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dc.contributor.authorRicciardi, Carlo
dc.contributor.authorEdmunds, Kyle J
dc.contributor.authorRecenti, Marco
dc.contributor.authorSigurdsson, Sigurdur
dc.contributor.authorGudnason, Vilmundur
dc.contributor.authorCarraro, Ugo
dc.contributor.authorGargiulo, Paolo
dc.date.accessioned2020-09-08T14:14:27Z
dc.date.available2020-09-08T14:14:27Z
dc.date.issued2020-02-18
dc.date.submitted2020-09
dc.identifier.citationRicciardi C, Edmunds KJ, Recenti M, et al. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep. 2020;10(1):2863. Published 2020 Feb 18. doi:10.1038/s41598-020-59873-9en_US
dc.identifier.pmid32071412
dc.identifier.doi10.1038/s41598-020-59873-9
dc.identifier.urihttp://hdl.handle.net/2336/621518
dc.descriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Downloaden_US
dc.description.abstractThe nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.en_US
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.relation.urlhttps://www.nature.com/articles/s41598-020-59873-9en_US
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029006/en_US
dc.subjectBlóðrásarsjúkdómaren_US
dc.subjectRöntgentæknien_US
dc.subject.meshCardiovascular Diseasesen_US
dc.subject.meshTissuesen_US
dc.subject.meshTomography, X-Ray Computeden_US
dc.titleAssessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions.en_US
dc.typeArticleen_US
dc.identifier.eissn2045-2322
dc.contributor.department1Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland. 2Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy. 3Icelandic Heart Association, (Hjartavernd), Kópavogur, Iceland. 4Faculty of Medicine, University of Iceland, Reykjavík, Iceland. 5CIR-Myo, Department of Biomedical Sciences, University of, Padova, Italy. 6A&C M-C Foundation for Translational Myology, Padova, Italy. 7Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland. paologar@landspitali.is. 8Department of Science, Landspítali, Reykjavík, Iceland. paologar@landspitali.is.en_US
dc.identifier.journalScientific reportsen_US
dc.rights.accessOpen Access - Opinn aðganguren_US
dc.departmentcodeRES12
dc.source.journaltitleScientific reports
dc.source.volume10
dc.source.issue1
dc.source.beginpage2863
dc.source.endpage
refterms.dateFOA2020-09-08T14:14:27Z
dc.source.countryEngland


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