MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record.
Sarah M UrbutMing Wai YeungShaan KhurshidSo Mi Jemma ChoArt SchuermansJakob GermanKodi TaraszkaAkl C FahedPatrick T EllinorLudovic TrinquartGiovanni ParmigianiAlexander GusevPradeep NatarajanPublished in: medRxiv : the preprint server for health sciences (2023)
Currently, coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. We designed a novel and general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. MSGene supports decision making about CAD prevention related to any of these states. We analyzed longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improved discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), with external validation. We also used MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore the potential public health value of our novel multistate model for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics.
Keyphrases
- coronary artery disease
- electronic health record
- public health
- percutaneous coronary intervention
- cardiovascular events
- coronary artery bypass grafting
- decision making
- cardiovascular disease
- dna methylation
- machine learning
- genome wide
- big data
- clinical decision support
- climate change
- deep learning
- loop mediated isothermal amplification
- adverse drug