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Diagnostics
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New Advances in Cardiovascular Risk Prediction
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New Advances in Cardiovascular Risk Prediction

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1296

Special Issue Editor

Dr. Ming-Shyan Lin

E-Mail Website
Guest Editor
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
Interests: cardiovascular diseases; biomarker; cohort study

Special Issue Information

Dear Colleagues,

New advances in cardiovascular risk prediction enable the early identification of high-risk individuals and the prevention of cardiac events. These advancements include the use of biomarkers, genetic tests, and imaging technologies. Furthermore, machine learning algorithms are being used to improve the prediction of cardiovascular risks. Machine learning has revolutionized healthcare by enabling more accurate and personalized disease risk assessments. It allows the analysis of vast amounts of data to identify patterns that might not be apparent to human analysts. This has enabled doctors to tailor better treatments and preventative measures for individuals based on their risk profile. Moreover, it has enabled healthcare providers to identify individuals at risk of developing certain diseases, allowing them to intervene and provide early interventions.

Dr. Ming-Shyan Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cardiovascular diseases
  • risk prediction
  • biomarker
  • machine learning

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Published Papers (2 papers)

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Research

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11 pages, 232 KiB  
Article
Retinal Microvascular Characteristics—Novel Risk Stratification in Cardiovascular Diseases
by Alexandra Cristina Rusu, Klara Brînzaniuc, Grigore Tinica, Clément Germanese, Simona Irina Damian, Sofia Mihaela David and Raluca Ozana Chistol
Diagnostics 2025, 15(9), 1073; https://doi.org/10.3390/diagnostics15091073 - 23 Apr 2025
Viewed by 231
Abstract
Background: Cardiovascular diseases (CVDs) are responsible for 32.4% of all deaths across the European Union (EU), and several CVD risk scores have been developed, with variable results. Retinal microvascular changes have been proposed as potential biomarkers for cardiovascular risk, especially in coronary heart [...] Read more.
Background: Cardiovascular diseases (CVDs) are responsible for 32.4% of all deaths across the European Union (EU), and several CVD risk scores have been developed, with variable results. Retinal microvascular changes have been proposed as potential biomarkers for cardiovascular risk, especially in coronary heart diseases (CHDs). This study aims to identify the retinal microvascular features associated with CHDs and evaluate their potential use in a CHD screening algorithm in conjunction with traditional risk factors. Methods: We performed a two-center cross-sectional study on 120 adult participants—36 patients previously diagnosed with severe CHDs and scheduled for coronary artery bypass graft surgery (CHD group) and 84 healthy controls. A brief medical history and a clinical profile were available for all cases. All patients benefited from optical coherence tomography angiography (OCTA), the use of which allowed several parameters to be quantified for the foveal avascular zone and superficial and deep capillary plexuses. We evaluated the precision of several classification models in identifying patients with CHDs based on traditional risk factors and OCTA characteristics: a conventional logistic regression model and four machine learning algorithms: k-Nearest Neighbors (k-NN), Naive Bayes, Support Vector Machine (SVM) and supervised logistic regression. Results: Conventional multiple logistic regression had a classification accuracy of 78.7% based on traditional risk factors and retinal microvascular features, while machine learning algorithms had higher accuracies: 81% for K-NN and supervised logistic regression, 85.71% for Naive Bayes and 86% for SVM. Conclusions: Novel risk scores developed using machine learning algorithms and based on traditional risk factors and retinal microvascular characteristics could improve the identification of patients with CHDs. Full article
(This article belongs to the Special Issue New Advances in Cardiovascular Risk Prediction)

Review

Jump to: Research

38 pages, 18640 KiB  
Review
Water Hammer Phenomenon in Coronary Arteries: Scientific Basis for Diagnostic and Predictive Modeling with Acoustic Action Mapping
by Khiem D. Ngo, Thach Nguyen, Huan Dat Pham, Hadrian Tran, Dat Q. Ha, Truong S. Dinh, Imran Mihas, Mihas Kodenchery, C. Michael Gibson, Hien Q. Nguyen, Thang Nguyen, Vu T. Loc, Chinh D. Nguyen, Hoang Anh Tien, Ernest Talarico, Jr., Marco Zuin, Gianluca Rigatelli, Aravinda Nanjundappa, Quynh T. N. Nguyen and The-Hung Nguyen
Diagnostics 2025, 15(5), 553; https://doi.org/10.3390/diagnostics15050553 - 25 Feb 2025
Viewed by 862
Abstract
Background: In the study of coronary artery disease, the mechanisms underlying atherosclerosis initiation and progression or regression remain incompletely understood. Our research conceptualized the cardiovascular system as an integrated network of pumps and pipes, advocating for a paradigm shift from static imaging of [...] Read more.
Background: In the study of coronary artery disease, the mechanisms underlying atherosclerosis initiation and progression or regression remain incompletely understood. Our research conceptualized the cardiovascular system as an integrated network of pumps and pipes, advocating for a paradigm shift from static imaging of coronary stenosis to dynamic assessments of coronary flow. Further review of fluid mechanics highlighted the water hammer phenomenon as a compelling analog for processes in coronary arteries. Methods: In this review, the analytical methodology employed a comprehensive, multifaceted approach that incorporated a review of fluid mechanics principles, in vitro acoustic experimentation, frame-by-frame visual angiographic assessments of in vivo coronary flow, and an artificial intelligence (AI) protocol designed to analyze the water hammer phenomenon within an acoustic framework. In the analysis of coronary flow, the angiograms were selected from patients with unstable angina if they had previously undergone one or more coronary angiograms, allowing for a longitudinal comparison of dynamic flow and phenomena. Results: The acoustic investigations pinpointed pockets of contrast concentrations, which might correspond to compression and rarefaction zones. Compression antinodes were correlated to severe stenosis, due to rapid shifts from low-pressure diastolic flow to high-pressure systolic surges, resulting in intimal injury. Rarefaction antinodes were correlated with milder lesions, due to de-escalating transitions from high systolic pressure to lower diastolic pressure. The areas of nodes remained without lesions. Based on the locations of antinodes and nodes, a coronary acoustic action map was constructed, enabling the identification of existing lesions, forecasting the progression of current lesions, and predicting the development of future lesions. Conclusions: The results suggested that intimal injury was likely induced by acoustic retrograde pressure waves from the water hammer phenomenon and developed new lesions at specifically exact locations. Full article
(This article belongs to the Special Issue New Advances in Cardiovascular Risk Prediction)
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