ReviewJournal of clinical medicine2021
Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics.
Review in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 31 citations in OpenAlex.
- Non-Dilated Left Ventricular Cardiomyopathy: Exploring the Spectrum from Phenotype to Disease Entity.Journal of clinical medicine · 2026Review
- AI-Enabled Precision Echocardiography: Toward Personalized Cardiovascular Care.Diagnostics (Basel, Switzerland) · 2026Review
- Knowledge graph visualization and retrospective analysis of genetic research on pediatric cardiomyopathy (2000-2024).Frontiers in cardiovascular medicine · 2026Article
- Impact of genotype-phenotype associations on prognosis in dilated cardiomyopathy.European journal of heart failure · 2025Article
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- Discovering new hub genes of dilated cardiomyopathy.ESC heart failure · 2025Article
- GC vitamin D-binding protein gene functional genetic variants and gestational diabetes mellitus risk and prediction.Scientific reports · 2025Article
- Yield of family screening for dilated cardiomyopathy: 10-year experience at a multidisciplinary cardiogenetic outpatient clinic.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2025Article
- Microvascular Dysfunction in Patients with Idiopathic Dilated Cardiomyopathy: Quantitative Assessment with Phase Contrast Cine MR Imaging of the Coronary Sinus.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Diverse Concepts in Definitions of Dilated Cardiomyopathy: Theory and Practice.Cardiology research · 2024Review
- Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus.Scientific reports · 2024Article
- Cardiac Magnetic Resonance as Risk Stratification Tool in Non-Ischemic Dilated Cardiomyopathy Referred for Implantable Cardioverter Defibrillator Therapy-State of Art and Perspectives.Journal of clinical medicine · 2023Review
- The application of radiology for dilated cardiomyopathy diagnosis, treatment, and prognosis prediction: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2023Article
- Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?Journal of clinical medicine · 2023Article
- Cardiomyopathy: A New Perspective from Diagnostic Strategy.Journal of clinical medicine · 2023Article
- Relationships of Whole-Heart Myocardial Mechanics and Cardiac Morphometrics by Transthoracic Echocardiography with Main Prognostic Factors of Heart Failure in Non-Ischemic Dilated Cardiomyopathy.Journal of clinical medicine · 2023Article
- Big Data in Cardiology: State-of-Art and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening.Frontiers in cardiovascular medicine · 2022Article
- Double p52Shc/p46Shc Rat Knockout Demonstrates Severe Gait Abnormalities Accompanied by Dilated Cardiomyopathy.International journal of molecular sciences · 2021Article
- The Role of AI in Characterizing the DCM Phenotype.Frontiers in cardiovascular medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.
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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.