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Qian S, Ugurlu D, Fairweather E, Strocchi M, Toso LD, Deng Y, Plank G, Vigmond E, Razavi R, Young A, Lamata P, Bishop M, Niederer S. Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.05.23299435. [PMID: 38106072 PMCID: PMC10723499 DOI: 10.1101/2023.12.05.23299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.
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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.
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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
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Rodero C, Baptiste TMG, Barrows RK, Keramati H, Sillett CP, Strocchi M, Lamata P, Niederer SA. A systematic review of cardiac in-silico clinical trials. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2023; 5:032004. [PMID: 37360227 PMCID: PMC10286106 DOI: 10.1088/2516-1091/acdc71] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/26/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.
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Affiliation(s)
- Cristobal Rodero
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Tiffany M G Baptiste
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rosie K Barrows
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Hamed Keramati
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Charles P Sillett
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Marina Strocchi
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Pablo Lamata
- Cardiac Modelling and Imaging Biomarkers (CMIB), Department of Biomedical Engineering and Imaging Sciences Department, King’s College London, London, United Kingdom
| | - Steven A Niederer
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiac Electro-Mechanics Research Group (CEMRG), Department of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, United Kingdom
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Vergara C, Stella S, Maines M, Africa PC, Catanzariti D, Demattè C, Centonze M, Nobile F, Quarteroni A, Del Greco M. Computational electrophysiology of the coronary sinus branches based on electro-anatomical mapping for the prediction of the latest activated region. Med Biol Eng Comput 2022; 60:2307-2319. [PMID: 35729476 PMCID: PMC9293833 DOI: 10.1007/s11517-022-02610-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/07/2022] [Indexed: 01/18/2023]
Abstract
This work dealt with the assessment of a computational tool to estimate the electrical activation in the left ventricle focusing on the latest electrically activated segment (LEAS) in patients with left bundle branch block and possible myocardial fibrosis. We considered the Eikonal-diffusion equation and to recover the electrical activation maps in the myocardium. The model was calibrated by using activation times acquired in the coronary sinus (CS) branches or in the CS solely with an electroanatomic mapping system (EAMS) during cardiac resynchronization therapy (CRT). We applied our computational tool to ten patients founding an excellent accordance with EAMS measures; in particular, the error for LEAS location was less than 4 mm. We also calibrated our model using only information in the CS, still obtaining an excellent agreement with the measured LEAS. The proposed tool was able to accurately reproduce the electrical activation maps and in particular LEAS location in the CS branches, with an almost real-time computational effort, regardless of the presence of myocardial fibrosis, even when information only at CS was used to calibrate the model. This could be useful in the clinical practice since LEAS is often used as a target site for the left lead placement during CRT.
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Affiliation(s)
- Christian Vergara
- LABS, Dipartimento Di Chimica, Materiali E Ingegneria Chimica “Giulio Natta”, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Simone Stella
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Massimiliano Maines
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Pasquale Claudio Africa
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
| | - Domenico Catanzariti
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Cristina Demattè
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
| | - Maurizio Centonze
- U.O. Di Radiologia Di Borgo-Pergine, Borgo Valsugana Hospital, viale Vicenza 9, 38051 Borgo Valsugana, (TN) Italy
| | - Fabio Nobile
- Institute of Mathematics, CSQI, École Polytechnique Fédérale de Lausanne, Route Cantonale, 1015 Lausanne, Switzerland
| | - Alfio Quarteroni
- Dipartimento Di Matematica, MOX, Politecnico Di Milano, Piazza Leonardo da Vinci 32, 20233 Milan, Italy
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maurizio Del Greco
- Department of Cardiology, S. Maria del Carmine Hospital, corso Verona 4, 38068 Rovereto, TN Italy
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2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. Translation of the document prepared by the Czech Society of Cardiology. COR ET VASA 2022. [DOI: 10.33678/cor.2022.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Glikson M, Nielsen JC, Kronborg MB, Michowitz Y, Auricchio A, Barbash IM, Barrabés JA, Boriani G, Braunschweig F, Brignole M, Burri H, Coats AJ, Deharo JC, Delgado V, Diller GP, Israel CW, Keren A, Knops RE, Kotecha D, Leclercq C, Merkely B, Starck C, Thylén I, Tolosana JM. Grupo de trabajo sobre estimulación cardiaca y terapia de resincronización cardiaca de la Sociedad Europea de Cardiología (ESC). Rev Esp Cardiol 2022. [DOI: 10.1016/j.recesp.2021.10.