1
|
Martínez Díaz P, Sánchez J, Fitzen N, Ravens U, Dössel O, Loewe A. The right atrium affects in silico arrhythmia vulnerability in both atria. Heart Rhythm 2024; 21:799-805. [PMID: 38301854 DOI: 10.1016/j.hrthm.2024.01.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/18/2024] [Accepted: 01/24/2024] [Indexed: 02/03/2024]
Affiliation(s)
- Patricia Martínez Díaz
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Information and Communication Technologies (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Nikola Fitzen
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ursula Ravens
- Institute for Experimental Cardiovascular Medicine, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
| |
Collapse
|
2
|
Schmidt S, Li W, Schubert M, Binnewerg B, Prönnecke C, Zitzmann FD, Bulst M, Wegner S, Meier M, Guan K, Jahnke HG. Novel high-dense microelectrode array based multimodal bioelectronic monitoring system for cardiac arrhythmia re-entry analysis. Biosens Bioelectron 2024; 252:116120. [PMID: 38394704 DOI: 10.1016/j.bios.2024.116120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/26/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
In recent decades, significant progress has been made in the treatment of heart diseases, particularly in the field of personalized medicine. Despite the development of genetic tests, phenotyping and risk stratification are performed based on clinical findings and invasive in vivo techniques, such as stimulation conduction mapping techniques and programmed ventricular pacing. Consequently, label-free non-invasive in vitro functional analysis systems are urgently needed for more accurate and effective in vitro risk stratification, model-based therapy planning, and clinical safety profile evaluation of drugs. To overcome these limitations, a novel multilayer high-density microelectrode array (HD-MEA), with an optimized configuration of 512 sensing and 4 pacing electrodes on a sensor area of 100 mm2, was developed for the bioelectronic detection of re-entry arrhythmia patterns. Together with a co-developed front-end, we monitored label-free and in parallel cardiac electrophysiology based on field potential monitoring and mechanical contraction using impedance spectroscopy at the same microelectrode. In proof of principle experiments, human induced pluripotent stem cell (hiPS)-derived cardiomyocytes were cultured on HD-MEAs and used to demonstrate the sensitive quantification of contraction strength modulation by cardioactive drugs such as blebbistatin (IC50 = 4.2 μM), omecamtiv and levosimendan. Strikingly, arrhythmia-typical rotor patterns (re-entry) can be induced by optimized electrical stimulation sequences and detected with high spatial resolution. Therefore, we provide a novel cardiac re-entry analysis system as a promising reference point for diagnostic approaches based on in vitro assays using patient-specific hiPS-derived cardiomyocytes.
Collapse
Affiliation(s)
- Sabine Schmidt
- Centre for Biotechnology and Biomedicine, Biochemical Cell Technology, Leipzig University, Deutscher Platz 5, D-04103, Leipzig, Germany
| | - Wener Li
- Institute of Pharmacology and Toxicology, Carl Gustav Carus Medical Faculty, Technical University Dresden, Fetscherstraße 74, D-01307, Dresden, Germany
| | - Mario Schubert
- Institute of Pharmacology and Toxicology, Carl Gustav Carus Medical Faculty, Technical University Dresden, Fetscherstraße 74, D-01307, Dresden, Germany
| | - Björn Binnewerg
- Institute of Pharmacology and Toxicology, Carl Gustav Carus Medical Faculty, Technical University Dresden, Fetscherstraße 74, D-01307, Dresden, Germany
| | - Christoph Prönnecke
- Centre for Biotechnology and Biomedicine, Biochemical Cell Technology, Leipzig University, Deutscher Platz 5, D-04103, Leipzig, Germany
| | - Franziska D Zitzmann
- Centre for Biotechnology and Biomedicine, Biochemical Cell Technology, Leipzig University, Deutscher Platz 5, D-04103, Leipzig, Germany
| | - Martin Bulst
- Sciospec Scientific Instruments GmbH, Leipziger Str. 43b, D-04828, Bennewitz, Germany
| | - Sebastian Wegner
- Sciospec Scientific Instruments GmbH, Leipziger Str. 43b, D-04828, Bennewitz, Germany
| | - Matthias Meier
- Centre for Biotechnology and Biomedicine, Biochemical Cell Technology, Leipzig University, Deutscher Platz 5, D-04103, Leipzig, Germany; Helmholtz Pioneer Campus, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Kaomei Guan
- Institute of Pharmacology and Toxicology, Carl Gustav Carus Medical Faculty, Technical University Dresden, Fetscherstraße 74, D-01307, Dresden, Germany
| | - Heinz-Georg Jahnke
- Centre for Biotechnology and Biomedicine, Biochemical Cell Technology, Leipzig University, Deutscher Platz 5, D-04103, Leipzig, Germany.
