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Ricciardi C, Edmunds KJ, Recenti M, Sigurdsson S, Gudnason V, Carraro U, Gargiulo P. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep 2020; 10:2863. [PMID: 32071412 PMCID: PMC7029006 DOI: 10.1038/s41598-020-59873-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 02/04/2020] [Indexed: 11/24/2022] Open
Abstract
The 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.
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Affiliation(s)
- Carlo Ricciardi
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.,Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy
| | - Kyle J Edmunds
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland
| | - Marco Recenti
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, (Hjartavernd), Kópavogur, Iceland.,Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Ugo Carraro
- CIR-Myo, Department of Biomedical Sciences, University of, Padova, Italy.,A&C M-C Foundation for Translational Myology, Padova, Italy
| | - Paolo Gargiulo
- Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland. .,Department of Science, Landspítali, Reykjavík, Iceland.
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