Abstract
Cardiovascular diseases (CVDs) are the primary cause of all death globally. Timely and accurate identification of people at risk of developing an atherosclerotic CVD and its sequelae is a central pillar of preventive cardiology. One widely used approach is risk prediction models; however, currently available models consider only a limited set of risk factors and outcomes, yield no actionable advice to individuals based on their holistic medical state and lifestyle, are often not interpretable, were built with small cohort sizes or are based on lifestyle data from the 1960s, e.g. the Framingham model. The risk of developing atherosclerotic CVDs is heavily lifestyle dependent, potentially making many occurrences preventable. Providing actionable and accurate risk prediction tools to the public could assist in atherosclerotic CVD prevention. Accordingly, we developed a benchmarking pipeline to find the best set of data preprocessing and algorithms to predict absolute 10-year atherosclerotic CVD risk. Based on the data of 464,547 UK Biobank participants without atherosclerotic CVD at baseline, we used a comprehensive set of 203 consolidated risk factors associated with atherosclerosis and its sequelae (e.g. heart failure). Our two best performing absolute atherosclerotic risk prediction models provided higher performance, (AUROC: 0.7573, 95% CI: 0.755-0.7595) and (AUROC: 0.7544, 95% CI: 0.7522-0.7567), than Framingham (AUROC: 0.680, 95% CI: 0.6775-0.6824) and QRisk3 (AUROC: 0.725, 95% CI: 0.7226-0.7273). Using a subset of 25 risk factors identified with feature selection, our reduced model achieves similar performance (AUROC 0.7415, 95% CI: 0.7392-0.7438) while being less complex. Further, it is interpretable, actionable and highly generalizable. The model could be incorporated into clinical practice and might allow continuous personalized predictions with automated intervention suggestions.
Highlights
Cardiovascular diseases (CVDs) are the number one cause of all death [1, 2]
Of 502,551 patients in the UK Biobank, we filtered out 7.6% who already experienced a relevant cardiovascular diseases (CVDs) outcome and the participants being lost or who withdrew from the biobank
We used both Framingham 10-year CVD risk versions, with and without lipids, as well as QRisk3 as baseline models to assess the performance of predicting someone’s 10-year risk of developing an atherosclerotic cardiovascular disease based on a holistic set of risk factors, with a focus on actionable risk factors and outcomes
Summary
Cardiovascular diseases (CVDs) are the number one cause of all death [1, 2]. In 2016, 17.9 million people died of CVDs alone, accounting for 31% of all global deaths [1]. Actionable absolute risk prediction of atherosclerotic cardiovascular disease under accession/application number 34802. Interested parties can apply for the data from UK Biobank directly, at http://www.ukbiobank.ac.uk. The UK Biobank will consider data applications from bona fide researchers for health-related research that is in the public interest. This process for reader access to the UK Biobank is the same process as followed by the authors of this study and will provide the same data. All extracted columns are stated in the supporting information files S1 Table and S2 Table
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
