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MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record
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, depend...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659503/ https://www.ncbi.nlm.nih.gov/pubmed/37986972 http://dx.doi.org/10.1101/2023.11.08.23298229 |
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author | Urbut, Sarah M. Yeung, Ming Wai Khurshid, Shaan Cho, So Mi Jemma Schuermans, Art German, Jakob Taraszka, Kodi Fahed, Akl C. Ellinor, Patrick Trinquart, Ludovic Parmigiani, Giovanni Gusev, Alexander Natarajan, Pradeep |
author_facet | Urbut, Sarah M. Yeung, Ming Wai Khurshid, Shaan Cho, So Mi Jemma Schuermans, Art German, Jakob Taraszka, Kodi Fahed, Akl C. Ellinor, Patrick Trinquart, Ludovic Parmigiani, Giovanni Gusev, Alexander Natarajan, Pradeep |
author_sort | Urbut, Sarah M. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10659503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106595032023-11-20 MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record Urbut, Sarah M. Yeung, Ming Wai Khurshid, Shaan Cho, So Mi Jemma Schuermans, Art German, Jakob Taraszka, Kodi Fahed, Akl C. Ellinor, Patrick Trinquart, Ludovic Parmigiani, Giovanni Gusev, Alexander Natarajan, Pradeep medRxiv Article 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. Cold Spring Harbor Laboratory 2023-11-08 /pmc/articles/PMC10659503/ /pubmed/37986972 http://dx.doi.org/10.1101/2023.11.08.23298229 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Urbut, Sarah M. Yeung, Ming Wai Khurshid, Shaan Cho, So Mi Jemma Schuermans, Art German, Jakob Taraszka, Kodi Fahed, Akl C. Ellinor, Patrick Trinquart, Ludovic Parmigiani, Giovanni Gusev, Alexander Natarajan, Pradeep MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title | MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title_full | MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title_fullStr | MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title_full_unstemmed | MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title_short | MSGene: Derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
title_sort | msgene: derivation and validation of a multistate model for lifetime risk of coronary artery disease using genetic risk and the electronic health record |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659503/ https://www.ncbi.nlm.nih.gov/pubmed/37986972 http://dx.doi.org/10.1101/2023.11.08.23298229 |
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