Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662864
Veronica Maidel, Maayan Lia Yizraeli Davidovich, Z. Shinar, Tal Klap
Lately, many health systems accelerated their initiatives of advanced remote monitoring systems. Moving to an unattended environment requires overcoming patients' compliance issues and demonstrating the effectiveness of remote monitoring technology. Current Early Warning Scores detection of deterioration, commonly based on spot check EMR data, demonstrates low translational impact from one facility to another. In this study we used vitals collected passively by a sensor, to build a Machine Learning model for timely prediction of deteriorating patients, within 24-hours of their transfer to ICU or death. Time series features, such as trends and vitals' variability were used in conjunction with age & comorbidity data. Evaluating the model yielded an AUROC of 0.81 on data from an inpatient setting, and an AUROC of 0.88 on an independent test set from a COVID-19 unit. The suggested model, based on passive measurement technology, performs equally well as models based on EMR that include nurse inputs. Applying the model on other acute settings (such as a COVID-19 unit) showed similar performance, increasing confidence of its robustness and transferability. The model performance combined with the fact that it does not require human compliance, makes it a good candidate for future testing on home settings.
{"title":"A Prediction Model of In-Patient Deteriorations Based on Passive Vital Signs Monitoring Technology","authors":"Veronica Maidel, Maayan Lia Yizraeli Davidovich, Z. Shinar, Tal Klap","doi":"10.23919/cinc53138.2021.9662864","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662864","url":null,"abstract":"Lately, many health systems accelerated their initiatives of advanced remote monitoring systems. Moving to an unattended environment requires overcoming patients' compliance issues and demonstrating the effectiveness of remote monitoring technology. Current Early Warning Scores detection of deterioration, commonly based on spot check EMR data, demonstrates low translational impact from one facility to another. In this study we used vitals collected passively by a sensor, to build a Machine Learning model for timely prediction of deteriorating patients, within 24-hours of their transfer to ICU or death. Time series features, such as trends and vitals' variability were used in conjunction with age & comorbidity data. Evaluating the model yielded an AUROC of 0.81 on data from an inpatient setting, and an AUROC of 0.88 on an independent test set from a COVID-19 unit. The suggested model, based on passive measurement technology, performs equally well as models based on EMR that include nurse inputs. Applying the model on other acute settings (such as a COVID-19 unit) showed similar performance, increasing confidence of its robustness and transferability. The model performance combined with the fact that it does not require human compliance, makes it a good candidate for future testing on home settings.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132945262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662740
B. Puszkarski, K. Hryniów, G. Sarwas
Introduction: Recurrent Neural Networks are useful tools for the prediction and classification of ECG problems. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture. This study aims to assess if another state-of-the-art solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), can be adopted to diagnose the same cardiac problems. In addition, a comparison is conducted for a different number of electrocardiogram leads. Methods: Two architectures were tested for performance and dimension reduction problems, both in variants consisting of blended branches, allowing retaining accuracy while reducing the computational capacity needed. Results: Our team's (WEAIT) entry was scored incorrectly due to unexpected formatting in outputs; hence only results from cross-validation are presented. LSTM outperforms N-BEATS in terms of multi-label classification, data set resilience, and obtained challenge metrics. Still, N-BEATS can obtain acceptable results and outperforms LSTM in terms of complexity and speed. Conclusions: This paper features a novel approach of using the N-BEATS, which was previously used only for forecasting, to classify ECG signals with success. While N-BEATS multi-label classification capacity is lower than LSTM, its speed allows it to be used on wearable devices.
