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

Article Biochemical Research Methods

A HILIC-MS/MS method development and validation for the quantitation of 13 acylcarnitines in human serum

Thomas Meikopoulos et al.

Summary: In this study, a rapid quantitation method for acylcarnitines in human serum was developed and validated. The method was successfully applied in the analysis of samples from patients with coronary artery disease.

ANALYTICAL AND BIOANALYTICAL CHEMISTRY (2022)

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Article Biochemical Research Methods

Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance

T. Liapikos et al.

Summary: This study developed QSRR models based on machine learning for the prediction of retention time in metabolomics. Different regression algorithms were tested and compared using experimental and publicly available chromatographic data. The results showed that the selected regression algorithms were able to effectively handle collinearity and there was no significant difference in the performance of the predictive models among different dataset configurations.

JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES (2022)

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Review Pharmacology & Pharmacy

CERT2 ceramide- and phospholipid-based risk score and major adverse cardiovascular events: A systematic review and meta-analysis

Andreas S. Papazoglou et al.

Summary: This study found that the risk score based on ceramides and phosphatidylcholine (CERT2) appears to be a robust predictor of major adverse cardiovascular events (MACE). The research also suggests that further studies are needed to establish the cost-effectiveness of calculating the CERT2 score for determining residual risk in patients with coronary artery disease.

JOURNAL OF CLINICAL LIPIDOLOGY (2022)

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Article Cardiac & Cardiovascular Systems

Non-invasive imaging as the cornerstone of cardiovascular precision medicine

Stephan Achenbach et al.

Summary: Cardiovascular imaging plays a crucial role in precision medicine by aiding in the diagnosis, management, and individualized treatment of cardiovascular diseases.

EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING (2022)

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Article Cardiac & Cardiovascular Systems

A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial

Nikolaos Mittas et al.

Summary: The study developed a data-driven framework using heterogeneous electronic medical and clinical records and advanced Machine Learning approaches to identify risk factors affecting coronary artery disease complexity and predict the expected SYNTAX score. The approach showed promising results in predicting and estimating the SYNTAX score.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2022)

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Article Cardiac & Cardiovascular Systems

Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease

Efstratios Karagiannidis et al.

Summary: Diabetes mellitus (DM) and coronary artery disease (CAD) are interrelated clinical entities. There is a need for novel biomarkers and metabolomics-based prediction models to predict clinical outcomes and assess CAD complexity in the comorbid population of DM and CAD.

CARDIOVASCULAR DIABETOLOGY (2022)

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Article Biochemistry & Molecular Biology

Correlation of Serum Acylcarnitines with Clinical Presentation and Severity of Coronary Artery Disease

Olga Deda et al.

Summary: Recent studies suggest that acylcarnitines play a significant role in the development and progression of cardiovascular disease. This metabolomics-based study investigated the association between serum acylcarnitine levels and the severity of coronary artery disease (CAD), as measured by SYNTAX Score. The results identified potential biomarkers and metabolites that may be useful for predicting CAD severity and individualizing patient management.

BIOMOLECULES (2022)

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Article Cardiac & Cardiovascular Systems

A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms

J. D. Schwalm et al.

Summary: Machine learning can improve the diagnostic yield of invasive coronary angiography in stable outpatients by predicting obstructive coronary artery disease, thereby reducing patient risk and healthcare costs.

CARDIOVASCULAR DIGITAL HEALTH JOURNAL (2022)

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Review Biochemical Research Methods

Deep learning meets metabolomics: a methodological perspective

Partho Sen et al.

Summary: The study discusses the application of deep learning in metabolomics, focusing on its role in addressing bottlenecks in data acquisition, processing, metabolite identification, and biomarker discovery. It also examines how deep learning is used in genome-scale metabolic modeling and interpreting metabolomics data, emphasizing its potential to assist computational biologists in integrating, predicting, and drawing statistical inference about biological outcomes based on metabolomics data.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Cardiac & Cardiovascular Systems

2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation

Jean-Philippe Collet et al.

EUROPEAN HEART JOURNAL (2021)

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Article Medicine, Research & Experimental

Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution

Aravind Akella et al.

Summary: The study successfully predicted the occurrence of coronary artery disease (CAD) using machine learning algorithms, with all six algorithms achieving accuracies greater than 80% and the neural network algorithm achieving an accuracy greater than 93%. Providing open source code may help improve the use of machine learning algorithms as a diagnostic tool for CAD.

FUTURE SCIENCE OA (2021)

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Article Cardiac & Cardiovascular Systems

Correlation of the severity of coronary artery disease with patients' metabolic profile- rationale, design and baseline patient characteristics of the CorLipid trial

Efstratios Karagiannidis et al.

