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Personalized Medicine for Cardiovascular Disease
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Personalized Medicine for Cardiovascular Disease

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Mechanisms of Diseases".

Deadline for manuscript submissions: closed (20 December 2020) | Viewed by 22188

Special Issue Editor

Dr. Hyung Joon Joo

E-Mail Website
Guest Editor
Department of Cardiology, Cardiovascular Center, College of Medicine, Korea University, Seoul 02841, Republic of Korea
Interests: big data; artificial intelligence; medical informatics; common data model; clinical decision support

Special Issue Information

Dear Colleagues,

Rapid progress in healthcare IT provides new chances to expand our data collection and its analysis. Through big data analytics and artificial intelligence (AI), researchers can build novel personalized and actionable applications for patients with cardiovascular disease.

At present, major sources for big data include administrative databases, clinical registries, and electronic health records. Recently, biometric data, patient-reported data, internet-driven data and multi-omics data have grown rapidly. Taken together, a movement toward more comprehensive and integrated analytics is emerging.

In this Special Issue, we aim to focus on practical works or products related to (1) the building of big data infrastructures (data sources), and (2) the development and implementation of personalized algorithms and applications (analytics and application) in the cardiovascular field. This includes administrative databases, clinical registries, and electronic health records, as well as a broad range of -omics data such as genomics, proteomics, and metabolomics.

As such, we invite innovative, reproducible, and open research, and practical research that can ultimately help patients. We are open to original articles, meta-analyses, reviews, and policy proposals, as long as they fit the scope of the Special Issue.

Dr. Hyung Joon Joo
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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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

  • precision medicine
  • big data
  • artificial intelligence
  • cardiovascular disease

Published Papers (2 papers)

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Research

11 pages, 1406 KiB  
Article
Genomic Screening Identifies Individuals at High Risk for Hereditary Transthyretin Amyloidosis
by Emily R. Soper, Sabrina A. Suckiel, Giovanna T. Braganza, Amy R. Kontorovich, Eimear E. Kenny and Noura S. Abul-Husn
J. Pers. Med. 2021, 11(1), 49; https://doi.org/10.3390/jpm11010049 - 15 Jan 2021
Cited by 10 | Viewed by 17363
Abstract
The TTR V142I variant associated with hereditary transthyretin amyloidosis (hATTR) is present in up to 4% of African American (AA) and 1% of Hispanic/Latinx (HL) individuals and increases risk for heart failure. Delayed and missed diagnoses could potentiate health disparities in these populations. [...] Read more.
The TTR V142I variant associated with hereditary transthyretin amyloidosis (hATTR) is present in up to 4% of African American (AA) and 1% of Hispanic/Latinx (HL) individuals and increases risk for heart failure. Delayed and missed diagnoses could potentiate health disparities in these populations. We evaluated whether population-based genomic screening could effectively identify individuals at risk for hATTR and prompt initiation of risk management. We identified participants of the BioMe Biobank in New York City who received TTR V142I results through a pilot genomic screening program. We performed a retrospective medical record review to evaluate for the presence hATTR-related systemic features, uptake of recommended follow-up, and short-term outcomes. Thirty-two AA (N = 17) and HL (N = 15) individuals received a TTR V142I result (median age 57, 81% female). None had a previous diagnosis of hATTR. Eighteen (56%) had hATTR-related systemic features, including 4 (13%) with heart failure, 10 (31%) with carpal tunnel syndrome, and 10 (31%) with spinal stenosis. Eighteen (56%) pursued follow-up with a cardiologist within 8 months. One person received a diagnosis of hATTR. Thus, we found that the majority of V142I-positive individuals had hATTR-related systemic features at the time of result disclosure, including well-described red flags. Genomic screening can help identify hATTR risk and guide management early on, avoiding potential delays in diagnosis and treatment. Full article
(This article belongs to the Special Issue Personalized Medicine for Cardiovascular Disease)
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12 pages, 1160 KiB  
Article
Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases
by Suho Jin, Kristin Kostka, Jose D. Posada, Yeesuk Kim, Seung In Seo, Dong Yun Lee, Nigam H. Shah, Sungwon Roh, Young-Hyo Lim, Sun Geu Chae, Uram Jin, Sang Joon Son, Christian Reich, Peter R. Rijnbeek, Rae Woong Park and Seng Chan You
J. Pers. Med. 2020, 10(4), 288; https://doi.org/10.3390/jpm10040288 - 18 Dec 2020
Cited by 9 | Viewed by 4308
Abstract
Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) [...] Read more.
Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62–0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine. Full article
(This article belongs to the Special Issue Personalized Medicine for Cardiovascular Disease)
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