Transthyretin amyloid cardiopathy (ATTR-CM) is difficult to diagnose due to its similar laboratory findings and symptoms to other heart diseases, resulting in a low diagnosis rate of approximately 12%. Consequently, there are many undiagnosed patients, and the lack of sufficient access to appropriate treatment remains a significant challenge.
In recent years, advances in treatments have made it possible to slow the progression of heart failure, making early diagnosis more essential than ever.
Given this context, early diagnosis is essential for improving the prognosis of this disease, and to achieve this, it is crucial to create “triggers for clinical suspicion” not only in specialized facilities but also in general clinical practice. We believe that AI-enabled diagnostic support tools hold the key to solving this challenge. However, their widespread implementation faces complex challenges, including an underdeveloped infrastructure for clinical use, psychological barriers in the field, and guidelines that are still under development.
Since these challenges are difficult for any single entity to resolve on its own, this consortium was established with the aim of driving the transformation of the diagnostic process and its societal implementation by fostering collaboration among clinicians, pharmaceutical companies, and IT and MedTech firms to gather their respective expertise. Through the optimization of diagnostic pathways based on high-precision AI screening, the generation of evidence, and the development of guidelines, this consortium aims to increase the diagnosis rate of ATTR-CM to 45% by 2030.