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Glikson M, Nielsen JC, Kronborg MB, Michowitz Y, Auricchio A, Barbash IM, Barrabés JA, Boriani G, Braunschweig F, Brignole M, Burri H, Coats AJS, Deharo JC, Delgado V, Diller GP, Israel CW, Keren A, Knops RE, Kotecha D, Leclercq C, Merkely B, Starck C, Thylén I, Tolosana JM, Leyva F, Linde C, Abdelhamid M, Aboyans V, Arbelo E, Asteggiano R, Barón-Esquivias G, Bauersachs J, Biffi M, Birgersdotter-Green U, Bongiorni MG, Borger MA, Čelutkienė J, Cikes M, Daubert JC, Drossart I, Ellenbogen K, Elliott PM, Fabritz L, Falk V, Fauchier L, Fernández-Avilés F, Foldager D, Gadler F, De Vinuesa PGG, Gorenek B, Guerra JM, Hermann Haugaa K, Hendriks J, Kahan T, Katus HA, Konradi A, Koskinas KC, Law H, Lewis BS, Linker NJ, Løchen ML, Lumens J, Mascherbauer J, Mullens W, Nagy KV, Prescott E, Raatikainen P, Rakisheva A, Reichlin T, Ricci RP, Shlyakhto E, Sitges M, Sousa-Uva M, Sutton R, Suwalski P, Svendsen JH, Touyz RM, Van Gelder IC, Vernooy K, Waltenberger J, Whinnett Z, Witte KK. 2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. Europace 2022; 24:71-164. [PMID: 34455427 DOI: 10.1093/europace/euab232] [Citation(s) in RCA: 140] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Said F, ter Maaten JM, Martens P, Vernooy K, Meine M, Allaart CP, Geelhoed B, Vos MA, Cramer MJ, van Gelder IC, Mullens W, Rienstra M, Maass AH. Aetiology of Heart Failure, Rather than Sex, Determines Reverse LV Remodelling Response to CRT. J Clin Med 2021; 10:jcm10235513. [PMID: 34884215 PMCID: PMC8658308 DOI: 10.3390/jcm10235513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction: Cardiac resynchronization therapy (CRT) is an established therapy for patients with heart failure with reduced ejection fraction (HFrEF). Women appear to respond differently to CRT, yet it remains unclear whether this is inherent to the female sex itself, or due to other patient characteristics. In this study, we aimed to investigate sex differences in response to CRT. Methods: This is a post-hoc analysis of a prospective, multicenter study (MARC) in the Netherlands, studying HFrEF patients with an indication for CRT according to the guidelines (n = 240). Primary outcome measures are left ventricular ejection fraction (LVEF) and left ventricular end systolic volume (LVESV) at 6 months follow-up. Results were validated in an independent retrospective Belgian cohort (n = 818). Results: In the MARC cohort 39% were women, and in the Belgian cohort 32% were women. In the MARC cohort, 70% of the women were responders (defined as >15% decrease in LVESV) at 6 months, compared to 55% of men (p = 0.040) (79% vs. 67% in the Belgian cohort, p = 0.002). Women showed a greater decrease in LVESV %, LVESV indexed to body surface area (BSA) %, and increase in LVEF (all p < 0.05). In regression analysis, after adjustment for BSA and etiology, female sex was no longer associated with change in LVESV % and LVESV indexed to BSA % and LVEF % (p > 0.05 for all). Results were comparable in the Belgian cohort. Conclusions: Women showed a greater echocardiographic response to CRT at 6 months follow-up. However, after adjustment for BSA and ischemic etiology, no differences were found in LV-function measures or survival, suggesting that non-ischemic etiology is responsible for greater response rates in women treated with CRT.
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Affiliation(s)
- Fatema Said
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
| | - Jozine M. ter Maaten
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
- Department of Cardiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium; (P.M.); (W.M.)
| | - Pieter Martens
- Department of Cardiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium; (P.M.); (W.M.)
- Faculty of Medicine and Life Sciences, Biomedical Research Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Kevin Vernooy
- Department of Cardiology, Maastricht University Medical Center, 6200 Maastricht, The Netherlands;
| | - Mathias Meine
- Department of Cardiology, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (M.M.); (M.J.C.)
| | - Cornelis P. Allaart
- Department of Cardiology, VU University Medical Center, 1081 Amsterdam, The Netherlands;
| | - Bastiaan Geelhoed
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
| | - Marc A. Vos
- Department of Medical Physiology, University of Utrecht, 3584 Utrecht, The Netherlands;
| | - Maarten J. Cramer
- Department of Cardiology, University Medical Center Utrecht, 3584 Utrecht, The Netherlands; (M.M.); (M.J.C.)
| | - Isabelle C. van Gelder
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
| | - Wilfried Mullens
- Department of Cardiology, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium; (P.M.); (W.M.)
- Faculty of Medicine and Life Sciences, Biomedical Research Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Michiel Rienstra
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
| | - Alexander H. Maass
- Department of Cardiology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (F.S.); (J.M.t.M.); (B.G.); (I.C.v.G.); (M.R.)
- Correspondence: ; Tel.: +31-50-361-2355
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Glikson M, Nielsen JC, Kronborg MB, Michowitz Y, Auricchio A, Barbash IM, Barrabés JA, Boriani G, Braunschweig F, Brignole M, Burri H, Coats AJS, Deharo JC, Delgado V, Diller GP, Israel CW, Keren A, Knops RE, Kotecha D, Leclercq C, Merkely B, Starck C, Thylén I, Tolosana JM. 2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. Eur Heart J 2021; 42:3427-3520. [PMID: 34455430 DOI: 10.1093/eurheartj/ehab364] [Citation(s) in RCA: 865] [Impact Index Per Article: 288.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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10
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Bifulco SF, Akoum N, Boyle PM. Translational applications of computational modelling for patients with cardiac arrhythmias. Heart 2020; 107:heartjnl-2020-316854. [PMID: 33303478 PMCID: PMC10896425 DOI: 10.1136/heartjnl-2020-316854] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/13/2020] [Accepted: 11/19/2020] [Indexed: 11/04/2022] Open
Abstract
Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Nazem Akoum
- Department of Cardiology, University of Washington, Seattle, Washington, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
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11
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Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis 2020; 13:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023]
Abstract
For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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12
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The Heart by Numbers. Biophys J 2019; 117:E1-E3. [PMID: 31791548 DOI: 10.1016/j.bpj.2019.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 11/14/2019] [Indexed: 11/22/2022] Open
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