Summary: This study aims to evaluate the diagnostic utility of plasma metabolomics-based biomarkers for determining the complexity and severity of CAD, as it is assessed via the SYNTAX score. The CorLipid trial is an ongoing trial investigating the correlation between metabolic profile and complexity of coronary artery disease. It aims to create an integrative metabolomic biomarkers-based algorithm for identifying high-risk patients with CAD.

BMC CARDIOVASCULAR DISORDERS (2021)

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Review Cardiac & Cardiovascular Systems

Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings

Aikaterini Iliou et al.

Summary: Metabolomics is a promising tool for better understanding pathways underlying cardiovascular disease and improving risk stratification. Epidemiological studies have linked metabolites to coronary heart disease and stroke, revealing the systemic nature of CVD and associated metabolic pathways. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways in CVD.

HEART (2021)

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Article Chemistry, Multidisciplinary

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study

Song Cui et al.

Summary: The study identified metabolic signatures associated with future risk of angina recurrence after PCI, allowing for the prediction of high-risk patients during remission. A multi-metabolite predictive model constructed from these signatures showed high accuracy, sensitivity, and specificity across independent cohorts, providing a potential tool for early intervention and treatment.

ADVANCED SCIENCE (2021)

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Article Biochemistry & Molecular Biology

A Study of Blood Fatty Acids Profile in Hyperlipidemic and Normolipidemic Subjects in Association with Common PNPLA3 and ABCB1 Polymorphisms

Thomai Mouskeftara et al.

Summary: Adiponutrin and P-glycoprotein are membrane proteins encoded by the PNPLA3 and ABCB1 genes, respectively, playing important roles in lipid metabolism and fatty liver disease. Common polymorphisms in these genes, particularly the rs738409 polymorphism in PNPLA3, can affect the distribution of blood fatty acids. This study optimized a method for analyzing fatty acids and found that the rs738409 polymorphism significantly influences the distribution of blood fatty acids.

METABOLITES (2021)

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Article Health Care Sciences & Services

Serum Ceramides as Prognostic Biomarkers of Large Thrombus Burden in Patients with STEMI: A Micro-Computed Tomography Study

Efstratios Karagiannidis et al.

Summary: The study found that serum ceramide levels were associated with intracoronary thrombus burden and could predict risk stratification in patients with STEMI, suggesting potential for personalized care.

JOURNAL OF PERSONALIZED MEDICINE (2021)

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Article Cardiac & Cardiovascular Systems

Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury

Hao Cui et al.

Summary: Metabolomics-based biomarkers in plasma samples within 24 hours post cardiac surgery can distinguish patients with acute kidney injury (AKI) from controls. Specific metabolites such as gluconic acid, fumaric acid, and pseudouridine were upregulated in AKI patients. A predictive model utilizing machine learning algorithms and clinical parameters showed excellent discriminative ability for identifying CSA-AKI.

JOURNAL OF THE AMERICAN HEART ASSOCIATION (2021)

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Review Respiratory System

Narrative review of metabolomics in cardiovascular disease

Julian Mueller et al.

Summary: Cardiovascular diseases are often accompanied by disorders in cardiac metabolism, and emerging metabolomics technology allows for the measurement of hundreds of metabolites to characterize each patient by their own metabolic fingerprint, significantly altering the management of cardiovascular diseases and possibly personalizing medicine.

JOURNAL OF THORACIC DISEASE (2021)

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Article Medicine, Research & Experimental

Purine metabolite-based machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease

Sunhee Jung et al.

Summary: This study highlights the potential of purine-related blood metabolites for developing machine learning models for predicting the risk and diagnosing coronary artery disease (CAD), emphasizing the distinct roles of xanthine and uric acid in differentiating between primary/secondary prevention or diagnosis of CAD.

BIOMEDICINE & PHARMACOTHERAPY (2021)

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Article Cardiac & Cardiovascular Systems

The GEnetic Syntax Score: a genetic risk assessment implementation tool grading the complexity of coronary artery disease-rationale and design of the GESS study

Ioannis S. Vizirianakis et al.

Summary: The study aims to develop a personalized risk prediction algorithm (GEnetic Syntax Score-GESS) for predicting cardiovascular risk and guiding therapeutic strategies by combining genetic markers with clinical and angiographic information. Next generation sequencing (NGS) technology will be used to genotype specific single-nucleotide polymorphisms (SNPs) relevant to CAD, with enrichment analyses performed to identify significant terms and pathways. The study will also calculate SYNTAX score for CAD severity assessment and follow up patients for major adverse cardiovascular events.

BMC CARDIOVASCULAR DISORDERS (2021)

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Article Biochemical Research Methods

Development and validation of a RPLC-MS/MS method for the quantification of ceramides in human serum

Olga Angeliki Begou et al.