| |
Collapse
|
3
|
Song E. Effects of hydroxychloroquine on atrial electrophysiology in in silico wild-type and PITX2 +/- atrial cardiomyocytes. Herz 2023; 48:384-392. [PMID: 36732468 PMCID: PMC9894744 DOI: 10.1007/s00059-023-05162-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Hydroxychloroquine (HCQ) is commonly used in the treatment of autoimmune diseases and increases the risk of QT interval prolongation. However, it is unclear how HCQ affects atrial electrophysiology and the risk of atrial fibrillation (AF). METHODS We quantitatively examined the potential atrial arrhythmogenic effects of HCQ on AF using a computational model of human atrial cardiomyocytes. We measured atrial electrophysiological markers after systematically varying HCQ concentrations. RESULTS The HCQ concentrations were positively correlated with the action potential duration (APD), resting membrane potential, refractory period, APD alternans threshold, and calcium transient alternans threshold (p < 0.05). By contrast, HCQ concentrations were inversely correlated with the maximum upstroke velocity and calcium transient amplitude (p < 0.05). When the therapeutic concentration (Cmax) of HCQ was applied, HCQ increased APD90 by 1.4% in normal sinus rhythm, 1.8% in wild-type AF, and 2.6% in paired-like homeodomain transcription factor 2 (PITX2)+/- AF, but did not affect the alternans thresholds. The overall in silico results suggest no significant atrial arrhythmogenic effects of HCQ at Cmax, instead implying a potential antiarrhythmic role of low-dose HCQ in AF. However, at an HCQ concentration of fourfold Cmax, a rapid pacing rate of 4 Hz induced prominent APD alternans, particularly in the PITX2+/- AF model. CONCLUSION Our in silico analysis suggests a potential antiarrhythmic role of low-dose HCQ in AF. Concomitant PITX2 mutations and high-dose HCQ treatments may increase the risk of AF, and this potential genotype/dose-dependent arrhythmogenic effect of HCQ should be investigated further.
Collapse
Affiliation(s)
- Euijun Song
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Yonsei University College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
4
|
Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
Collapse
Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| |
Collapse
|
5
|
Solís-Lemus JA, Baptiste T, Barrows R, Sillett C, Gharaviri A, Raffaele G, Razeghi O, Strocchi M, Sim I, Kotadia I, Bodagh N, O'Hare D, O'Neill M, Williams SE, Roney C, Niederer S. Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study. Comput Biol Med 2023; 162:107009. [PMID: 37301099 PMCID: PMC10790305 DOI: 10.1016/j.compbiomed.2023.107009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/11/2023] [Accepted: 05/03/2023] [Indexed: 06/12/2023]
Abstract
This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.
Collapse
Affiliation(s)
- José Alonso Solís-Lemus
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK.
| | - Tiffany Baptiste
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Rosie Barrows
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Charles Sillett
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Ali Gharaviri
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Giulia Raffaele
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; School of Medical Education, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Orod Razeghi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Department of Haematology, NHS Blood and Transplant Centre, University of Cambridge, Cambridge, UK
| | - Marina Strocchi
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Iain Sim
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Irum Kotadia
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Neil Bodagh
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Daniel O'Hare
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Mark O'Neill
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK
| | - Steven E Williams
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Centre for Cardiovascular Science, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, Scotland, UK
| | - Caroline Roney
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Queen Mary University of London, Mile End Rd, Bethnal Green, London, E1 4NS, UK
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas Hospital, London, SE1 7EH, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK
| |
Collapse
|
6
|
Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
Collapse
Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| |
Collapse
|
7
|
Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sanchez J, Unger L, Dössel O, Jadidi A, Loewe A. Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence. Europace 2022; 25:211-222. [PMID: 35943361 PMCID: PMC9907752 DOI: 10.1093/europace/euac116] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022] Open
Abstract
AIMS The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. METHODS AND RESULTS Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5-6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. CONCLUSION The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.
Collapse
Affiliation(s)
- Luca Azzolin
- Corresponding author. Tel: +393381319986, E-mail address:
| | | | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Jorge Sanchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Building 30.33, Fritz-Haber-Weg 1, 76131 Karlsruhe, Germany
| | | | | |
Collapse
|
8
|
Gander L, Pezzuto S, Gharaviri A, Krause R, Perdikaris P, Sahli Costabal F. Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification. Front Physiol 2022; 13:757159. [PMID: 35330935 PMCID: PMC8940533 DOI: 10.3389/fphys.2022.757159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
Collapse
Affiliation(s)
- Lia Gander
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Paris Perdikaris
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
| |
Collapse
|
9
|
Roney CH, Sim I, Yu J, Beach M, Mehta A, Alonso Solis-Lemus J, Kotadia I, Whitaker J, Corrado C, Razeghi O, Vigmond E, Narayan SM, O’Neill M, Williams SE, Niederer SA. Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models. Circ Arrhythm Electrophysiol 2022; 15:e010253. [PMID: 35089057 PMCID: PMC8845531 DOI: 10.1161/circep.121.010253] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
Collapse
Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- School of Engineering and Materials Science, Queen Mary University of London, United Kingdom (C.H.R.)
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jin Yu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Marianne Beach
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Arihant Mehta
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Jose Alonso Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- The Department of Internal Medicine, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA (J.W.)
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, France (E.V.)
- Univ. Bordeaux, IMB, UMR 5251, F-33400 Talence, France (E.V.)
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA (S.M.N.)
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
- Centre for Cardiovascular Science, College of Medicine and Veterinary Medicine, University of Edinburgh (S.E.W.)
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom (C.H.R., I.S., J.Y., M.B., A.M., J.A.S.-L., I.K., J.W., C.C., O.R., M.O., S.E.W., S.A.N.)
| |
Collapse
|
10
|
Roney CH, Child N, Porter B, Sim I, Whitaker J, Clayton RH, Laughner JI, Shuros A, Neuzil P, Williams SE, Razavi RS, O'Neill M, Rinaldi CA, Taggart P, Wright M, Gill JS, Niederer SA. Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response. Front Physiol 2021; 12:707189. [PMID: 34646149 PMCID: PMC8503618 DOI: 10.3389/fphys.2021.707189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/24/2021] [Indexed: 11/13/2022] Open
Abstract
Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.
Collapse
Affiliation(s)
- Caroline H. Roney
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Nicholas Child
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Bradley Porter
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard H. Clayton
- INSIGNEO Institute for In Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | | | - Allan Shuros
- Boston Scientific Corp, St. Paul, MN, United States
| | - Petr Neuzil
- Department of Cardiology, Na Holmolce Hospital, Prague, Czechia
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Reza S. Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Peter Taggart
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Matt Wright
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Jaswinder S. Gill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
11
|
Plank G, Loewe A, Neic A, Augustin C, Huang YL, Gsell MAF, Karabelas E, Nothstein M, Prassl AJ, Sánchez J, Seemann G, Vigmond EJ. The openCARP simulation environment for cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106223. [PMID: 34171774 DOI: 10.1016/j.cmpb.2021.106223] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.
Collapse
Affiliation(s)
- Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Yung-Lin Huang
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac-Bordeaux, France; Université Bordeaux, IMB, UMR 5251, F-33400 Talence, France
| |
Collapse
|
12
|
Nagel C, Luongo G, Azzolin L, Schuler S, Dössel O, Loewe A. Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG-A Large-Scale Computational Study Covering Anatomical Variability. J Clin Med 2021; 10:1797. [PMID: 33924210 PMCID: PMC8074591 DOI: 10.3390/jcm10081797] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 11/21/2022] Open
Abstract
The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibrillation propensity. In this work, we realized 54,000 combinations of different atria and thorax geometries from statistical shape models capturing anatomical variability in the general population. For each atrial model, 10 different volume fractions (0-45%) were defined as fibrotic. Electrophysiological simulations in sinus rhythm were conducted for each model combination and the respective 12-lead ECGs were computed. P wave features (duration, amplitude, dispersion, terminal force in V1) were extracted and compared between the healthy and the diseased model cohorts. All investigated feature values systematically in- or decreased with the left atrial volume fraction covered by fibrotic tissue, however value ranges overlapped between the healthy and the diseased cohort. Using all extracted P wave features as input values, the amount of the fibrotic left atrial volume fraction was estimated by a neural network with an absolute root mean square error of 8.78%. Our simulation results suggest that although all investigated P wave features highly vary for different anatomical properties, the combination of these features can contribute to non-invasively estimate the volume fraction of atrial fibrosis using ECG-based machine learning approaches.
Collapse
Affiliation(s)
- Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany; (G.L.); (L.A.); (S.S.); (O.D.); (A.L.)
| | | | | | | | | | | |
Collapse
|