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Kigka VI, Georga E, Tsakanikas V, Kyriakidis S, Tsompou P, Siogkas P, Michalis LK, Naka KK, Neglia D, Rocchiccioli S, Pelosi G, Fotiadis DI, Sakellarios A. Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data. Diagnostics (Basel) 2022; 12:diagnostics12061466. [PMID: 35741275 PMCID: PMC9221964 DOI: 10.3390/diagnostics12061466] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/06/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022] Open
Abstract
The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model.
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Affiliation(s)
- Vassiliki I. Kigka
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Eleni Georga
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Vassilis Tsakanikas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Savvas Kyriakidis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Panagiota Tsompou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
| | - Panagiotis Siogkas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Lampros K. Michalis
- Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece; (L.K.M.); (K.K.N.)
| | - Katerina K. Naka
- Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece; (L.K.M.); (K.K.N.)
| | - Danilo Neglia
- Fondazione Toscana Gabriele Monasterio, IT 56126 Pisa, Italy;
| | - Silvia Rocchiccioli
- Institute of Clinical Physiology, National Research Council, IT 56124 Pisa, Italy; (S.R.); (G.P.)
| | - Gualtiero Pelosi
- Institute of Clinical Physiology, National Research Council, IT 56124 Pisa, Italy; (S.R.); (G.P.)
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
| | - Antonis Sakellarios
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; (V.I.K.); (E.G.); (V.T.); (S.K.); (P.T.); (P.S.); (D.I.F.)
- Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
- Correspondence: ; Tel.: +30-26510-07848
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Coronary Computer Tomography Angiography in 2021-Acquisition Protocols, Tips and Tricks and Heading beyond the Possible. Diagnostics (Basel) 2021; 11:diagnostics11061072. [PMID: 34200866 PMCID: PMC8230532 DOI: 10.3390/diagnostics11061072] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/06/2021] [Accepted: 06/09/2021] [Indexed: 01/09/2023] Open
Abstract
Recent technological advances, together with an increasing body of evidence from randomized trials, have placed coronary computer tomography angiography (CCTA) in the center of the diagnostic workup of patients with coronary artery disease. The method was proven reliable in the diagnosis of relevant coronary artery stenosis. Furthermore, it can identify different stages of the atherosclerotic process, including early atherosclerotic changes of the coronary vessel wall, a quality not met by other non-invasive tests. In addition, newer computational software can measure the hemodynamic relevance (fractional flow reserve) of a certain stenosis. In addition, if required, information related to cardiac and valvular function can be provided with specific protocols. Importantly, recent trials have highlighted the prognostic relevance of CCTA in patients with coronary artery disease, which helped establishing CCTA as the first-line method for the diagnostic work-up of such patients in current guidelines. All this can be gathered in one relatively fast examination with minimal discomfort for the patient and, with newer machines, with very low radiation exposure. Herein, we provide an overview of the current technical aspects, indications, pitfalls, and new horizons with CCTA, providing examples from our own clinical practice.
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Szabó GT, Üveges Á, Tar B, Ágoston A, Dorj A, Jenei C, Kolozsvári R, Csippa B, Czuriga D, Kőszegi Z. The Holistic Coronary Physiology Display: Calculation of the Flow Separation Index in Vessel-Specific Individual Flow Range during Fractional Flow Reserve Measurement Using 3D Coronary Reconstruction. J Clin Med 2021; 10:jcm10091910. [PMID: 33924961 PMCID: PMC8124623 DOI: 10.3390/jcm10091910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 12/25/2022] Open
Abstract
In order to make optimal decisions on the treatment of atherosclerotic coronary heart disease (CHD), appropriate evaluation is necessary, including both the anatomical and physiological assessment of the coronary arteries. According to current guidelines, a fractional flow reserve (FFR)-based clinical decision is recommended, but coronary flow reserve (CFR) measurements and microvascular evaluation should also be considered in special cases for a detailed exploration of the coronary disease state. We aimed to generate an extended physiological evaluation during routine FFR measurement and define a new pathological flow-related prognostic factor. Fluid dynamic equations were applied to calculate CFR on the basis of the three-dimensional (3D) reconstruction of the invasively acquired coronary angiogram and the measured intracoronary pressure data. A new, potentially robust prognostic parameter of a coronary lesion called the "flow separation index" (FSi), which is thought to detect the pathological flow amount through a stenosis was introduced in a vessel-specific flow range. Correlations between FSi and the clinically established physiological indices (CFR and FFR) were determined. The FSi was calculated in 19 vessels of 16 patients, including data from the pre- and post-stent revascularization treatment of 3 patients. There was no significant correlation between the FSi and the CFR (r = -0.23, p = 0.34); however, there was significant negative correlation between the FSi and the FFR (r = -0.66, p = 0.002). An even stronger correlation was found between the FSi and the ratio of the resting pressure ratio and the FFR (r = 0.92, p < 0.0001). The diagnostic power of the FSi for predicting the FFR value of <0.80, as a gold standard prognostic factor, was tested by receiver operating characteristic analysis. FSi > 0.022 proved to be the cutoff value of the prediction of a pathologically low FFR with a 0.856 area under the curve (95% confidence interval: 0.620 to 0.972). The present flow-pressure-velocity display provides a comprehensive summary of patient-specific pathophysiology in CHD. The consequences of epicardial stenoses can be evaluated together with their complex relations to microvascular conditions. Based on these values, clinical decision-making concerning both pharmacological therapy and percutaneous or surgical revascularization may be more precisely guided.
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Affiliation(s)
- Gábor Tamás Szabó
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
| | - Áron Üveges
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
- Szabolcs–Szatmár–Bereg County Hospitals and University Teaching Hospital, 4400 Nyíregyháza, Hungary
| | - Balázs Tar
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
- Szabolcs–Szatmár–Bereg County Hospitals and University Teaching Hospital, 4400 Nyíregyháza, Hungary
| | - András Ágoston
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
- Szabolcs–Szatmár–Bereg County Hospitals and University Teaching Hospital, 4400 Nyíregyháza, Hungary
| | - Azzaya Dorj
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
| | - Csaba Jenei
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
| | - Rudolf Kolozsvári
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
| | - Benjamin Csippa
- Department of Hydrodynamic Systems, Budapest University of Technology and Economics, 1111 Budapest, Hungary;
| | - Dániel Czuriga
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
| | - Zsolt Kőszegi
- Division of Cardiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, 4032 Debrecen, Hungary; (G.T.S.); (A.D.); (C.J.); (R.K.); (D.C.)
- Kálmán Laki Doctoral School of Biomedical and Clinical Sciences, University of Debrecen, 4032 Debrecen, Hungary; (Á.Ü.); (B.T.); (A.Á.)
- Szabolcs–Szatmár–Bereg County Hospitals and University Teaching Hospital, 4400 Nyíregyháza, Hungary
- Correspondence: ; Tel./Fax: +36-52-255928
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