Risk Stratification in Cardiovascular Diseases
Submission Deadline: 30 Oct 2022
Guest Editors
Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong, China;Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China;Kent and Medway Medical School, Kent, UK
Interests: risk stratification; cardiac ion channelopathies; diabetes mellitus; electrocardiology; machine learning
Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong, China
Interests: risk stratification; cardiac ion channelopathies; diabetes mellitus; electrocardiology; machine learning
Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
Interests: atrial fibrillation; diabetes and atrial remodeling; cardio-oncology; syncope
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Special Issue in Atrial Fibrillation: From Bench-to-Bedside
Special Issue Information
Dear Colleagues,
Over the past decades, under the advancement of healthcare and the trend of the aging population, cardiovascular diseases have become an increasingly important cause of morbidity and mortality across the globe. Under the influence of different clinical, social and environmental factors, the prognosis of patients with the same disease can have significant variations. With an increasing number of patients suffering from multiple comorbidities, the interplay of diseases further complicates the prediction of disease outcomes. Therefore, there is a call for a more personalized management approach in the field of cardiovascular medicine, which requires the triage of patients by their risk profiles. Consequently, significant efforts have been exerted by the scientific community to advance research in the development of risk models and scores for the prognostication of cardiovascular diseases.
To improve the prognostic accuracy of the risk stratification models, multiparametric models with the inclusion of clinical, biochemical and imaging findings have been developed. New disease monitoring parameters derived from conventional biomarkers, electrocardiograms, echocardiograms and more were explored for their predictive value in disease progression. Existing diagnostic models were also examined for their risk stratifying potential. Machine learning approaches have been employed to uncover the non-linear relationships between variables, and account for the inter-predictor interactions to advance the predictive accuracy of models.
This special issue aims to collect high-quality original articles and comprehensive reviews on the advancements in risk stratification in the field of cardiovascular medicine. Topics of interest include but are not limited to:
1)Novel risk models on any cardiovascular diseases
2)Current trends in the risk stratification of any cardiovascular diseases
3)The use of machine-learning in the prognostication of cardiovascular diseases
Prof. Gary Tse, Dr. Sharen Lee and Prof. Tong Liu
Guest Editors
Keywords
- risk stratification
- cardiovascular
- machine learning
- prognosis
- diagnosis
Published Papers (8)
Association between Dental and Cardiovascular Diseases: A Systematic Review
Rev. Cardiovasc. Med. 2023, 24(6), 159; https://doi.org/10.31083/j.rcm2406159
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Using Machine Learning to Predict the In-Hospital Mortality in Women with ST-Segment Elevation Myocardial Infarction
Rev. Cardiovasc. Med. 2023, 24(5), 126; https://doi.org/10.31083/j.rcm2405126
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Risk Prediction Models for Ischemic Cardiovascular Outcomes in Patients with Acute Coronary Syndrome
Rev. Cardiovasc. Med. 2023, 24(4), 106; https://doi.org/10.31083/j.rcm2404106
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Sex Differences in Two International Guidelines for Assessing Obstructive Coronary Artery Disease in Symptomatic Outpatients by Coronary Computed Tomographic Angiography
Rev. Cardiovasc. Med. 2023, 24(4), 101; https://doi.org/10.31083/j.rcm2404101
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
A New Risk Score for Predicting Postoperative Mortality in Suspected Heart Failure Patients Undergoing Valvular Surgery
Rev. Cardiovasc. Med. 2023, 24(2), 38; https://doi.org/10.31083/j.rcm2402038
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study
Rev. Cardiovasc. Med. 2023, 24(2), 37; https://doi.org/10.31083/j.rcm2402037
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Revision of Clinical Pre-Test Probability Scores in Hospitalized Patients with Pulmonary Embolism and SARS-CoV-2 Infection
Rev. Cardiovasc. Med. 2023, 24(1), 18; https://doi.org/10.31083/j.rcm2401018
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)
Clinical Characteristics, Genetic Basis and Healthcare Resource Utilisation and Costs in Patients with Catecholaminergic Polymorphic Ventricular Tachycardia: A Retrospective Cohort Study
Rev. Cardiovasc. Med. 2022, 23(8), 276; https://doi.org/10.31083/j.rcm2308276
(This article belongs to the Special Issue Risk Stratification in Cardiovascular Diseases)