Indian Wells

Researchers are evaluating a new approach to prevent cardiovascular events in patients at increased risk due to age and conditions like type 2 diabetes, prediabetes, or metabolic syndrome but without known symptomatic cardiovascular disease. The study compares a Cleerly Coronary Artery Disease (CAD) Staging System-based care strategy with standard risk factor-based care to see if the former can better reduce cardiovascular events. The Cleerly system uses imaging to visualize and quantify coronary artery disease and guides personalized treatment and education based on this assessment. The trial uses the Cleerly CAD Staging System device, which employs a proprietary algorithm to detect and stage coronary artery disease and generate a risk score to guide treatment decisions. Participants receive either this stage-based care or the usual care based on traditional risk factors. The study is prospective, randomized, and pragmatic, designed to follow patients over an average of 3.5 years to compare cardiovascular event outcomes between these two care approaches. Participants will be monitored through cardiovascular event tracking throughout the study period. Data collected includes imaging results, risk scores, and treatment adherence to evaluate the impact of the care strategies. The primary outcome is the comparison of cardiovascular event risk between the Cleerly stage-based care and risk factor-based care groups. The study also includes ongoing safety monitoring and personalized management by a cardiologist-led team via digital communication devices.

Researchers are studying how to improve the diagnosis and early detection of diseases such as cardiovascular diseases, cancer, dementia, and traumatic brain injury. This research focuses on using machine learning and data analysis to find subtle patterns in electronic medical records that can help diagnose these diseases earlier and more accurately. The study aims to create combined biomarker risk scores to predict disease development and enhance risk assessment for patients. This is an observational study where no treatments or interventions will be performed. Instead, data from participants will be analyzed using advanced machine learning techniques to improve diagnostic confidence and reduce the time it takes to reach a diagnosis. The study also plans to guide participants to other relevant clinical trials based on physician review and recommendations. Participants will be followed for up to five years, during which researchers will monitor prostate cancer characteristics such as Gleason score, grade group, staging, and disease-specific mortality. The study involves collecting and analyzing all available biomarkers and health determinants to increase diagnostic accuracy. Safety and long-term outcomes will be assessed through ongoing data collection and physician evaluations.