ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
Machine learning-based risk model using
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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
25 citing papers in PubMed, 32 citations in OpenAlex.
- Positron Emission Tomography Imaging of Regional Versus Global Myocardial Sympathetic Activity to Improve Risk Stratification in Patients With Ischemic Cardiomyopathy.Circulation. Cardiovascular imaging · 2021Trial
- Stratifying risk of heart failure death and arrhythmic events: a ¹²³I-meta-iodobenzylguanidine-based multinomial logistic model.European journal of nuclear medicine and molecular imaging · 2026Article
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Machine Learning-Based Risk Stratification for Sudden Cardiac Death Using Clinical and Device-Derived Data.Sensors (Basel, Switzerland) · 2025Article
- Improving Risk Stratification in Sudden Cardiac Death Using Interpretable Machine Learning: A Clinical Perspective.Healthcare (Basel, Switzerland) · 2025Article
- Cardiac sympathetic activity and lethal arrhythmic events: insight into bell-shaped relationship betweenEJNMMI research · 2024Article
- J-ACCESS investigation and nuclear cardiology in Japan: implications for heart failure.Annals of nuclear medicine · 2023Review
- Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- Review
- Virtual healthcare solutions in heart failure: a literature review.Frontiers in cardiovascular medicine · 2023Review
- Decreased cholinesterase level combined with renal dysfunction and sympathetic denervation associated with increased cardiac mortality in systolic heart failure.Frontiers in cardiovascular medicine · 2023Article
- Three-Dimensional Heart Segmentation and Absolute Quantitation of CardiacAnnals of nuclear cardiology · 2023Article
- Artificial Intelligence in Ventricular Arrhythmias and Sudden Death.Arrhythmia & electrophysiology review · 2023Review
- CardiacPharmaceuticals (Basel, Switzerland) · 2022Review
- Prognostic value of nutritional parameters in systolic heart failure with renal dysfunction.PloS one · 2022Article
- Nuclear Cardiology Data Analyzed Using Machine Learning.Annals of nuclear cardiology · 2022Review
- Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.JMIR medical informatics · 2021Article
- Value of combined perfusion and metabolic tracers versus stress-rest perfusion imaging for coronary heart disease.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Impaired Cardiac Sympathetic Innervation Increases the Risk of Cardiac Events in Heart Failure Patients with Left Ventricular Hypertrophy and Mechanical Dyssynchrony.Journal of clinical medicine · 2021Article
- Convolutional neural network-based automatic heart segmentation and quantitation inEJNMMI research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiac sympathetic dysfunction is closely associated with cardiac mortality in patients with chronic heart failure (CHF). We analyzed the ability of machine learning incorporating METHODS AND
resultsA model was created based on patients with documented 2-year outcomes of CHF (n = 526; age, 66 ± 14 years). Classifiers were trained using 13 variables including age, gender, NYHA functional class, left ventricular ejection fraction and planar
conclusionA risk model based on machine learning using clinical variables and
Indexed as
Identifiers
What Socratic 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 Socratic graph.