{"title":"N-BEATS for Heart Dysfunction Classification","authors":"B. Puszkarski, K. Hryniów, G. Sarwas","doi":"10.23919/cinc53138.2021.9662740","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662740","url":null,"abstract":"Introduction: Recurrent Neural Networks are useful tools for the prediction and classification of ECG problems. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture. This study aims to assess if another state-of-the-art solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), can be adopted to diagnose the same cardiac problems. In addition, a comparison is conducted for a different number of electrocardiogram leads. Methods: Two architectures were tested for performance and dimension reduction problems, both in variants consisting of blended branches, allowing retaining accuracy while reducing the computational capacity needed. Results: Our team's (WEAIT) entry was scored incorrectly due to unexpected formatting in outputs; hence only results from cross-validation are presented. LSTM outperforms N-BEATS in terms of multi-label classification, data set resilience, and obtained challenge metrics. Still, N-BEATS can obtain acceptable results and outperforms LSTM in terms of complexity and speed. Conclusions: This paper features a novel approach of using the N-BEATS, which was previously used only for forecasting, to classify ECG signals with success. While N-BEATS multi-label classification capacity is lower than LSTM, its speed allows it to be used on wearable devices.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"68 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114793592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662910
Ataollah Tajabadi, Aditi Roy, M. Varela, O. Aslanidi
Atrial fibrillation (AF) is the most common arrhythmia, but its mechanisms are still unclear. Commonly observed phenomena during AF are epicardial re-entrant drivers (rotors) and breakthrough waves. This study aims to elucidate AF mechanisms, including links between rotors and breakthroughs. We used 3D canine atrial models based on micro-CT reconstruction of biatrial geometry combined with region-specific electrophysiology models. Hence, the 3D model included ionic and structural heterogeneities in the entire atria, with special focus on the right atrium (RA) and pectinate muscles (PM). Results were visualized through 3D atrial membrane voltage maps (VM), 2D isochronal maps (IM), and wave maps (WM). AF episodes were initiated in the atria and were maintained by several epicardial rotors in the PV and RA. Transmural rotors were also seen to propagate through the PM and reemerge at the RA epicardium during these episodes. IM and WM revealed multiple breakthroughs at the region where the PM connect to the RA. The VM simulations, as well as electrogram-based IM and WM, showed that the complex AF patterns seen experimentally can be explained by the interactions of epicardial and transmural rotors.
{"title":"Evolution of Epicardial Rotors into Breakthrough Waves During Atrial Fibrillation in 3D Canine Biatrial Model with Detailed Fibre Orientation","authors":"Ataollah Tajabadi, Aditi Roy, M. Varela, O. Aslanidi","doi":"10.23919/cinc53138.2021.9662910","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662910","url":null,"abstract":"Atrial fibrillation (AF) is the most common arrhythmia, but its mechanisms are still unclear. Commonly observed phenomena during AF are epicardial re-entrant drivers (rotors) and breakthrough waves. This study aims to elucidate AF mechanisms, including links between rotors and breakthroughs. We used 3D canine atrial models based on micro-CT reconstruction of biatrial geometry combined with region-specific electrophysiology models. Hence, the 3D model included ionic and structural heterogeneities in the entire atria, with special focus on the right atrium (RA) and pectinate muscles (PM). Results were visualized through 3D atrial membrane voltage maps (VM), 2D isochronal maps (IM), and wave maps (WM). AF episodes were initiated in the atria and were maintained by several epicardial rotors in the PV and RA. Transmural rotors were also seen to propagate through the PM and reemerge at the RA epicardium during these episodes. IM and WM revealed multiple breakthroughs at the region where the PM connect to the RA. The VM simulations, as well as electrogram-based IM and WM, showed that the complex AF patterns seen experimentally can be explained by the interactions of epicardial and transmural rotors.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116322754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662700
C. Bartolucci, P. Mesirca, Claire Belles, Eugenio Ricci, E. Torre, J. Louradour, M. Mangoni, S. Severi
Nowadays, mathematical modeling has been one of the improvements in technologically advanced science in supporting decision-making in different healthcare scenarios. In the field of numerical modelling of heart electrophysiology, several models of action potential (AP) have been developed for cardiac chambers of different species. The atrioventricular node (AVN) acts as a subsidiary pacemaker and controls impulse conduction between the atria and ventricles. Despite its physiological importance, limited data are available for computing AVN cellular electrophysiology. Further, the ionic mechanisms underlying the automaticity of AVN myocytes are incompletely understood. Only two computational models of AVN have been developed in the last decades (one for rabbit, the other for mouse but without calcium handling). We aimed to develop a new mouse AVN model. We thus build on the preliminary AP mouse AVN model published by Marger et al., which has been updated and improved, by implementing more realistic cellular compartments and calculation of dynamics and handling of intracellular $Ca^{2+}$. The new model reproduces almost all the AVN AP hallmarks and has been used to simulate the effects of blockade of ionic currents involved in AVN pacemaking.
