Repository logo
 
Publication

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

2023-03Journal article Open access
Simple item page
dc.contributor.authorPark, Hyung Bok
dc.contributor.authorLee, Jina
dc.contributor.authorHong, Yongtaek
dc.contributor.authorByungchang, So
dc.contributor.authorKim, Wonse
dc.contributor.authorLee, Byoung K.
dc.contributor.authorLin, Fay Y.
dc.contributor.authorHadamitzky, Martin
dc.contributor.authorKim, Yong Jin
dc.contributor.authorConte, Edoardo
dc.contributor.authorAndreini, Daniele
dc.contributor.authorPontone, Gianluca
dc.contributor.authorBudoff, Matthew J.
dc.contributor.authorGottlieb, Ilan
dc.contributor.authorChun, Eun Ju
dc.contributor.authorCademartiri, Filippo
dc.contributor.authorMaffei, Erica
dc.contributor.authorMarques, Hugo
dc.contributor.authorGonçalves, Pedro de A.
dc.contributor.authorLeipsic, Jonathon A.
dc.contributor.authorShin, Sanghoon
dc.contributor.authorChoi, Jung H.
dc.contributor.authorVirmani, Renu
dc.contributor.authorSamady, Habib
dc.contributor.authorChinnaiyan, Kavitha
dc.contributor.authorStone, Peter H.
dc.contributor.authorBerman, Daniel S.
dc.contributor.authorNarula, Jagat
dc.contributor.authorShaw, Leslee J.
dc.contributor.authorBax, Jeroen J.
dc.contributor.authorMin, James K.
dc.contributor.authorKook, Woong
dc.contributor.authorChang, Hyuk Jae
dc.date.accessioned2024-06-12T13:38:58Z
dc.date.available2024-06-12T13:38:58Z
dc.date.issued2023-03
dc.description.abstractBackground 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1002/clc.23964pt_PT
dc.identifier.eid85147146563
dc.identifier.issn0160-9289
dc.identifier.pmcPMC10018106
dc.identifier.pmid36691990
dc.identifier.urihttp://hdl.handle.net/10400.14/45473
dc.identifier.wos000923065400001
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCardiovascular risk factorspt_PT
dc.subjectCoronary artery diseasept_PT
dc.subjectMachine learningpt_PT
dc.titleRisk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: results from the PARADIGM registrypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage327pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage320pt_PT
oaire.citation.titleClinical Cardiologypt_PT
oaire.citation.volume46pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
67181933.pdf
Size:
1.49 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.44 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CIIS - Contribuições em Revistas Científicas / Contribution to Journals