ReviewCardiology research2026
Prediction of Risk of Cardiovascular Events in Patients With Stable Angina Using Artificial Intelligence: A Systematic Review.
Review in Cardiology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stable angina pectoris is a prevalent condition with concerning symptoms and an increased risk of myocardial infarction (MI), stroke, heart failure, and mortality. Risk stratification is important in preventive care because the severity of the condition significantly impacts prognosis. With the availability of digital data such as electrocardiogram (ECG) and clinical variables, artificial intelligence (AI) approaches, including machine learning and deep learning, can be beneficial for risk prediction of chronic diseases, such as coronary artery disease (CAD), in patients with stable angina. At the same time, traditional approaches such as Diamond-Forrester, Framingham Risk Score, and PROCAM lag because they rely on linear and population-oriented assumptions. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, evaluates AI models that have been developed to predict cardiovascular events in adults suffering from a stable angina condition. Out of 1,250 articles, only five research studies have been included because they predict the risk of cardiovascular diseases in patients suffering from stable angina. Data modalities in these studies include longitudinal electronic health record (EHR) variables, patient-reported outcomes (Seattle Angina Questionnaire), invasive angiography indices (e.g., Gensini score), and 12-lead ECG. With area under the curve (AUC) varying from moderate (about 0.78-0.83) to excellent (> 0.95) based on outcome and data richness, AI enhances discrimination for obstructive CAD and adverse outcomes across studies. This review highlights that there are a limited number of studies on this topic and a lack of clinical validity. Also, the final set of features in model development varies highly. Hence, heterogeneity, a lack of external validation, and implementation limitations exist.
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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.