Improving prevention of cardiovascular disease: from proof of principle to implementation readiness of a live cardiovascular risk management dashboard in electronic health record in routine clinical practice

Michiel Bots, Menno Brandjes

Cardiovascular risk management (CVRM) guidelines support and advocate use of risk prediction algorithms estimating 10 year risk of a cardiovascular event. Risk estimates lead to treatment decisions. Current algorithms are inflexible (do-not run when data misses), perform modestly, and use limited information. Also, algorithms are not integrated in electronic health records (EHR) limiting use in practice. Information in EHR is phenomenal and potentially suited to improve prediction algorithms through data mining, machine learning and neural network techniques. Furthermore, integration in EHR is key. Yet, whether this results in better and valid risk estimates and improves quality of care and patient outcomes is unknown.

This project aims to explore innovative big data analytics for development of flexible and improved risk prediction algorithms to estimate cardiovascular risk to apply 'live' in routine clinical practice to improve cardiovascular prevention.



Source of Funding

The Heart Foundation aims to detect and treat cardiovascular diseases earlier.

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  • Funder :Hartstichting
  • Project Category : PPS Call 2018
  • Project Reference : 2018B006
  • Funded Period : Oct 2018 - present
  • Funded Amount : EUR 250,000