Objective To explore median effective dose(ED50)of 0.375％ropivacaine based on the cross sectional area(CSA)of supraclavicular brachial plexus block(SCBPB)with L-shaped baffle intervention.Methods Patients scheduled for upper limb surgery from September 2023 to May 2024 at Ningbo NO.6 Hospital were enrolled.Patients were randomly divided into two groups:L-shaped baffle compression group(group L)and non-compression group(group C).CSA of supraclavicular brachial plexus was measured by ultrasound,and 0.375％ropivacaine was administered based on the CSA.The ED50 was determined by using the Dixon up-and-down sequential method,with an initial dose of 0.4 ml/mm2 and an incremental difference of 0.04ml/mm2.If the block was effective within 30 minutes,the next patient received a lower dose;If ineffective,a higher dose was administered.The process continued until seven cross-over points(ineffective to effective)were observed.ED50 and its 95％CI were calculated by using the Probit method.Adverse reactions,such as phrenic nerve paralysis,nerve injury,dyspnea were recorded.Results The ED50 of 0.375％ropivacaine for SCBPB in group C was 0.254 ml/mm2(95％CI:0.228-0.278),while in group L,it was 0.239 ml/mm2(95％CI:0.215-0.262),with no statistically significant difference between two groups(P＞0.05).The incidence of phrenic nerve paralysis in group L was significantly lower than that in group C(14.29％vs.41.67％,P＜0.05).No significant nerve injuries,dyspnea,or local anesthetic toxicity were observed in either group.Conclusion The ED50 of 0.375％ropivacaine for SCBPB with L-shaped baffle compression,based on the CSA of the brachial plexus,was 0.239 ml/mm2(95％CI:0.215-0.262).L-shaped baffle compression significantly reduced the incidence of phrenic nerve paralysis without notable side effects.

Objective To explore median effective dose(ED50)of 0.375％ropivacaine based on the cross sectional area(CSA)of supraclavicular brachial plexus block(SCBPB)with L-shaped baffle intervention.Methods Patients scheduled for upper limb surgery from September 2023 to May 2024 at Ningbo NO.6 Hospital were enrolled.Patients were randomly divided into two groups:L-shaped baffle compression group(group L)and non-compression group(group C).CSA of supraclavicular brachial plexus was measured by ultrasound,and 0.375％ropivacaine was administered based on the CSA.The ED50 was determined by using the Dixon up-and-down sequential method,with an initial dose of 0.4 ml/mm2 and an incremental difference of 0.04ml/mm2.If the block was effective within 30 minutes,the next patient received a lower dose;If ineffective,a higher dose was administered.The process continued until seven cross-over points(ineffective to effective)were observed.ED50 and its 95％CI were calculated by using the Probit method.Adverse reactions,such as phrenic nerve paralysis,nerve injury,dyspnea were recorded.Results The ED50 of 0.375％ropivacaine for SCBPB in group C was 0.254 ml/mm2(95％CI:0.228-0.278),while in group L,it was 0.239 ml/mm2(95％CI:0.215-0.262),with no statistically significant difference between two groups(P＞0.05).The incidence of phrenic nerve paralysis in group L was significantly lower than that in group C(14.29％vs.41.67％,P＜0.05).No significant nerve injuries,dyspnea,or local anesthetic toxicity were observed in either group.Conclusion The ED50 of 0.375％ropivacaine for SCBPB with L-shaped baffle compression,based on the CSA of the brachial plexus,was 0.239 ml/mm2(95％CI:0.215-0.262).L-shaped baffle compression significantly reduced the incidence of phrenic nerve paralysis without notable side effects.

Objective To address the difficulty in collecting sufficient endoscopic images of colorectal submucosal tumors for traditional deep learning model training,a few-shot learning based model(FSL model)is proposed for classifying colorectal submucosal tumors and polyps on endoscopic images.Methods A total of 172 endoscopic images of colorectal submucosal tumors were collected from different centers,including 43 each of colorectal lipomas(CRLs),neuroendocrine tumors(NETs),serrated lesions and polyps(SLPs),and traditional adenomas.A support set and a query set were constructed using these endoscopic images.ResNet50 which was pre-trained on ImageNet and esophageal endoscopic images was used to extract image features.Subsequently,K-nearest neighbors algorithm was used for classification based on the calculated Euclidean distance.The classification performance of FSL model was evaluated through the comparison with the original model and endoscopists.Results FSL model had a 4-class classification accuracy of 0.831,Macro AUC of 0.925,Macro F1-score of 0.831;moreover,the proposed model achieved diagnostic accuracies of 0.925 and 0.906 for CRLs and NETs,with F1 score of 0.850 and 0.805.Additionally,the proposed model exhibited high classification consistency(Kappa=0.775)and interpretability.Conclusion The established FSL model performs well in distinguishing CRLs,NETs,SLPs and traditional adenomas on endoscopic images,indicating its potential utility in assisting the identification of colorectal submucosal tumors under endoscopy.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms