ArticleMetabolites2022
Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04580173 (Correlation of Clinical Types and Complexity of Coronary Artery Disease With Patients' Metabolic Profile), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Correlation of Clinical Types and Complexity of Coronary Artery Disease With Patients' Metabolic Profile
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Construction of classification model and analysis of risk factors in patients with multi-vessel coronary artery disease.BMC medical informatics and decision making · 2026Article
- Review
- Article
- Ceramide in Coronary Artery Disease: Troublesome or Helpful Future Tools in the Assessment of Risk Prediction and Therapy Effectiveness?Metabolites · 2025Review
- Multi-Omics Research on Angina Pectoris: A Novel Perspective.Aging and disease · 2024Review
- Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.European heart journal. Digital health · 2024Article
- Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease.Journal of proteome research · 2024Article
- Linking Diabetic Retinopathy Severity to Coronary Artery Disease Risk Factors in Type 2 Diabetic Patients.Cureus · 2024Article
- Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population.The Journal of international medical research · 2023Article
- Review
- Article
- Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography.Journal of clinical medicine · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.