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(1) of these 700,000 live in the Nordic countries. IHD causes chest pain, myocardial infarcts, reduced physical capacity and reduces life-expectance. IHD is considered a chronic disease and may progress despite current optimal treatment.
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 approaches 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 residual high risk are not identified and treatment not optimized or compliance re-enforced.
The objective is to develop and clinically implement personalized medicine (PM) with the dual purpose of avoiding futile overtreatment as well as undertreatment in IHD.
In this Nordic interdisciplinary collaboration, we intend to establish and merge large Nordic cohorts with well-described IHD geno- and phenotypes, by combining existing data and new data. The purpose is to differentiate between different subgroups of IHD, and from the deep 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, e.g. birth weight, pregnancy complications, familial occurrence of IHD, genetics, socio-economic factors, up to 20-40-year long trajectories of time-ordered co-morbidities, medication use etc.
A Nordic collaboration will make it feasible to target this heterogeneous patient group and make it possible to cross-validate and benchmark the results in a new and unprecedented, robust manner. This will be of major value to the patient, the healthcare system and society in general, as we can reduce overall use of medication, reduce hospital visits and thus healthcare expenses.