An investigation into the quality of life improvements after vertebral osteomyelitis depending on the status of pathogen detection.

OBJECTIVE: This study aimed to compare clinical characteristics and quality of life (QoL) after vertebral osteomyelitis (VO) based on the status of pathogen detection in microbiological sampling. METHODS: We conducted a post hoc data analysis from a prospective single-center study in a tertiary referral hospital, including VO patients from 2008 to 2020. Data were collected preoperatively (T0) and 1-year post surgery (T1). The primary outcome was QoL, assessed with the Oswestry Disability Index and Core Outcome Measures Index. RESULTS: Data from 133 patients with surgically treated thoracic or lumbar VO were evaluated. The pathogen was detected from cultured intraoperative samples in 100 (75.2%) patients (group 1). Culture remained negative in 33 (24.8%) patients (group 2). Quality of life did not differ significantly between the groups at T1. We observed higher preoperative C-reactive protein values and higher rates of spinal empyema at T0 in group 1. CONCLUSION: Quality of life improved significantly for all patients at T1, but scores remained comparable to those reported by patients with chronic back pain. Quality of life was not affected by pathogen detection. However, attempts to detect pathogens are still indicated due to the concomitant findings, including bacteremia and epidural abscesses, along with the advantages of targeted antibiotic therapy. The most critical step for detection may be avoiding pre-sampling antibiotic administration. Cite this article as: Beyer F, Wenk B, Jung N, Bredow J, Eysel P, Yagdiran A. An investigation into quality of life improvements after vertebral osteomyelitis depending on the status of pathogen detection. Acta Orthop Traumatol Turc., 2024; 10.5152/j.aott.2024.23073 [Epub Ahead of Print].

Clinical trial to evaluate the performance of a flexible self-adherent absorbent dressing coated with a soft silicone layer compared to a standard wound dressing after orthopedic or spinal surgery: study protocol for a randomized controlled trial.

BACKGROUND: Postoperative wound infection is a preventable risk. One potential postoperative complication is blistering, which leads to increased pain, delayed healing, and higher care costs. The incidence of wound blisters has been reported to be between 6 and 24%. The aim of this study is to assess whether the risks of postoperative blistering and wound infections within the first 6 days postsurgery will be reduced using a special dressing compared to a standard one. METHODS/DESIGN: This is a randomized clinical trial in a University hospital. Patients presenting for knee or hip arthroplasty or spine procedures will be assessed against study inclusion and exclusion criteria. After giving written informed consent, patients will be randomized to participate in the 7-day study during hospitalization. One hundred patients will be randomized per group. The primary outcome measure is blistering incidence from day 0 to day 6 postsurgery. Photo documentation will be evaluated in a blinded manner by the Clinical Evaluation Committee (CEC). DISCUSSION: A new dressing will be compared to the standard wound dressing regarding the risk of postoperative blistering, wound infection, and patient comfort. This study will assess the potential advantages of a modern wound dressing. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01988818 (Entered 13 November 2011).

Software-based matching of x-ray images and 3D models of knee prostheses.

BACKGROUND: Revision joint replacements are challenging surgical tasks. Knowing the exact type of primary prosthesis is essential to avoid long preoperative organisation, long operation times, and especially loss of bone and soft-tissue during operation. In daily routine there is often no information about the primary prosthesis. OBJECTIVE: We are developing methods for identifying implanted prostheses from x-ray images by means of matching template images generated from prosthesis CAD data. METHODS: The application is separated into three major components: The "Template Image Generation" adds 3d models of endoprostheses to a database. The "X-ray Image Segmentation" extracts endoprostheses from provided sets of x-ray images. The "Template Matching" finds the best matching prosthesis types in the data base. At the current stage, one prosthesis model (Corin, Knee ProthesisUniglide) was used for evaluating these algorithms. RESULTS: Very accurate identifications with accuracies of about 90% for lateral and over 70% for frontal images could be achieved. CONCLUSIONS: The current results of this feasibility study are very promising. A reliable and fast prosthesis identification process seems realistic to support the surgeon when planning and performing revision arthroplasty. Further improvements of segmentation accuracies and extending the prosthesis data base are intended next steps towards this goal.