Funded by ERA PerMed.
Cardiovascular diseases are the leading cause of death globally, making early identification of patients at high risk of major adverse cardiovascular events (MACE) crucial. Currently, these patients are identified using clinical prediction models based on risk scores that have several limitations. Firstly, most rule-based scores require manual calculation or additional data entry, which makes it infeasible to screen all hospitalized patients due to resource constraints. This results in a selection bias, as only a subset of patients are evaluated, leading to many high-risk patients remaining undetected. Secondly, traditionally used risk scores have been developed for specific populations, meaning they do not consider diverse individual risk factors.
An alternative approach is to use predictions based on machine learning (ML) models, which can incorporate a larger number of predictors and account for nonlinearity in data. In particular, these ML models can use electronic health record (EHR) data to estimate MACE risk for individual patients. This combination of ML models and EHR data allows for rapid, automated, and personalized risk prediction that can be applied to large patient groups. However, although numerous ML models have been developed in recent years, validation is rare, and it is unclear how these models perform in different clinical settings or with different populations.
The PRE-CARE ML project aims to further develop and evaluate ML models, specifically federated learning (FL) models, that can estimate a patient's MACE risk using EHR data. To achieve this, the project has three main goals: (i) validate and improve risk-predicting ML models across different hospital networks and populations, (ii) integrate ML models into different hospital information systems and evaluate their impact on daily hospital routines, and (iii) investigate effective risk communication strategies to encourage behavioral changes in patients.
