Globally, the leading cause of years of life lost is ischemic heart disease (IHD). In the EU 13.2 mio. patients are diagnosed with IHD, 700,000 live in the Nordic countries. IHD causes chest pain, myocardial infarcts, reduced physical capacity and reduces life-expectance. IHD is not caused by a single mechanism but rather by a variety of different ones. Many risk factors and disease mechanisms are known, but we urgently need to manage complex data that can drive precise sub-classifications and risk stratification. At present, patients with IHD are generally diagnosed and treated using one-size-fits-all standard regimes. This leads to inefficient, costly, potentially harmful over-management. At the same time, patients with high risk are not identified and treatment not optimized. The objective is to develop and clinically implement personalized medicine (PM) with the dual purpose of avoiding both overtreatment as well as under-treatment in IHD. In this Nordic interdisciplinary collaboration, we intend to establish and merge large Nordic cohorts with well-described IHD genotypes and clinical expressions by combining existing and new data. The purpose is to differentiate between different subgroups of IHD, and from the better characterization identify each patient's cause of IHD. Using a machine learning approach, we will create a clinical integrative IHD algorithm, that will aggregate the available data and in each diagnostic subgroup estimate the risk for future complications in the individual patient. The data foundation will be routinely obtained clinical data, supplemented by data from the Nordic national registries and biobanks, This Nordic collaboration will target this patient group to cross-validate and benchmark the results in a new and unprecedented manner, a major value to the patient, the healthcare system and society in general, reducing use of medication, hospital visits and healthcare expenses.
Project leader: Kristian Hveem
Institution: NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU