ArticleJACC. Clinical electrophysiology2022
Prediction of Sudden Cardiac Death Manifesting With Documented Ventricular Fibrillation or Pulseless Ventricular Tachycardia.
Article in JACC. Clinical electrophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 49 citations in OpenAlex.
- The neglected role of lethal arrhythmia in the sudden death of stroke survivors.Nature cardiovascular research · 2026Article
- Clinical Triage of Individuals With Warning Symptoms of Imminent Cardiac Arrest.Circulation. Arrhythmia and electrophysiology · 2026Observational
- Deep learning-based early prediction of life-threatening ventricular arrhythmias using long-term Holter ECG signals.Frontiers in cardiovascular medicine · 2026Article
- What limits a bystander's action to perform cardiopulmonary resuscitation in China? Insights from a cross-sectional study conducted in Shaanxi Province.Frontiers in public health · 2026Article
- Beyond cardiac risk factors: non-cardiovascular comorbidities in sudden cardiac death prediction.Frontiers in cardiovascular medicine · 2026Review
- Left ventricular ejection fraction and myocardial fibrosis in sudden cardiac death.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Age-Specific Mechanisms of Sudden Death Associated With Epilepsy.JACC. Clinical electrophysiology · 2025Article
- Moderate Kidney Dysfunction Independently Increases Sudden Cardiac Arrest Risk: A Community-Based Study.Journal of the American Heart Association · 2025Article
- Temporal trends in left ventricular ejection fraction before sudden death in patients with heart failure.Heart rhythm · 2025Article
- Validation of a Novel Risk Prediction Score for Sudden Cardiac Death in the Framingham Heart Study.Circulation. Arrhythmia and electrophysiology · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort.International journal of cardiology. Heart & vasculature · 2025Article
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- How to Prevent Arrhythmias Following Acute Coronary Syndrome.Clinical cardiology · 2025Article
- Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.Circulation · 2024Article
- Lack of Prognostic Value of T-Wave Alternans for Implantable Cardioverter-Defibrillator Benefit in Primary Prevention.Journal of the American Heart Association · 2024Article
- Ventricular fibrillation and the proteome problem: can we solve it?European heart journal. Acute cardiovascular care · 2024Article
- Dynamic electrocardiogram changes are a novel risk marker for sudden cardiac death.European heart journal · 2024Article
- An ECG-based artificial intelligence model for assessment of sudden cardiac death risk.Communications medicine · 2024Article
- Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation.Circulation. Arrhythmia and electrophysiology · 2024Article
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesThis study aimed to develop a novel clinical prediction algorithm for avertable sudden cardiac death.
backgroundSudden cardiac death manifests as ventricular fibrillation (VF)/ ventricular tachycardia (VT) potentially treatable with defibrillation, or nonshockable rhythms (pulseless electrical activity/asystole) with low likelihood of survival. There are no available clinical risk scores for targeted prediction of VF/VT.
methodsSubjects with out-of-hospital sudden cardiac arrest presenting with documented VF or pulseless VT (33% of total cases) were ascertained prospectively from the Portland, Oregon, metro area with population ≈1 million residents (n = 1,374, 2002-2019). Comparisons of lifetime clinical records were conducted with a control group (n = 1,600) with ≈70% coronary disease prevalence. Prediction models were constructed from a training dataset using backwards stepwise logistic regression and applied to an internal validation dataset. Receiver operating characteristic curves (C statistic) were used to evaluate model discrimination. External validation was performed in a separate, geographically distinct population (Ventura County, California, population ≈850,000, 2015-2020).
resultsA clinical algorithm (VFRisk) constructed with 13 clinical, electrocardiogram, and echocardiographic variables had very good discrimination in the training dataset (C statistic = 0.808; [95% CI: 0.774-0.842]) and was successfully validated in internal (C statistic = 0.776 [95% CI: 0.725-0.827]) and external (C statistic = 0.782 [95% CI: 0.718-0.846]) datasets. The algorithm substantially outperformed the left ventricular ejection fraction (LVEF) ≤35% (C statistic = 0.638) and performed well across the LVEF spectrum.
conclusionsAn algorithm for prediction of sudden cardiac arrest manifesting with VF/VT was successfully constructed using widely available clinical and noninvasive markers. These findings have potential to enhance primary prevention, especially in patients with mid-range or preserved LVEF.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.