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Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of s...

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Autores principales: Panteris, Eleftherios, Deda, Olga, Papazoglou, Andreas S., Karagiannidis, Efstratios, Liapikos, Theodoros, Begou, Olga, Meikopoulos, Thomas, Mouskeftara, Thomai, Sofidis, Georgios, Sianos, Georgios, Theodoridis, Georgios, Gika, Helen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504538/
https://www.ncbi.nlm.nih.gov/pubmed/36144220
http://dx.doi.org/10.3390/metabo12090816
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author Panteris, Eleftherios
Deda, Olga
Papazoglou, Andreas S.
Karagiannidis, Efstratios
Liapikos, Theodoros
Begou, Olga
Meikopoulos, Thomas
Mouskeftara, Thomai
Sofidis, Georgios
Sianos, Georgios
Theodoridis, Georgios
Gika, Helen
author_facet Panteris, Eleftherios
Deda, Olga
Papazoglou, Andreas S.
Karagiannidis, Efstratios
Liapikos, Theodoros
Begou, Olga
Meikopoulos, Thomas
Mouskeftara, Thomai
Sofidis, Georgios
Sianos, Georgios
Theodoridis, Georgios
Gika, Helen
author_sort Panteris, Eleftherios
collection PubMed
description Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691–0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.
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spelling pubmed-95045382022-09-24 Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial Panteris, Eleftherios Deda, Olga Papazoglou, Andreas S. Karagiannidis, Efstratios Liapikos, Theodoros Begou, Olga Meikopoulos, Thomas Mouskeftara, Thomai Sofidis, Georgios Sianos, Georgios Theodoridis, Georgios Gika, Helen Metabolites Article Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691–0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD. MDPI 2022-08-30 /pmc/articles/PMC9504538/ /pubmed/36144220 http://dx.doi.org/10.3390/metabo12090816 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Panteris, Eleftherios
Deda, Olga
Papazoglou, Andreas S.
Karagiannidis, Efstratios
Liapikos, Theodoros
Begou, Olga
Meikopoulos, Thomas
Mouskeftara, Thomai
Sofidis, Georgios
Sianos, Georgios
Theodoridis, Georgios
Gika, Helen
Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title_full Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title_fullStr Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title_full_unstemmed Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title_short Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
title_sort machine learning algorithm to predict obstructive coronary artery disease: insights from the corlipid trial
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9504538/
https://www.ncbi.nlm.nih.gov/pubmed/36144220
http://dx.doi.org/10.3390/metabo12090816
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