ReviewNature reviews. Cardiology2019
Computational models in cardiology.
Review in Nature reviews. Cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 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
212 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Simulating impaired left ventricular-arterial coupling in aging and disease: a systematic review.Biomedical engineering online · 2024Pooled it
- Determining properties of human-induced pluripotent stem cell-derived cardiomyocytes using spatially resolved electromechanical metrics.The Journal of physiology · 2026Article
- In silico modelling of multi-electrode arrays for enhancing cardiac drug testing on hiPSC-CM heterogeneous tissues.The Journal of physiology · 2026Article
- Hierarchy Aware CT-Driven Constrained Constructive Optimization: A Framework for Coronary Network Generation.Annals of biomedical engineering · 2026Article
- I-GraphECG: Observability-Based Lead Selection and Interpretable Disease Prediction from the 12-Lead ECG Using a Gray-Box Graph Electrophysiology Surrogate.Sensors (Basel, Switzerland) · 2026Article
- Integration of multiple perspectives to understand the left ventricular pressure-volume relationship.Communications medicine · 2026Review
- The Hodgkin-Huxley-Katz Prize Lecture: Heart digital twins: A new paradigm in arrhythmia medicine.The Journal of physiology · 2026Article
- Prediction of arrhythmogenesis in non-ischemic cardiac resynchronization therapy patients.APL bioengineering · 2026Article
- The functional impact of myofiber macroscopic organization and disarray in computational models of the murine heart.PLoS computational biology · 2026Article
- Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing for diagnosis and prognosis.Bioengineering & translational medicine · 2026Review
- Calcium-mediated force-interval relationship drives post-extrasystolic potentiation in premature ventricular complexes: a computational study.Biomechanics and modeling in mechanobiology · 2026Article
- Post-MI Remodeling Mechanics of Left Ventricle: Microstructure-Informed Models, Identifiability, and Uncertainty for Patient-Specific Prediction.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.Journal of arrhythmia · 2026Review
- Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.Indian pediatrics · 2026Article
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Article
- Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.PLoS computational biology · 2026Article
- Article
- Cardiac remodelling in type 2 diabetes: Pathophysiological mechanisms and opportunities for multiscale computational modelling and simulation.The Journal of physiology · 2026Review
152 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
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.