ArticleMedical & biological engineering & computing2015
Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Connecting transcriptomics with computational modeling to reveal developmental adaptations in pediatric human atrial tissue.American journal of physiology. Heart and circulatory physiology · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Human induced pluripotent stem cell-derived cardiomyocytes as an electrophysiological model: Opportunities and challenges-The Hamburg perspective.Frontiers in physiology · 2023Review
- Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomyocytes.Cardiovascular research · 2022Article
- In Silico Assessment of Class I Antiarrhythmic Drug Effects onInternational journal of molecular sciences · 2021Article
- Populations of in silico myocytes and tissues reveal synergy of multiatrial-predominant KBritish journal of pharmacology · 2020Article
- Improving performance of 3D speckle tracking in arterial hypertension and paroxysmal atrial fibrillation by using novel strain parameters.Scientific reports · 2019Observational
- Cardiac fibroblasts : Active players in (atrial) electrophysiology?Herzschrittmachertherapie & Elektrophysiologie · 2018Review
- Report on the Ion Channel Symposium : Organized by the German Cardiac Society Working Group on Cellular Electrophysiology (AG 18).Herzschrittmachertherapie & Elektrophysiologie · 2018Review
- Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges.Frontiers in physiology · 2018Review
- A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research.Frontiers in physiology · 2018Review
- Modelling variability in cardiac electrophysiology: a moment-matching approach.Journal of the Royal Society, Interface · 2017Article
- Computational models of atrial cellular electrophysiology and calcium handling, and their role in atrial fibrillation.The Journal of physiology · 2016Review
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Authors and funding
15 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.
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