MULTIDISCIPLINARY RESEARCH PROJECTS ON PERSONALISED MEDICINE – DEVELOPMENT OF CLINICAL SUPPORT TOOLS FOR PERSONALISED MEDICINE IMPLEMENTATION

Cardiovascular disease is the leading cause of death worldwide.

Underlying atherosclerosis and conditions such as myocardial infarction, ischemic heart disease and stroke cause tremendous morbidity, mortality and economic loss. Early identification of patients with high risk for such clinical events enables preventive actions. The use of machine learning (ML) for risk prediction can outperform traditional risk scores.

Although many ML models have been developed over the last years, validation is rare. We do not know how models perform in different clinical settings or populations. Furthermore, as models use numerous and diverse predictors, it is hard to transfer models to other health systems.

Recently, we developed risk prediction models for major adverse cardiovascular events and progression of kidney disease.

However, the models lack external validation, hindering implementation in different clinical contexts and limiting generalizability. This project has 3 main aims. A) to validate and improve our ML models across different populations.

B) to integrate ML models in different hospital information systems and evaluate their impact on daily hospital routine. C) to study effective risk communication strategies in order to effect behavioral changes in patients.

Therefore, our project makes a fundamental contribution towards employing innovative personalized risk prediction and assesses its clinical implementation in a transnational context.