan-Meier method was used to evaluate the discriminatory ability of each model in identifying risks in the surgical population, and the log-rank test was used for analysis. The area under the receiver operating characteristic curve (AUC), C-index at different time points, and calibration curve were used to evaluate the predictive ability of each scoring model. Results Compared with the death group, the survival group had significantly lower age ( Z =2.884, P < 0.05), higher albumin ( t =3.577, P < 0.05), and Na + ( Z =-3.756, P < 0.05) and significantly lower proportion of patients with alcoholic cirrhosis ( χ 2 =22.674, P < 0.05), aspartate aminotransferase ( Z =2.141, P < 0.05), prothrombin time ( Z =2.486, P < 0.05), international normalized ratio ( Z =2.429, P < 0.05), total bilirubin ( Z =3.754, P < 0.05), severity of ascites ( χ 2 =14.186, P < 0.05), and scores of the five models (all P < 0.05). Survival analysis showed that all scoring models effectively stratified the prognostic risk of the patients undergoing TIPS. Comparison of the C-index of each scoring model at different time points showed that Child-Pugh score had the strongest ability in predicting postoperative survival, followed by MELD-Na score, MELD score, and CLIF-C AD score, and FIPS score had a relatively poor predictive ability; in addition, the prediction efficiency of each score gradually decreased over time. Child-Pugh score had the largest AUC of 0.832 in predicting 1-year survival rate after surgery, and MELD-Na score had the largest AUC of 0.726 in predicting 3-year survival rate after surgery, but FIPS score had a poor ability in predicting 1- and 3-year survival rates. Conclusion All five scoring models can predict the survival of patients with liver cirrhosis after TIPS and can provide effective stratification of prognostic risk for such patients. Child-Pugh score has a better ability in predicting short-term survival, while MELD-Na score has a better ability in predicting long-term survival, but FIPS score has a relatively poor predictive ability in predicting both short-term and long-term survival.