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Sudden Cardiac Death: Innovations in Diagnosis, Prevention, and Treatment
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Sudden Cardiac Death: Innovations in Diagnosis, Prevention, and Treatment

A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: 16 September 2026 | Viewed by 1063

Special Issue Editors

Prof. Dr. Martina Lovrić-Benčić

E-Mail Website
Guest Editor
1. School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
2. Department for Ischemic Heart Disease, University Clinic of Cardiovascular Diseases, Clinical Hospital Center Zagreb, 10000 Zagreb, Croatia
Interests: SCD; sudden cardiac death; VT; cardiomyopathy; CMP; risk stratification; prevention; malignant arrhythmias
Dr. Mislav Puljevic

E-Mail Website
Guest Editor Assistant
1. School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
2. Devison of Vascular Diseases, Clinical Unit of Electrophysiology and Electrostomulation, University Clinic of Cardiovascular Diseases, Clinical Hospital Centre Zagreb, 10000 Zagreb, Croatia
Interests: SCD; sudden cardiac death; VT; cardiomyopathy; CMP; risk stratification; prevention; malignant arrhythmias

Special Issue Information

Dear Colleagues,

This is proposal for a Special Issue dedicated to the critical topic of Sudden Cardiac Death (SCD), which remains one of the leading causes of mortality globally. This Special Issue aims to provide a comprehensive overview of current advancements in the diagnosis, prevention, and treatment of SCD, reflecting on the latest research and clinical practices. I invite original research articles, reviews, and clinical studies that investigate various aspects of SCD. Topics may include innovative diagnostic methods such as advanced imaging techniques and the development of biomarkers for early detection and risk assessment. Also, I invite contributions that explore effective prevention strategies, including lifestyle interventions, pharmacological approaches, and the role of wearable technology in monitoring at-risk populations. The specialty of this Special Issue will be regarding the use of AI in predicting SCD. Additionally, this Special Issue will focus on emerging therapeutic interventions, highlighting advancements in devices such as implantable cardioverter-defibrillators and cardiac resynchronization therapy. By bringing together diverse perspectives from leading experts in the field, this Special Issue aims to enhance the understanding of and improve management strategies for SCD, ultimately benefiting patients and healthcare providers alike.

Prof. Dr. Martina Lovrić-Benčić
Guest Editor

Dr. Mislav Puljevic
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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. Healthcare 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 2700 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

  • sudden cardiac death
  • VT
  • cardiomyopathy
  • CMP
  • risk stratification
  • prevention
  • malignant arrhythmias

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Published Papers (1 paper)

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Research

14 pages, 265 KB  
Article
Improving Risk Stratification in Sudden Cardiac Death Using Interpretable Machine Learning: A Clinical Perspective
by Hana Ivandic, Branimir Pervan, Vedran Velagic, Alan Jovic and Mislav Puljevic
Healthcare 2025, 13(21), 2788; https://doi.org/10.3390/healthcare13212788 - 3 Nov 2025
Viewed by 558
Abstract
Background: Sudden cardiac death (SCD) remains a major cause of cardiovascular mortality. Implantable cardioverter-defibrillators (ICDs) reduce arrhythmic mortality, but current selection based largely on left ventricular ejection fraction (LVEF) lacks precision. Many patients undergo device implantation without ever receiving therapy, while others [...] Read more.
Background: Sudden cardiac death (SCD) remains a major cause of cardiovascular mortality. Implantable cardioverter-defibrillators (ICDs) reduce arrhythmic mortality, but current selection based largely on left ventricular ejection fraction (LVEF) lacks precision. Many patients undergo device implantation without ever receiving therapy, while others at risk remain unprotected. Interpretable machine learning (ML) can integrate diverse clinical variables and refine patient selection while maintaining transparency in clinical reasoning. Methods: We retrospectively analyzed 607 patients who underwent ICD or CRT-D implantation at a Croatian tertiary care center. Baseline demographic, clinical, echocardiographic, laboratory, and device-related variables were collected. Patients were followed through routine device interrogations, with appropriate ICD activation serving as a surrogate for SCD prevention. A logistic regression (LR) model was trained to predict appropriate device activation. Results: LR model demonstrated strong predictive ability (AUC-ROC 0.74, sensitivity 86.50%). Significant predictors included ventricular tachycardia (VT) burden, sustained VT, longer follow-up, and secondary prevention. The combination of furosemide and spironolactone therapy was linked to lower predicted SCD risk. Conclusions: ML applied to routinely collected data can support risk stratification in SCD and complement existing guideline criteria by reinforcing known predictors and uncovering novel associations. Full article
(This article belongs to the Special Issue Sudden Cardiac Death: Innovations in Diagnosis, Prevention, and Treatment)
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