Cargando…

Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry

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 coro...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018106/
https://www.ncbi.nlm.nih.gov/pubmed/36691990
http://dx.doi.org/10.1002/clc.23964
_version_ 1784907740701786112
author 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_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_sort Park, Hyung‐Bok
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10018106
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-100181062023-03-17 Risk factors based vessel‐specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry 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 Clin Cardiol Clinical Trial 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. John Wiley and Sons Inc. 2023-01-24 /pmc/articles/PMC10018106/ /pubmed/36691990 http://dx.doi.org/10.1002/clc.23964 Text en © 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Trial
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
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: Results from the PARADIGM registry
title_full 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_fullStr 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_full_unstemmed 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_short 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_sort risk factors based vessel‐specific prediction for stages of coronary artery disease using bayesian quantile regression machine learning method: results from the paradigm registry
topic Clinical Trial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018106/
https://www.ncbi.nlm.nih.gov/pubmed/36691990
http://dx.doi.org/10.1002/clc.23964
work_keys_str_mv AT parkhyungbok riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT leejina riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT hongyongtaek riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT byungchangso riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT kimwonse riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT leebyoungk riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT linfayy riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT hadamitzkymartin riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT kimyongjin riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT conteedoardo riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT andreinidaniele riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT pontonegianluca riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT budoffmatthewj riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT gottliebilan riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT chuneunju riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT cademartirifilippo riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT maffeierica riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT marqueshugo riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT goncalvespedrodea riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT leipsicjonathona riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT shinsanghoon riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT choijungh riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT virmanirenu riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT samadyhabib riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT chinnaiyankavitha riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT stonepeterh riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT bermandaniels riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT narulajagat riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT shawlesleej riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT baxjeroenj riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT minjamesk riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT kookwoong riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry
AT changhyukjae riskfactorsbasedvesselspecificpredictionforstagesofcoronaryarterydiseaseusingbayesianquantileregressionmachinelearningmethodresultsfromtheparadigmregistry