Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2023 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/149277 |
Resumo: | Funding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02) and funded in part by a generous gift from the Dalio Institute of Cardiovascular Imaging and the Michael Wolk Foundation. This work was also supported by the National Research Foundation of Korea [RS‐2022‐00165404, 2022R1A5A6000840, 2020R1I1A1A01073151]. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Publisher Copyright: © 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC. |
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Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning methodResults from the PARADIGM registrycardiovascular risk factorscoronary artery diseasemachine learningCardiology and Cardiovascular MedicineSDG 3 - Good Health and Well-beingFunding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02) and funded in part by a generous gift from the Dalio Institute of Cardiovascular Imaging and the Michael Wolk Foundation. This work was also supported by the National Research Foundation of Korea [RS‐2022‐00165404, 2022R1A5A6000840, 2020R1I1A1A01073151]. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Publisher Copyright: © 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.Background and Hypothesis: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. Methods: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. Results: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%–50% and 5.6%–7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. Conclusions: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNPark, Hyung BokLee, JinaHong, YongtaekByungchang, SoKim, WonseLee, Byoung K.Lin, Fay Y.Hadamitzky, MartinKim, Yong JinConte, EdoardoAndreini, DanielePontone, GianlucaBudoff, Matthew J.Gottlieb, IlanChun, Eun JuCademartiri, FilippoMaffei, EricaMarques, HugoGonçalves, Pedro de A.Leipsic, Jonathon A.Shin, SanghoonChoi, Jung H.Virmani, RenuSamady, HabibChinnaiyan, KavithaStone, Peter H.Berman, Daniel S.Narula, JagatShaw, Leslee J.Bax, Jeroen J.Min, James K.Kook, WoongChang, Hyuk Jae2023-02-15T22:20:38Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/149277eng0160-9289PURE: 52690369https://doi.org/10.1002/clc.23964info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:31:11Zoai:run.unl.pt:10362/149277Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:41.489206Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method Results from the PARADIGM registry |
| title |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| spellingShingle |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method Park, Hyung Bok cardiovascular risk factors coronary artery disease machine learning Cardiology and Cardiovascular Medicine SDG 3 - Good Health and Well-being |
| title_short |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| title_full |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| title_fullStr |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| title_full_unstemmed |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| title_sort |
Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method |
| author |
Park, Hyung Bok |
| author_facet |
Park, Hyung Bok Lee, Jina Hong, Yongtaek Byungchang, So Kim, Wonse Lee, Byoung K. Lin, Fay Y. Hadamitzky, Martin Kim, Yong Jin Conte, Edoardo Andreini, Daniele Pontone, Gianluca Budoff, Matthew J. Gottlieb, Ilan Chun, Eun Ju Cademartiri, Filippo Maffei, Erica Marques, Hugo Gonçalves, Pedro de A. Leipsic, Jonathon A. Shin, Sanghoon Choi, Jung H. Virmani, Renu Samady, Habib Chinnaiyan, Kavitha Stone, Peter H. Berman, Daniel S. Narula, Jagat Shaw, Leslee J. Bax, Jeroen J. Min, James K. Kook, Woong Chang, Hyuk Jae |
| author_role |
author |
| author2 |
Lee, Jina Hong, Yongtaek Byungchang, So Kim, Wonse Lee, Byoung K. Lin, Fay Y. Hadamitzky, Martin Kim, Yong Jin Conte, Edoardo Andreini, Daniele Pontone, Gianluca Budoff, Matthew J. Gottlieb, Ilan Chun, Eun Ju Cademartiri, Filippo Maffei, Erica Marques, Hugo Gonçalves, Pedro de A. Leipsic, Jonathon A. Shin, Sanghoon Choi, Jung H. Virmani, Renu Samady, Habib Chinnaiyan, Kavitha Stone, Peter H. Berman, Daniel S. Narula, Jagat Shaw, Leslee J. Bax, Jeroen J. Min, James K. Kook, Woong Chang, Hyuk Jae |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
| dc.contributor.author.fl_str_mv |
Park, Hyung Bok Lee, Jina Hong, Yongtaek Byungchang, So Kim, Wonse Lee, Byoung K. Lin, Fay Y. Hadamitzky, Martin Kim, Yong Jin Conte, Edoardo Andreini, Daniele Pontone, Gianluca Budoff, Matthew J. Gottlieb, Ilan Chun, Eun Ju Cademartiri, Filippo Maffei, Erica Marques, Hugo Gonçalves, Pedro de A. Leipsic, Jonathon A. Shin, Sanghoon Choi, Jung H. Virmani, Renu Samady, Habib Chinnaiyan, Kavitha Stone, Peter H. Berman, Daniel S. Narula, Jagat Shaw, Leslee J. Bax, Jeroen J. Min, James K. Kook, Woong Chang, Hyuk Jae |
| dc.subject.por.fl_str_mv |
cardiovascular risk factors coronary artery disease machine learning Cardiology and Cardiovascular Medicine SDG 3 - Good Health and Well-being |
| topic |
cardiovascular risk factors coronary artery disease machine learning Cardiology and Cardiovascular Medicine SDG 3 - Good Health and Well-being |
| description |
Funding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02) and funded in part by a generous gift from the Dalio Institute of Cardiovascular Imaging and the Michael Wolk Foundation. This work was also supported by the National Research Foundation of Korea [RS‐2022‐00165404, 2022R1A5A6000840, 2020R1I1A1A01073151]. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Publisher Copyright: © 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-02-15T22:20:38Z 2023 2023-01-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/10362/149277 |
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http://hdl.handle.net/10362/149277 |
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eng |
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eng |
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0160-9289 PURE: 52690369 https://doi.org/10.1002/clc.23964 |
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openAccess |
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