Management of chronic cardiometabolic disease and treatment discontinuity in adult ADHD patients

Objectives

• Determine if and how ADHD in adults worsens prognosis and hampers the management of cardiometabolic disease, leveraging the largest data sets and population registries available world-wide.
• Identify the cardiometabolic risks and benefits of multidisciplinary treatment approaches in patients with ADHD, performing advanced pharmacological and epidemiological analyses on available data as well as  acquiring new and unique real-world data using active and passive apps for smartphones and a groundbreaking new advanced smartwatch for continuous health monitoring.
• Pinpoint reasons for ADHD treatment discontinuity in adult patients with and without cardiometabolic disease. Capitalizing on so far unused real-world clinician’s data through new algorithms, created utilizing Machine Learning (ML) and natural language processing techniques in conjunction with using state-of-the-art genomic approaches.
• Discern patients with ADHD at high-risk for poor cardiometabolic outcomes and treatment discontinuity by applying novel AI driven methods like deep learning neural networks (DLNNs) on existing large-scale cohort  studies and linked electronic health record databases in multiple countries with different health care systems.
• Identify optimized and personalized treatment approaches across multiple disciplines, to minimize harm and maximize positive changes in disease prognosis and to improve treatment discontinuity.
• Improve clinical outcomes, as well as quality of life in adult ADHD patients with co-occurring cardiometabolic disease.