Richland

This study aims to collect prospective safety and effectiveness data for the C-Brace System, a microprocessor-controlled knee ankle foot orthosis, following standard care practices. The research focuses on patients with lower extremity pareses who are fitted with the C-Brace device. The goal is to better understand how the C-Brace influences walking speed, balance, risk of falling, and confidence in balance over time. The C-Brace is a custom-made device with thigh, calf, and foot components connected by an ankle joint or spring element. It uses sensors to continuously monitor knee joint movement and walking phases, adjusting hydraulic resistance and knee motion accordingly. Patients enrolled in the registry will undergo baseline evaluation, fitting, training or therapy sessions, and follow-up visits at 6, 12, 24, and 36 months after fitting. Participants will be assessed through various tests including timed walk tests, balance confidence scores, and mobility assessments at different intervals. The study also tracks falls related to device use and changes in activity levels with an activity tracker. Data collected will help characterize the safety and functional impact of the C-Brace over three years of follow-up.

Many patients with severe and sometimes life-threatening symptoms remain undiagnosed despite extensive medical evaluations, leading to uncertainty for them and their families. The National Institutes of Health NIH established the Undiagnosed Diseases Program UDP in 2008 to provide care and answers for individuals with mysterious conditions. Building on the UDPs success, the Undiagnosed Diseases Network UDN was created to enhance diagnosis and research through collaboration among multiple medical research centers. Participants in this observational study undergo extensive clinical evaluations, including phenotyping, genetic analyses, and functional studies of potential disease-causing variants. Testing involves medically necessary procedures and research investigations such as skin biopsies, blood draws, and DNA analysis. The UDN facilitates sharing of identifiable and de-identified health and genetic information among network sites to improve understanding and diagnosis of rare and undiagnosed diseases. Throughout the study, participants provide detailed clinical data and biological samples while researchers collect and share standardized phenotypic and genotypic information. The study aims to create a collaborative research community, facilitate research into causes of undiagnosed diseases, and improve diagnosis and care. Participation includes follow-up assessments over time, with the study continuing through 2028.

Central Line-Associated Bloodstream Infections CLABSIs are a serious problem in U.S. hospitals, leading to higher death rates, longer hospital stays, and increased costs. This research evaluates whether a machine learning ML model that predicts possible CLABSI risk can help Infection Preventionists IPs reduce infection rates compared to usual practices. The study is a prospective, multi-center, cluster-randomized trial conducted in 20 hospitals with the highest CLABSI rates. In this trial, hospitals are split into early and late groups. Early hospitals get access to the ML model via a daily dashboard that flags high-risk patients. Infection Preventionists use this information to provide targeted education and recommend best practices for central line care, including line removal when appropriate. The model was built using extensive patient data and predicts infection risk with high accuracy. The control hospitals continue with routine clinical practice without access to the model during the study period. Participants are hospitalized adults with central lines, and the model scores patients daily if their line has been in place for over 48 hours. Researchers monitor CLABSI rates per 1,000 central line-days, central line removals within 48 hours of alerts, positive blood culture rates, and IP intervention frequencies. Safety outcomes such as pneumothorax and hemorrhage are also tracked. The study duration is about five months with interim and final analyses planned to assess the impact of providing the ML model to Infection Preventionists.

Researchers are comparing the rates of surgical and minimally invasive interventions, as well as any harms, in Medicare beneficiaries treated with the MILD procedure versus those treated with interspinous process decompression IPD for lumbar spinal stenosis with neurogenic claudication. This observational study uses Medicare claims data to follow patients for 24 months after their initial procedure starting from January 1, 2017. The purpose is to evaluate outcomes between these two types of procedures without requiring prior patient enrollment or consent. The study includes two groups patients who received MILD, which is a percutaneous image-guided lumbar decompression performed under fluoroscopic guidance through a dorsal approach to the spine, and patients who received IPD, a different device-based decompression procedure. Data on reoperations and complications will be collected for both groups over a 24-month follow-up period using Medicare claims. Enrollment continues until the sponsor decides to stop. Participants involvement is passive as the study uses existing Medicare claims data. Researchers will monitor rates of harms related to the initial procedure and subsequent surgical or minimally invasive interventions over two years. No direct patient visits or interventions are conducted, and the study is exempt from institutional review board oversight. The total study duration extends to December 2026, covering cases treated since early 2017.