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.

Researchers are investigating rare and undiagnosed disorders through the Undiagnosed Diseases Network (UDN), which builds on the NIH Undiagnosed Diseases Program (UDP). Many patients with severe and unexplained symptoms undergo numerous tests without a diagnosis, leaving families uncertain about their health futures. The UDN aims to provide answers, advance understanding of disease mechanisms, apply genomic technologies, and identify potential treatment targets for these complex cases. Participants with undiagnosed disorders undergo thorough clinical evaluations across multiple research centers. The process includes detailed phenotyping, genetic testing, functional studies, and medically necessary procedures such as skin biopsies and blood draws. The UDN facilitates sharing of identifiable phenotypic and genetic information among network sites to enhance collaboration and improve diagnosis and care. During the study, participants receive comprehensive assessments including clinical exams and laboratory tests to collect high-quality data on their condition. Researchers measure outcomes related to the development of a collaborative research community, investigation of disease causes, and improvements in diagnosis and care protocols. Participants may also provide samples for DNA analysis and agree to data sharing to support ongoing research. The study accommodates individuals unable to consent through appropriate enrollment procedures and includes follow-up evaluations as needed.

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 evaluating the outcomes of two treatments for lumbar spinal stenosis with neurogenic claudication (LSS with NC) in Medicare beneficiaries. This observational study compares the rates of surgical and minimally invasive interventions, as well as any harms, occurring within 24 months after receiving either the MILD procedure or Interspinous Process Decompression (IPD). The study uses Medicare claims data starting from patients treated on or after January 1, 2017, and continues enrollment until the sponsor stops it. The study groups include Medicare patients who underwent the MILD procedure, which involves a partial decompression performed under fluoroscopic image guidance through the removal of tissue and bone at the symptomatic spinal level. The control group consists of Medicare patients treated with Interspinous Process Decompression during the same enrollment period. Both groups are monitored for reoperation and harms for 24 months following their initial treatment. Participants are included based on Medicare claims with the study's NCT number, which automatically enrolls them without requiring prior consent. Researchers will analyze Medicare claims data to track surgical or minimally invasive interventions and any complications related to the initial procedure over two years. The study does not involve direct patient visits or interventions and is exempt from Institutional Review Board oversight. The total follow-up duration for outcome measurement is 24 months after the index procedure.