Summary: Ceramides play a crucial role in cellular processes and their levels in blood circulation are associated with various disease states. A rapid quantification method for ceramides in human serum was developed and validated, showing accuracy and reliability for clinical analyses.

JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES (2021)

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Article Cardiac & Cardiovascular Systems

Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease

Hong Yan Qiao et al.

Summary: The study aimed to investigate whether a machine learning-based CT-FFR-derived FSSCTA could predict clinical outcomes in patients with three-vessel CAD. The results showed that FSSCTA had better predictive accuracy for MACE compared to SSCTA and SSICA, and could potentially change revascularization strategies in these patients.

EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING (2021)

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Article Cardiac & Cardiovascular Systems

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Subhi J. Al'Arefilb et al.

EUROPEAN HEART JOURNAL (2020)

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Article Biochemical Research Methods

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning

Alena Orlenko et al.

BIOINFORMATICS (2020)

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Article Medicine, Research & Experimental

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease

Annelise M. Poss et al.

JOURNAL OF CLINICAL INVESTIGATION (2020)

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Article Cardiac & Cardiovascular Systems

Prediction of Residual Risk by Ceramide-Phospholipid Score in Patients With Stable Coronary Heart Disease on Optimal Medical Therapy

Mika Hilvo et al.

JOURNAL OF THE AMERICAN HEART ASSOCIATION (2020)

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Review Biochemistry & Molecular Biology

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Ulf W. Liebal et al.

METABOLITES (2020)

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Article Multidisciplinary Sciences

Machine learning prediction in cardiovascular diseases: a meta-analysis

Chayakrit Krittanawong et al.

SCIENTIFIC REPORTS (2020)

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Article Cardiac & Cardiovascular Systems

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Subhi J. Al'Aref et al.

EUROPEAN HEART JOURNAL (2020)

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Review Biochemistry & Molecular Biology

Deep metabolome: Applications of deep learning in metabolomics

Yotsawat Pomyen et al.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2020)

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Review Cardiac & Cardiovascular Systems

Deep learning for cardiovascular medicine: a practical primer

Chayakrit Krittanawong et al.

EUROPEAN HEART JOURNAL (2019)

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Article Cardiac & Cardiovascular Systems

Mortality From Ischemic Heart Disease Analysis of Data From the World Health Organization and Coronary Artery Disease Risk Factors From NCD Risk Factor Collaboration

Alexandra N. Nowbar et al.

CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES (2019)

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Article Cardiac & Cardiovascular Systems

Association of Circulating Metabolites With Risk of Coronary Heart Disease in a European Population Results From the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium

Ersin Cavus et al.

JAMA CARDIOLOGY (2019)

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Article Medicine, General & Internal

NMR-based metabolomics identifies patients at high risk of death within two years after acute myocardial infarction in the AMI-Florence II cohort

Alessia Vignoli et al.

BMC MEDICINE (2019)

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Review Cardiac & Cardiovascular Systems

Relevance of Multi-Omics Studies in Cardiovascular Diseases

Paola Leon-Mimila et al.

FRONTIERS IN CARDIOVASCULAR MEDICINE (2019)

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Review Cardiac & Cardiovascular Systems

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

Benjamin A. Goldstein et al.

EUROPEAN HEART JOURNAL (2017)

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Review Cardiac & Cardiovascular Systems

Artificial Intelligence in Precision Cardiovascular Medicine

Chayakrit Krittanawong et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2017)

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Article Mathematical & Computational Biology

Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events

Menelaos Pavlou et al.

STATISTICS IN MEDICINE (2016)

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Article Biochemical Research Methods

Integration of metabolomics, lipidomics and clinical data using a machine learning method

Animesh Acharjee et al.

BMC BIOINFORMATICS (2016)

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Article Biochemical Research Methods

Targeted analysis of omega-6-derived eicosanoids in human serum by SPE-LC-MS/MS for evaluation of coronary artery disease

Maria Auxiliadora Fernandez Peralbo et al.

ELECTROPHORESIS (2013)

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Review Cardiac & Cardiovascular Systems

Metabolomics as a tool for cardiac research

Julian L. Griffin et al.

NATURE REVIEWS CARDIOLOGY (2011)

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Article Cardiac & Cardiovascular Systems

Validation of the Global Registry of Acute Coronary Event (GRACE) risk score for in-hospital mortality in patients with acute coronary syndrome in Canada

Basem Elbarouni et al.

AMERICAN HEART JOURNAL (2009)

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Review Cardiac & Cardiovascular Systems

Beyond established and novel risk factors - Lifestyle risk factors for cardiovascular disease

Dariush Mozaffarian et al.

CIRCULATION (2008)

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

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