Objective:To compare the efficacy of catheter-directed thrombolysis versus catheter-directed thrombectomy for high-risk pulmonary embolism.Methods:The clinical data of 105 patients with high-risk pulmonary embolism from April 2020 to January 2023 in Hebei China Petroleum Central Hospital were retrospectively analyzed. Among them, 52 patients were treated with catheter-directed thrombolysis (thrombolysis group), and 53 patients were treated with catheter-directed thrombectomy (thrombectomy group). The efficacy, symptom relief time, oxygen saturation recovery time, mortality rate, Qanadli embolic index, pulmonary artery pressure and complications were compared between two groups.Results:There were no statistical differences in total effective rate, symptom relief time, oxygen saturation recovery time, mortality rate and total incidence of complications between two groups ( P>0.05). Compared with before treatment, the Qanadli embolic index and pulmonary artery pressure after treatment in thrombolysis group and thrombectomy group were significantly lower, thrombolysis group: 22.08 ± 8.57 vs. 45.18 ± 13.27 and (24.18 ± 5.19) mmHg (1 mmHg = 0.133 kPa) vs. (34.15 ± 6.22) mmHg, thrombectomy group: 23.11 ± 8.62 vs. 44.82 ± 13.14 and (23.66 ± 5.02) mmHg vs. (34.89 ± 6.27) mmHg, and there were statistical differences ( P<0.01); but there was no statistical difference the Qanadli embolic index and pulmonary artery pressure before and after treatment between two groups ( P>0.05). Conclusions:In patients with high-risk pulmonary embolism, both catheter-directed thrombolysis and catheter-directed thrombectomy have good efficacy and can promote the relief of clinical symptoms and the recovery of oxygen saturation, improving the prognosis.

Objective To investigate the efficacy and safety of domestic bortezomibˉbased chemotherapy for patients with multiple myeloma (MM). Methods The clinical data of 60 MM patients treated with domestic bortezomibˉbased chemotherapy regimen (the observation group) in the First Affiliated Hospital of Zhengzhou University from April 2018 to October 2018 were retrospectively analyzed, which were compared with 112 MM patients treated with original treatment regimen (the control group) at the same hospital from November 2010 to November 2014. According to the disease stage, the patients were divided into newly diagnosed MM (NDMM) group and relapsed refractory MM (RRMM) group, and efficacy and adverse reactions of domestic bortezomib were evaluated. Results The total response rate (ORR) of the observation group was 71.7% (43/60), severe complete response (sCR) + complete response (CR) rate was 16.7% (10/60), very good partial response (VGPR) rate was 18.3% (11/60), and partial response (PR) rate was 36.7% (22/60). The ORR of NDMM group (45 cases) and RRMM group (15 cases) was 82.2% (37/45) and 40.0% (6/15), respectively, and the difference was statistically significant (χ2＝ 9.877, P < 0.05). There was no significant difference between ISS stage Ⅰ+Ⅱ and stage Ⅲ [ORR: 75.7% (28/37) vs. 65.2% (15/23), respectively; χ2＝0.764, P >0.05]. ORR and CR rates in the NDMM group and RRMM group of the observation group and the control group were not statistically different (all P>0.05). In the treatment of bortezomibˉbased chemotherapy, the common adverse reaction was peripheral neuropathy, mostly belonging to grade 1-2. Other side effects included hematocytopenia, gastrointestinal events and herpes zoster, which could be alleviated or restored to normality after supportive treatments. One patient died of pulmonary infection, respiratory failure and septic shock during the intermittent period of chemotherapy. Conclusion ORR of domestic bortezomibˉbased chemotherapy in treatment of the patients with MM is high, and the incidence of adverse reactions shows no significant increase compared with original drugs.

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models,and to analyze its clinical value.Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease,the patients were divided into the severe group(n=88)and the non-severe group(n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features.Results In the training set,the area under curve(AUC)of the improved machine learning model was 0.922.In the testing set,the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein,blood chlorine,blood magnesium and fibrinogen level,which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis.Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.