7 results match your criteria: "The Six Affiliated Hospital of Xinjiang Medical University[Affiliation]"
Development and validation of machine learning models for intraoperative blood transfusion prediction in severe lumbar disc herniation.
iScience
November 2024
Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China.
Article Synopsis
- Lumbar disc herniation (LDH) is a prevalent source of lower back pain and sciatica, with posterior lumbar interbody fusion (PLIF) being a standard treatment method, prompting a study on predicting blood transfusion needs during surgery.
- This study involved 6,241 patients across 22 medical centers in China and utilized various machine learning techniques to create an optimal predictive model for intraoperative blood transfusion using robust evaluation methods.
- The best-performing model, a simulated annealing support vector machine recursive + stacking model, achieved an area under the curve of 0.884, leading to the creation of a publicly accessible web calculator to aid clinicians in decision-making and improve patient management.
Development of machine learning model for predicting prolonged operation time in lumbar stenosis undergoing posterior lumbar interbody fusion: a multicenter study.
Spine J
October 2024
Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China; State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China. Electronic address:
Article Synopsis
- - The research focuses on developing a clinical model to predict which patients undergoing posterior lumbar interbody fusion (PLIF) for lumbar spinal stenosis are likely to experience prolonged surgical times, which can lead to complications and affect recovery.
- - A total of 3,233 patients from 22 hospitals in China from 2015 to 2022 were included in the study, and their data was analyzed using machine-learning techniques to identify key factors associated with longer surgery durations.
- - The study utilized a training cohort and four test groups, applying various algorithms and performance evaluations to create a predictive model, ultimately aiming to enhance patient safety and surgical outcomes.
Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.
J Orthop Surg Res
February 2024
Department of Spinal Surgery, Guangxi Medical University Affiliated Liuzhou People's Hospital, Liuzhou, China.
Article Synopsis
- The research focused on creating a machine learning model to predict the risk of extended hospital stays before surgery, aiming to enhance patient management.
- Patients who had posterior spinal deformity surgery in China between 2015 and 2022 were included, and their preoperative data was analyzed using various machine learning techniques to identify risk factors.
- The K Nearest Neighbors algorithm was the most effective in predicting prolonged hospitalization, with key factors including preoperative hemoglobin, height, BMI, age, and white blood cells, leading to the development of a web-based calculator for practical use.
Comparing the efficacy of unilateral biportal endoscopic transforaminal lumbar interbody fusion and minimally invasive transforaminal lumbar interbody fusion in lumbar degenerative diseases: a systematic review and meta-analysis.
J Orthop Surg Res
November 2023
Department of Spine Surgery, The Six Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Objective: To compare the efficacy and safety of unilateral biportal endoscopic transforaminal lumbar interbody fusion (BE-TLIF) and minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) in lumbar degenerative diseases.
Methods: This study was registered on International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42023432460). We searched PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wan Fang Database, and Wei Pu Database by computer to collect controlled clinical studies on the efficacy and safety of unilateral BE-TLIF and MIS-TLIF in lumbar degenerative diseases from database establishment to May 2023.
Palliative Treatment for the Management of Advanced Pelvic Hydatid Bone Disease.
Am J Trop Med Hyg
September 2023
Department of Spine Surgery, The Six Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Hydatid bone disease is a zoonotic parasitic infection that is caused primarily by the tapeworm Echinococcus granulosus, and it continues to be a major public health concern in pastoral regions. The reconstruction of limb function after limb salvage surgery remains a challenge for clinicians. The purpose of this study was to determine the clinical efficacy of palliative treatment of the management of advanced pelvic hydatid bone disease.
View Article and Find Full Text PDFEfficacy and safety of tranexamic acid in posterior lumbar interbody fusion: a meta-analysis of randomized controlled trials.
J Orthop Surg Res
January 2023
Department of Spine Surgery, The Six Affiliated Hospital of Xinjiang Medical University, Ürümqi, 830002, Xinjiang, China.
Objective: To evaluate the efficacy and safety of tranexamic acid (TXA) in hemostasis in patients undergoing posterior lumbar interbody fusion (PLIF) by meta-analysis.
Methods: This study was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022354812). The databases PubMed, Cochrane Library, Web of Science, and Embase were searched for randomized controlled trial (RCT) papers on the use of TXA in patients with PLIF from database establishment to August 2022.
Screening for potential genes associated with bone overgrowth after mid-shaft femur fracture in a rat model.
J Orthop Surg Res
January 2017
The Six Affiliated Hospital of Xinjiang Medical University Medical Examination Center, Autonomous Region, Five Star South Road, No. 39, Urumqi City, Xinjiang Uygur, 830002, China.
Background: We investigated the underlying molecular mechanisms of bone overgrowth after femoral fracture by using high-throughput bioinformatics approaches.
Methods: The gene expression profile of GSE3298 (accession number) was obtained from the Gene Expression Omnibus database. Sixteen femoral growth plate samples, including nine samples without fracture and seven fracture samples for seven time points, were used for analysis.