BACKGROUND: Total knee arthroplasty is a procedure for treatment of knee osteoarthritisa with standardized, mature technology and affirmative efficacy. Total knee arthroplasty can result in overt excessive bleeding, decreased hemoglobin levels, patient mouth infection and other complications. As a new technology, autologous blood transfusion device can effectively reduce the rate of blood transfusion through reinfusing the unwashed and filterable drainage blood after operation. Up to now, no systematic reviews incorporating meta-analyses have found directly sufficient evidence to compare autologous blood transfusion drainage and no drainage after primary total knee arthroplasty. OBJECTIVE: To study the clinical efficacy, safety and potential advantages of the application of autologous blood transfusion device/no drainage based on the meta-analysis. METHODS:PubMed, Embase, the Cochrane Library, CBMdisc, China HowNet, VIP, Wanfang database were searched comprehensively by computer. The search strategies were developed by the way of MeSH terms combining with free words: “total knee replacement” OR “total knee arthroplasty” OR “total knee prosthesis” OR “unicompartmental” OR “unicondylar” OR “unicompartmenta” OR “arthroplasty, replacement, knee” [MeSH terms] AND “autologous blood transfusion” OR “Autotransfusion” OR “blood transfusion, autologous” [MeSH Terms] OR “Intraoperative Blood Salvage” OR “Intraoperative Blood” OR “Postoperative Blood Salvage” OR “Intraoperative Blood Cel Salvage” OR “Operative Blood Salvage” [MeSH Terms]. Data included in the final literature were analyzed using RevMan 5.3.5 software recommended by Cochrane. The main outcome measure was the rate of transfusion. The secondary outcome measures were the average change in hemoglobin, hemoglobin levels at the 3rd day, hospitalization time and intraoperative mouth infection rate. RESULTS AND CONCLUSION:Five randomized controlled trials, a total of 667 patients were enroled. Meta-analysis results showed that there were no significant differences in the transfusion rate (OR=0.73, 95%CI: 0.47-1.13;Z=1.41,P=0.16), average change in hemoglobin (WMD=0.20, 95%CI:-0.28-0.68;Z=0.82,P=0.41), the hemoglobin levels at the 3rdday (WMD=0.41, 95%CI:-0.26-1.09;Z=1.20,P=0.23), hospitalization time (OR=1.01, 95%CI: 0.06-16.27;Z= 0.01,P=1.00), intraoperative mouth infection rate (OR=1.01, 95%CI: 0.06-16.27;Z=0.01,P=1.00) between the postoperative use of autologous blood transfusion and no drainage. These results suggest that the meta-analysis of outcome measures has not provided the evidence-based medical support for the clinical efficacy of autologous blood transfusion device (including blood transfusion rate, the average change in hemoglobin, average hemoglobin change at the 3rd day, hospitalization time). Given the inherent limitations of the quality of the included studies and the publication bias, future high-quality, large-volume, multi-center randomized controled trials are awaited to confirm and update the findings of this analysis.

Objective To assess the clinical efficiency , safety and potential advantages of autologous blood transfusion (ABT) drains compared with the closed-suction/no drainage. Methods Pubmed, Embase, Cochrane Library, CBMdisc, CNKI, VIP and WANGFANG were searched comprehensively. The statistical anal-ysis was conducted by using the Cochrane Collaboration review Manager 5.3.5. Results The pooled data of seventeen RCTs including a total of 1 993 patients showed that the patients in the ABT drainage group might benefit from the low rate of blood transfusion [ 16 . 59% and 37 . 47%, OR: 0 . 28 ( 0 . 14 ~ 0 . 55 ); 13 . 05% and 16.91%, OR: 0.73 (0.47 ~ 1.13), respectively]. The ABT drainage and the closed-suction drainage/no drainage have the similar clinical efficiency and safety length of hospital stay and wound infection on days 3 post-operative haemoglobin. Conclusion This systematic review provides the evidence that the ABT drainage offers a safe and efficient alternative to CS/no drainage with the lowered blood transfusion rate.

BACKGROUND:MRI is important for the diagnosis of early knee osteoarthritis.MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE:Through deep learning of MRI images of knee osteoarthritis,the segmentation of femur,tibia,patella,cartilage,meniscus,ligaments,muscles and effusion of knee can be automatically divided,and then volume of knee fluid and muscle content were measured. METHODS:100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset(n=160),validation dataset(n=20),and test dataset(n=20)according to the ratio of 8:1:1.The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model.A Coarse MRI segmentation model of the knee sagittal plane was trained first,and the rough segmentation results were used as a mask,and then the fine segmentation model was trained.The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input,and DeepLab v3 was used to segment bone,cartilage,ligament,meniscus,muscle,and effusion of knee,and 3D reconstruction was finally displayed and automatic measurement results(muscle content and volume of knee fluid)were displayed to complete the deep learning application program.The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION:(1)The 26 normal subjects were selected,including 13 females and 13 males,with a mean age of(34.88±11.75)years old.The mean muscle content of the knee joint was(1 051 322.94±2 007 249.00)mL,the mean median was 631 165.21 mL,and the mean volume of effusion was(291.85±559.59)mL.The mean median was 0 mL.(2)There were 38 patients with knee osteoarthritis,including 30 females and 8 males.The mean age was(68.53±9.87)years old.The mean muscle content was(782 409.18±331 392.56)mL,the mean median was 689 105.66 mL,and the mean volume of effusion was(1 625.23±5 014.03)mL.The mean median was 178.72 mL.(3)There was no significant difference in muscle content between normal people and knee osteoarthritis patients.The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects,and the difference was significant(P<0.05).(4)It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past.The more accuracy evaluation of knee osteoarthritis was necessary,and the image segmentation was processed more precisely in the future to improve the accuracy of the results.