ArticleJournal of the American Heart Association2020
Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy.
Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01076660 (Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of), which is not on this map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of: Functional Energetics and Imaging for Phenotypic Characterization of Patients at Risk for Sudden Cardiac Death, See Also NCT000181233]
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.EBioMedicine · 2023Pooled it
- Dynamic Risk Trajectories for Sudden Cardiac Arrest: The Role of Recurrent Cardiovascular Events.Journal of the American Heart Association · 2026Observational
- Combined Analysis of Cardiac Repolarization Entropy and Magnetic Resonance Imaging Enhances Risk Stratification of Patients Receiving a Primary-Prevention ICD.JACC. Clinical electrophysiology · 2026Article
- Sleep is associated with posttraumatic stress symptom severity in patients with an implantable cardioverter-defibrillator depending on age.Scientific reports · 2026Article
- Predictors and Prognostic Significance of Appropriate Implantable Cardioverter-Defibrillator Therapy in Primary Prevention Patients with Ischemic Cardiomyopathy.Journal of clinical medicine · 2026Article
- Dynamic Risk Trajectories for Sudden Cardiac Arrest: The Role of Recurrent Cardiovascular Events.medRxiv : the preprint server for health sciences · 2025Article
- Ventricular Arrhythmias and Myocardial Infarction: Electrophysiological and Neuroimmune Mechanisms.Biomedicines · 2025Review
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- An ECG-based artificial intelligence model for assessment of sudden cardiac death risk.Communications medicine · 2024Article
- Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study.PloS one · 2024Article
- Article
- Fir(e)ing the Rhythm: Inflammatory Cytokines and Cardiac Arrhythmias.JACC. Basic to translational science · 2023Review
- A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study.Reviews in cardiovascular medicine · 2023Article
- Artificial Intelligence in Ventricular Arrhythmias and Sudden Death.Arrhythmia & electrophysiology review · 2023Review
- Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models.Journal of cardiovascular development and disease · 2022Article
- Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.Medicina (Kaunas, Lithuania) · 2022Article
- The Association of Clustered Ventricular Arrhythmia and Cycle Length With Scar Burden in Cardiomyopathy.JACC. Clinical electrophysiology · 2022Article
- Review
- Myocardial ATP depletion detected noninvasively predicts sudden cardiac death risk in patients with heart failure.JCI insight · 2022Observational
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.
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Registered trials
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.