Objective To investigate the factors influencing international normalized ratio (INR)>3.0 in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Methods A retrospective analysis was performed on the clinical data of patients who underwent mechanical heart valve replacement surgery and received warfarin anticoagulation therapy at West China Hospital of Sichuan University from January 1, 2011 to June 30, 2022. Based on the discharge INR values, patients were divided into two groups: an INR≤3.0 group and an INR>3.0 group. The factors associated with INR>3.0 at the time of discharge were analyzed. Results A total of 8901 patients were enrolled, including 3409 males and 5492 females, with a median age of 49.3 (43.5, 55.6) years. The gender, body mass index (BMI), New York Heart Association (NYHA) cardiac function grading, INR, glutamic oxaloacetic transaminase, and preoperative prothrombin time (PT) were statistically different between the two groups (P<0.05). Multivariate logistic regression analysis revealed that lower BMI, preoperative PT>15 s, and mitral valve replacement were independent risk factors for INR>3.0 at discharge (P<0.05). Conclusion BMI, preoperative PT, and surgical site are factors influencing INR>3.0 at discharge in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Special attention should be given to patients with lower BMI, longer preoperative PT, and mitral valve replacement to avoid excessive anticoagulation therapy.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: To assess the risk of aristolochic acid (AA)-associated cancer in patients with AA nephropathy (AAN). METHODS: A retrospective study was conducted on patients diagnosed with AAN at Peking University First Hospital from January 1997 to December 2014. Long-term surveillance and follow-up data were analyzed to investigate the influence of different factors on the prevalence of cancer. The primary endpoint was the incidence of liver cancer, and the secondary endpoint was the incidence of urinary cancer during 1 year after taking AA-containing medication to 2014. RESULTS: A total of 337 patients diagnosed with AAN were included in this study. From the initiation of taking AA to the termination of follow-up, 39 patients were diagnosed with cancer. No cases of liver cancer were observed throughout the entire follow-up period, with urinary cancer being the predominant type (34/39, 87.17%). Logistic regression analysis showed that age, follow-up period, and diabetes were potential risk factors, however, the dosage of the drug was not significantly associated with urinary cancer. CONCLUSIONS No cases of liver cancer were observed at the end of follow-up. However, a high prevalence of urinary cancer was observed in AAN patients. Establishing a direct causality between AA and HCC is challenging.
Humans ; Retrospective Studies ; Incidence ; Carcinoma, Hepatocellular ; Liver Neoplasms/epidemiology* ; Kidney Diseases/chemically induced* ; Aristolochic Acids/adverse effects*

Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.

Objective To investigate the efficacies of proximal femoral nail anti-rotation(PFNA)internal fixation in traction bed supine position and non-traction bed lateral position in the treatment of elderly unstable femoral intertrochanteric fractures.Methods The clinical data of patients with unstable femoral intertrochanteric fractures treated with PFNA internal fixation in our hospital were retrospec-tively analyzed,41 patients received treatment in traction bed supine position were included in the supine position group,and 55 patients treated received treatment in non-traction bed lateral position were included in the lateral position group.The perioperative related indicators,surgical reduction,hip Harris score,and incidence of complications in the two groups were analyzed.Results The operation time and incision length of patients in the lateral position group were shorter than those in the supine position group,and the intraoperative blood loss and fluoroscopy times were less than those in the supine position group,with statistically significant differences(P<0.05).There was no significant difference in the anesthesia mode,blood transfusion or hospital stay of patients between the two groups(P>0.05).There was no significant difference in the incidence of postoperative complications of patients between the two groups(P>0.05).There was no significant difference in neck-shaft angle,tip-apex distance or hip Harris score of patients between the two groups(P>0.05).Conclusion PFNA internal fixation in traction bed supine position and non-traction bed lateral position have the same effect in the treatment of elderly unstable femoral intertrochanteric fractures,while the non-traction bed lateral position for treatment has more advantages in shortening operation time,decreasing intraoperative blood loss,and reducing radiation exposure.

AIM To evaluate the quality of Beidougen Formula Granules.METHODS Fifteen batches of standard decoctions and three batches of formula granules were prepared,after which paste rate and contents,transfer rates of magnoflorine,daurisoline,dauricine were determined.HPLC specific chromatograms were established,and cluster analysis was adopted in chemical pattern recognition.RESULTS For three batches of formula granules,the paste rates were 15.1%-16.6%,the contents of magnoflorine,daurisoline,dauricine were 18.93-19.39,9.42-9.60,6.79-6.85 mg/g with the transfer rates of 34.42%-35.25%,43.81%-44.65%,27.27%-27.51%from decoction pieces to formula granules,respectively,and there were seven characteristic peaks in the specific chromatograms with the similarities of more than 0.95,which demonstrated good consistence with those of standard decoctions and accorded with related limit requirements.Fifteen batches of standard decoctions were clustered into two types,and the medicinal materials produced from Jilin,Hebei,Shangdong could be used for the preparation of formula granules.CONCLUSION This reasonable and reliable method can provide references for the quality control and clinical application of Beidougen Formula Granules.