Objective To evaluate the value of MRI and MDCT in detecting both inferior vena cava tumor thrombus and vena cava wall invasion in renal cell carcinoma. Methods Databases including PubMed, EMbase, The Cochrane Library, MEDLINE (Ovid), CBM, CNKI, VIP and WanFang Data were searched from January 2000 to February 2012. Relevant studies were screened on the basis of the inclusion and exclusion criteria, and then quality assessment and data extraction were conducted. Then heterogeneity test and meta-analysis were conducted using RevMan 5 and Meta-disc 1.4. Results A total of 6 trials involving 244 patients and 246 cases of renal cell carcinoma were included. The results of meta-analysis showed that, for the MRI group and the MDCT group, the sensitivity was 0.963 and 0.952, the specificity was 0.969 and 0.979, the value of +LR was 9.759 and 15.57, the value of ?LR was 0.091 and 0.108, and the dOR was 198.71 and 251.54, respectively. There were no significant differences in pooled effect-size among groups (Pgt;0.05). The area under curve (AUC) of summary ROC curve analysis as well as Q index of the MDCT group were 0.981 8 and 0.940 7, respectively. Conclusion There is no significant difference in the value of MRI and MDCT in detecting inferior vena cava tumor thrombus induced by renal cell carcinoma. More original studies on vena cava wall invasion by tumor thrombus should be conducted in the future due to the limitation of current materials.
Objective To explore clinical effect of failure mode and effect analysis in improving the submission rate of pathogen examination in counterpart supported high-altitude county hospitals, and formulate practical measures and methods suitable for high-altitude county hospitals to improve the submission rate of pathogen examination. Methods Patients admitted to the People’s Hospital of Ganzi County between January and December 2024 were selected. The data of hospitalized patients between January and June 2024 were as the control group, and the data of hospitalized patients between July and December 2024 were as the intervention group. The study analyzed and compared the submission rate of pathogen testing and the pass rate of microbiological test specimens before antimicrobial treatment between the two groups. Results A total of 3 984 patients were included. Among them, there were 1 748 cases in the control group and 2 236 cases in the intervention group. A total of 10 risk factors and 2 high-risk points were identified. There were statistically significant differences in the submission rate of pathogen specimens before antibiotic treatment [36.21% (633/1 748) vs. 49.33% (1 103/2 236); χ2=68.646, P<0.001] and the qualified rate of microbiological test specimens [26.75% (122/456) vs. 36.45% (261/716); χ2=11.910, P=0.001] between the control group and the intervention group. Conclusions Failure mode and effect analysis can effectively find out the weak points in low pathogen examination submission rate in high-altitude county hospitals. According to the high-risk points to guide the formulation of relevant measures, the pathogen submission rate in the region can be effectively improved.
Objective To investigate the relation between the sites of colorectal cancer and liver metastatic distribution. Methods The enhanced multiple-slice spiral CT images and clinical data of 105 cases diagnosed colorectalcancer with liver metastases admitted from January 2010 to April 2012 were analyzed retrospectively. Primary site of the tumor， numbers of the metastases on CT images， and the anatomical position of the inferior mesenteric vein （IMV） terminates were recorded. Results ①The ratio of metastases in the right and left hemiliver was 2∶1 for 38 right-sided primary tumors as compared with 1.2∶1 for 67 left-sided primary tumors. The pattern of lobar distribution was significantly different in the two groups （χ2=8.709， P=0.003）. ②In the left-sided colon cancer group， the ratio of metastases in the right and left hemiliver was 65∶98 for 28 patients with IMV terminating in splenic vein （SpV）， 116∶52 for 36 patients with IMV terminating in superior mesenteric vein （SMV）， and 13∶15 for 3 patients with IMV terminating in the junction of SMV and SpV. The pattern of lobar distribution was significantly different among the three groups （χ2=28.575， P=0.000）. Further comparison between the former two groups， the difference was statistically significant （χ2=27.951， P=0.000）. ③In 25 patients with IMV terminating in SpV， the metastases of 19 cases were mainly distributed in the left lobe of liver （P=0.001）；In 34 patients with IMV terminating in SMV， the metastases of 25 cases located mainly in the right hepatic lobe （P=0.000）. Conclusions Right-sided colon cancers selectively involve the right lobe of liver， while left-sided tumors selectively involve the right lobe of liver when its IMV terminates in SMV and involve the left lobe when its IMV terminates in SpV， respectively. The discovery may help shorting the diagnostic workup in patients presenting with liver metastases from an unknown primary site， and may improve the detection rate of metastases in initial diagnosis and follow-up.
Objective To explore the clinical effect of failure mode and effect analysis (FMEA) combined with PDCA cycle management model in the prevention and control of multidrug-resistant organisms (MDROs) in intensive care unit (ICU), and provide evidences for drawing up improvement measures in healthcare-associated MDRO infections in ICU. Methods In January 2020, a risk assessment team was established in the Department of Critical Care Medicine, the First People’s Hospital of Longquanyi District of Chengdu, to analyze the possible risk points of MDRO infections in ICU from then on. FMEA was used to assess risks, and the failure modes with high risk priority numbers were selected to evaluate the high-risk points of MDRO infections. The causes of the high-risk points were analyzed, and improvement measures were formulated to control the risks through PDCA cycle management model. The incidence of healthcare-associated MDRO infections in ICU, improvement of high-risk events, and satisfaction of doctors and nurses after the implementation of intervention measures (from January 2020 to June 2021) were retrospectively collected and compared with those before the implementation of intervention measures (from January 2018 to December 2019). Results Six high-risk factors were screened out, namely single measures of isolation, unqualified cleaning and disinfection of bed units, irrational use of antimicrobial agents, weak consciousness of isolation among newcomers of ICU, weak awareness of pathogen inspection, and untimely disinfection. The incidence of healthcare-associated MDRO infections was 2.71% (49/1800) before intervention and 1.71% (31/1808) after intervention, and the difference between the two periods was statistically significant (χ2=4.224, P=0.040). The pathogen submission rate was 56.67% (1020/1800) before intervention and 61.23% (1107/1808) after intervention, and the difference between the two periods was statistically significant (χ2=7.755, P=0.005). The satisfaction rate of doctors and nurses was 75.0% (30/40) before intervention and 95.0% (38/40) after intervention, and the difference between the two periods was statistically significant (χ2=6.275, P=0.012). Conclusions FMEA can effectively find out the weak points in the prevention and treatment of MDRO infections in ICU, while PDCA model can effectively formulate improvement measures for the weak points and control the risks. The combined application of the two modes provides a scientific and effective guarantee for the rational prevention and treatment of MDRO infections in ICU patients.
The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T2KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T2KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×106 vs. 106.1×106, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
