ArticleJournal of the American College of Cardiology2017
Seattle Heart Failure and Proportional Risk Models Predict Benefit From Implantable Cardioverter-Defibrillators.
Article in Journal of the American College of Cardiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ACC/AHA/ASE/HFSA/HRS/SCAI/SCCT/SCMR 2025 Appropriate Use Criteria for Implantable Cardioverter-Defibrillators, Cardiac Resynchronization Therapy, and Pacing.Journal of the American College of Cardiology · 2025Guideline
- Experiences Implementing a Suite of Decision Aids for Implantable Cardioverter Defibrillators: Qualitative Insights From the DECIDE-ICD Trial.Circulation. Cardiovascular quality and outcomes · 2022Trial
- Sudden Death After Hospitalization for Heart Failure With Reduced Ejection Fraction (from the EVEREST Trial).The American journal of cardiology · 2018Trial
- Predictors and Prognostic Significance of Appropriate Implantable Cardioverter-Defibrillator Therapy in Primary Prevention Patients with Ischemic Cardiomyopathy.Journal of clinical medicine · 2026Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Impact of non-cardiovascular factors on cardiovascular death in acute decompensated heart failure: Enhancing predictive models with frailty and comorbid constipation-the Constipation and Frailty Augmented AHEAD score.Geriatrics & gerontology international · 2025Article
- Cardiac rhythm devices in heart failure with reduced ejection fraction - role, timing, and optimal use in contemporary practice. European Journal of Heart Failure expert consensus document.European journal of heart failure · 2025Review
- JCS/JHRS 2024 Guideline Focused Update on Management of Cardiac Arrhythmias.Journal of arrhythmia · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Assessment of ICD eligibility in non-ischaemic cardiomyopathy patients: a position statement by the Task Force of the Dutch Society of Cardiology.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2024Review
- Implantable Cardioverter Defibrillator Tachycardia Therapies: Past, Present and Future Directions.Journal of cardiovascular development and disease · 2024Review
- Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.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 · 2023Article
- Review
- Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.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 · 2023Observational
- 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
- Time to rechallenge primary prevention ICD guidelines.ESC heart failure · 2022Article
- Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models.Journal of cardiovascular development and disease · 2022Article
- Usefulness of the MADIT-ICD Benefit Score in a Large Mixed Patient Cohort of Primary Prevention of Sudden Cardiac Death.Journal of personalized medicine · 2022Article
- Survival Probability and Survival Benefit Associated With Primary Prevention Implantable Cardioverter-Defibrillator Generator Changes.Journal of the American Heart Association · 2022Article
Corrections and comments
- Commented on by
Authors and funding
15 authors.
Funding
Abstract
backgroundRecent clinical trials highlight the need for better models to identify patients at higher risk of sudden death.
objectivesThe authors hypothesized that the Seattle Heart Failure Model (SHFM) for overall survival and the Seattle Proportional Risk Model (SPRM) for proportional risk of sudden death, including death from ventricular arrhythmias, would predict the survival benefit with an implantable cardioverter-defibrillator (ICD).
methodsPatients with primary prevention ICDs from the National Cardiovascular Data Registry (NCDR) were compared with control patients with heart failure (HF) without ICDs with respect to 5-year survival using multivariable Cox proportional hazards regression.
resultsAmong 98,846 patients with HF (87,914 with ICDs and 10,932 without ICDs), the SHFM was strongly associated with all-cause mortality (p < 0.0001). The ICD-SPRM interaction was significant (p < 0.0001), such that SPRM quintile 5 patients had approximately twice the reduction in mortality with the ICD versus SPRM quintile 1 patients (adjusted hazard ratios [HR]: 0.602; 95% confidence interval [CI]: 0.537 to 0.675 vs. 0.793; 95% CI: 0.736 to 0.855, respectively). Among patients with SHFM-predicted annual mortality ≤5.7%, those with a SPRM-predicted risk of sudden death below the median had no reduction in mortality with the ICD (adjusted ICD HR: 0.921; 95% CI: 0.787 to 1.08; p = 0.31), whereas those with SPRM above the median derived the greatest benefit (adjusted HR: 0.599; 95% CI: 0.530 to 0.677; p < 0.0001).
conclusionsThe SHFM predicted all-cause mortality in a large cohort with and without ICDs, and the SPRM discriminated and calibrated the potential ICD benefit. Together, the models identified patients less likely to derive a survival benefit from primary prevention ICDs.
Indexed as
Identifiers
What OpenQuestion holds
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.