Objective To observe the effects of progressive resistance training(PRT)combined with hyperextension traction elastic pressure on pain indexes and rehabilitation outcome in patients with osteoporotic vertebral compression fracture(OVCF)after percutaneous vertebroplasty(PVP).Methods A total of 98 OVCF patients were randomly divided into observation group and control group,with 49 cases in each group.All underwent PVP following preoperative hyperextension traction elastic pressure.After operation,control group was given routine rehabilitation training,while observation group was treated with PRT in addition to routine rehabilitation training.All were intervened for 3 months.The changes of imaging indexes,lumbar pain indexes,lumbar function indexes,bone metabolism indexes and activities of daily living before,at 1 week and 3 months after operation were observed.Results Three months after operation,the anterior vertebral height was higher(P<0.05)and Cobb Angle was lower(P<0.05)in observation group than in control group.Compared with control group,observation group had lower scores on visual analog scale and Oswsetry disability index,obtained higher Japanese Orthopaedic Association score for low back pain,scored higher on activities of daily living scale,and showed lower levels of serum osteocalcin and bone specific alkaline phosphatase(all P<0.05).Conclusion Hyperextension traction elastic pressure before PVP combined with PRT after surgery can better reduce postoperative lumbar pain index,and promote bone growth,further improving lumbar function and activities of daily living in OVCF patients.

OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
Adult ; Humans ; Big Data ; Emergency Service, Hospital ; Triage/methods* ; Intensive Care Units ; Hospitalization ; Retrospective Studies