Advancements in Artificial Intelligence and Data Science for Cardiovascular Health

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "TeleHealth and Digital Healthcare".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 9088

Special Issue Editor


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Guest Editor
Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, Uxbridge UB8 3PH, UK
Interests: (genetic) epidemiology of cardiovascular diseases; big data; genome-wide association studies; genetic risk scores; mendelian randomization; machine learning

Special Issue Information

Dear Colleagues,

Cardiovascular disease is the highest cause of mortality worldwide. Recent advances in molecular epidemiological methods and techniques have remarkably enhanced cardiovascular health. Techniques such as genomics, metabolomics, proteomics, and transcriptomics have generated a large amount of data.

In addition, novel analytical approaches in genomics, such as genome-wide association analysis, genetic correlation, and gene-set enrichment analysis, have emerged in the last decade, improving our understanding of the biological pathways involved in cardiovascular diseases. Methods such as Mendelian randomization analysis and polygenic risk scores have improved our insight into causal factors. Machine learning approaches are increasingly being applied in the field and provide a better understanding of various predictors of cardiovascular diseases. In this issue, we are seeking to obtain insight into the advances that these methods and techniques have brought into the field.

The topics of interest include, but are not limited to, the following:

  • Accelerating patient benefit in cardiology using artificial intelligence;
  • Artificial intelligence for public health;
  • Cardiac image processing;
  • Data mining in cardiology;
  • Decision support systems in cardiovascular health;
  • Digital cardiology;
  • Machine learning and deep learning methods in cardiovascular health;
  • Machine learning in drug development in cardiology;
  • Machine learning to handle cardiology hospital records;
  • Personalised cardiology using machine learning;
  • The prediction of cardiovascular risk (hypertension, myocardial infarction, atherosclerosis, stroke, etc.).

Dr. Raha Pazoki
Guest Editor


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Keywords

  • molecular epidemiology
  • machine learning
  • cardiovascular disease
  • data science

Published Papers (3 papers)

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Research

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13 pages, 2180 KiB  
Article
Urinary Sodium Excretion Enhances the Effect of Alcohol on Blood Pressure
by Xiyun Jiang, Mila D. Anasanti, Fotios Drenos, Alexandra I. Blakemore and Raha Pazoki
Healthcare 2022, 10(7), 1296; https://doi.org/10.3390/healthcare10071296 - 13 Jul 2022
Cited by 2 | Viewed by 2769
Abstract
Alcohol consumption is linked to urinary sodium excretion and both of these traits are linked to hypertension and cardiovascular diseases (CVDs). The interplay between alcohol consumption and sodium on hypertension, and cardiovascular diseases (CVDs) is not well-described. Here, we used genetically predicted alcohol [...] Read more.
Alcohol consumption is linked to urinary sodium excretion and both of these traits are linked to hypertension and cardiovascular diseases (CVDs). The interplay between alcohol consumption and sodium on hypertension, and cardiovascular diseases (CVDs) is not well-described. Here, we used genetically predicted alcohol consumption and explored the relationships between alcohol consumption, urinary sodium, hypertension, and CVDs. Methods: We performed a comparative analysis among 295,189 participants from the prospective cohort of the UK Biobank (baseline data collected between 2006 and 2010). We created a genetic risk score (GRS) using 105 published genetic variants in Europeans that were associated with alcohol consumption. We explored the relationships between GRS, alcohol consumption, urinary sodium, blood pressure traits, and incident CVD. We used linear and logistic regression and Cox proportional hazards (PH) models and Mendelian randomization in our analysis. Results: The median follow-up time for composite CVD and stroke were 6.1 years and 7.1 years respectively. Our analyses showed that high alcohol consumption is linked to low urinary sodium excretion. Our results showed that high alcohol GRS was associated with high blood pressure and higher risk of stroke and supported an interaction effect between alcohol GRS and urinary sodium on stage 2 hypertension (Pinteraction = 0.03) and CVD (Pinteraction = 0.03), i.e., in the presence of high urinary sodium excretion, the effect of alcohol GRS on blood pressure may be enhanced. Conclusions: Our results show that urinary sodium excretion may offset the risk posed by genetic risk of alcohol consumption. Full article
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12 pages, 1374 KiB  
Article
The Impact of Age on Statin-Related Glycemia: A Propensity Score-Matched Cohort Study in Korea
by Shaopeng Xu, Seung-Woon Rha, Byoung Geol Choi and Hong Seog Seo
Healthcare 2022, 10(5), 777; https://doi.org/10.3390/healthcare10050777 - 22 Apr 2022
Viewed by 1360
Abstract
The aim of this study was to investigate the influence of statin on glycemic control in different age groups. Patients admitted for suspected or confirmed coronary artery disease between January 2005 and December 2013 in Seoul, Korea were initially enrolled. After propensity score [...] Read more.
The aim of this study was to investigate the influence of statin on glycemic control in different age groups. Patients admitted for suspected or confirmed coronary artery disease between January 2005 and December 2013 in Seoul, Korea were initially enrolled. After propensity score matching, 2654 patients (1:1 statin users and non-users) were selected out of total 5041 patients, including 1477 “young” patients (≤60 y) and 1177 elderly patients (>60 y). HbA1c was decreased by 0.04% (±0.86%) in statin non-users. On the contrary, a slight increment of 0.05% (±0.71%) was found in statin users (p < 0.001). The change patterns of HbA1c were constant in both young and elderly patient groups. Furthermore, elderly statin users demonstrated significantly worse glycemic control in serum insulin and homeostatic model assessment—insulin resistance (HOMA-IR) index. In elderly patients, statin users were found to have a 2.61 ± 8.34 μU/mL increment in serum insulin, whereas it was 2.35 ± 6.72 μU/mL for non-users (p = 0.012). Statin users had a 0.78 ± 3.28 increment in HOMA-IR, in contrast to the 0.67 ± 2.51 increment in statin non-users (p = 0.008). In conclusion, statin treatment was associated with adverse glycemic control in the elderly population. Full article
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Review

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19 pages, 975 KiB  
Review
The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature
by Dhir Gala, Haditya Behl, Mili Shah and Amgad N. Makaryus
Healthcare 2024, 12(4), 481; https://doi.org/10.3390/healthcare12040481 - 16 Feb 2024
Cited by 4 | Viewed by 3661
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding [...] Read more.
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes. Full article
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