Risk Stratification Strategies for Cardiac Rhythm Abnormalities

194.3K

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328

authors

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articles

Editorial

27 April 2022

Editorial: Risk Stratification Strategies for Cardiac Rhythm Abnormalities

Gary Tse

,

Nan Zhang

,

Wenhua Song

,

Konstantinos P. Letsas

, 2 more and

Tong Liu

1,684 views

1 citations

Editors

5

Kamalan Jeevaratnam

University of Surrey

Konstantinos Letsas

Onassis Cardiac Surgery Center

Tianjin Medical University

Tachapong Ngarmukos

Ramathibodi Nursing School, Faculty of Medicine Ramathibodi Hospital, Mahidol University

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Cardiac rhythm abnormalities account for a significant proportion of adverse cardiac events and mortality. In fact, most cardiovascular pathologies will lead to some form of rhythm abnormalities as the pathology worsens. In many instances, such rhythm abnormalities precede the fatal event. The ability to risk stratify patients for cardiac rhythm abnormalities is central to early and timely intervention in patients. There are protocols and diagnostic algorithms currently in place that help the clinician to profile the risk associated with a condition. None of these tools are fully accurate that thus, despite efforts to improve risk stratification strategies, identification of high-risk individuals remains challenging.

The aim of this research topic is to address the following questions:
1) What are the physiological mechanisms that underlie rhythm abnormalities and how can they be used to inform approaches or tools for risk stratification.
2) What are the current approaches used for risk stratification in different conditions leading to cardiac rhythm abnormalities and their impact on patient outcomes.
3) How do experimental models and computational systems inform development of risk stratification tools for rhythm abnormalities.

We welcome researchers from all experience levels to contribute high-quality reviews, mini-reviews, original investigations and methodology papers, that shed light on the development and our understanding on the use of various risk stratification tools and approaches. This may involve work at the basic science level that helps inform development of novel risk stratification approaches for cardiac rhythm abnormalities, innovations using computational or machine learning approaches or realignment of currently established criteria for risk stratification of cardiac rhythm abnormalities. Insightful opinion pieces or hypotheses papers are also encouraged.

Cardiac rhythm abnormalities account for a significant proportion of adverse cardiac events and mortality. In fact, most cardiovascular pathologies will lead to some form of rhythm abnormalities as the pathology worsens. In many instances, such rhythm abnormalities precede the fatal event. The ability to risk stratify patients for cardiac rhythm abnormalities is central to early and timely intervention in patients. There are protocols and diagnostic algorithms currently in place that help the clinician to profile the risk associated with a condition. None of these tools are fully accurate that thus, despite efforts to improve risk stratification strategies, identification of high-risk individuals remains challenging.

The aim of this research topic is to address the following questions:
1) What are the physiological mechanisms that underlie rhythm abnormalities and how can they be used to inform approaches or tools for risk stratification.
2) What are the current approaches used for risk stratification in different conditions leading to cardiac rhythm abnormalities and their impact on patient outcomes.
3) How do experimental models and computational systems inform development of risk stratification tools for rhythm abnormalities.

We welcome researchers from all experience levels to contribute high-quality reviews, mini-reviews, original investigations and methodology papers, that shed light on the development and our understanding on the use of various risk stratification tools and approaches. This may involve work at the basic science level that helps inform development of novel risk stratification approaches for cardiac rhythm abnormalities, innovations using computational or machine learning approaches or realignment of currently established criteria for risk stratification of cardiac rhythm abnormalities. Insightful opinion pieces or hypotheses papers are also encouraged.

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Editors

Kamalan Jeevaratnam

University of Surrey

Konstantinos Letsas

Onassis Cardiac Surgery Center

Tianjin Medical University

Tachapong Ngarmukos

Ramathibodi Nursing School, Faculty of Medicine Ramathibodi Hospital, Mahidol University

Impact

194,295 Total views

141,271 Article views

50,882 Article downloads

2,142 Topic views

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Frontiers in Cardiovascular Medicine

Cardiac Rhythmology

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