{"title":"A Novel Computational Model of Pacemaker Activity in the Mouse Atrioventricular Node Cell","authors":"C. Bartolucci, P. Mesirca, Claire Belles, Eugenio Ricci, E. Torre, J. Louradour, M. Mangoni, S. Severi","doi":"10.23919/cinc53138.2021.9662700","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662700","url":null,"abstract":"Nowadays, mathematical modeling has been one of the improvements in technologically advanced science in supporting decision-making in different healthcare scenarios. In the field of numerical modelling of heart electrophysiology, several models of action potential (AP) have been developed for cardiac chambers of different species. The atrioventricular node (AVN) acts as a subsidiary pacemaker and controls impulse conduction between the atria and ventricles. Despite its physiological importance, limited data are available for computing AVN cellular electrophysiology. Further, the ionic mechanisms underlying the automaticity of AVN myocytes are incompletely understood. Only two computational models of AVN have been developed in the last decades (one for rabbit, the other for mouse but without calcium handling). We aimed to develop a new mouse AVN model. We thus build on the preliminary AP mouse AVN model published by Marger et al., which has been updated and improved, by implementing more realistic cellular compartments and calculation of dynamics and handling of intracellular $Ca^{2+}$. The new model reproduces almost all the AVN AP hallmarks and has been used to simulate the effects of blockade of ionic currents involved in AVN pacemaking.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116905751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662729
Jakub Hejc, D. Pospisil, Petra Novotna, M. Pešl, O. Janousek, M. Ronzhina, Z. Stárek
Timing pattern of intracardiac atrial activity recorded by multipolar catheter in the coronary sinus (CS) provides insightful information about the type and approximate origin of common non-complex arrhythmias. Depending on the anatomy of the CS, the atrial activity can be substantially disturbed by ventricular far field complex preventing accurate segmentation by convential methods. In this paper, we present small clinically validated database of 326 surface 12-lead and intracardiac electrograms (ECG and IEGs) and a simple deep learning framework for semantic beat-to-beat segmentation of atrial activity in CS recordings. The model is based on a residual convolutional neural network (CNN) combined with pyramidal upsampling decoder. It is capable to recognize well between atrial and ventricular signals recorded by decapolar CS catheter in multiple arrhythmic scenarios reaching dice score of 0.875 on evaluation dataset. To address a dataset size and imbalance issues, we have adopted several preprocessing and learning techniques with adequate evaluation of its impact on the model performance.
{"title":"Segmentation of Atrial Electrical Activity in Intracardiac Electrograms (IECGs) Using Convolutional Neural Network (CNN) Trained on Small Imbalanced Dataset","authors":"Jakub Hejc, D. Pospisil, Petra Novotna, M. Pešl, O. Janousek, M. Ronzhina, Z. Stárek","doi":"10.23919/cinc53138.2021.9662729","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662729","url":null,"abstract":"Timing pattern of intracardiac atrial activity recorded by multipolar catheter in the coronary sinus (CS) provides insightful information about the type and approximate origin of common non-complex arrhythmias. Depending on the anatomy of the CS, the atrial activity can be substantially disturbed by ventricular far field complex preventing accurate segmentation by convential methods. In this paper, we present small clinically validated database of 326 surface 12-lead and intracardiac electrograms (ECG and IEGs) and a simple deep learning framework for semantic beat-to-beat segmentation of atrial activity in CS recordings. The model is based on a residual convolutional neural network (CNN) combined with pyramidal upsampling decoder. It is capable to recognize well between atrial and ventricular signals recorded by decapolar CS catheter in multiple arrhythmic scenarios reaching dice score of 0.875 on evaluation dataset. To address a dataset size and imbalance issues, we have adopted several preprocessing and learning techniques with adequate evaluation of its impact on the model performance.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123989816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662872
H. Jessen, R. V. D. Leur, P. Doevendans, R. V. Es
Portable ECG devices with a reduced number of leads are increasingly being used in clinical practice. As part of the PhysioNet/Computing in Cardiology Challenge 2021, this study aims to develop an algorithm for automated diagnosis of reduced-lead ECGs. We compared separate baseline classifiers for the different lead-subsets with our newly proposed shared classifier. The different models were pre-trained on a physician-annotated dataset of 269,72612-lead ECGs. Fine-tuning was done on the challenge dataset, consisting of 88,243 ECGs. Even though different models showed promising results on the internal pre-training dataset, optimal scores were achieved by the baseline model on the hidden test set. Our team, UMCU, received scores of 0.47, 0.40, 0.41, 0.41, and 0.41 (ranked 14th, 17th, 17th, 17th, and 16th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set.
减少导联数量的便携式心电设备越来越多地用于临床实践。作为PhysioNet/Computing in Cardiology Challenge 2021的一部分，该研究旨在开发一种自动诊断低导联心电图的算法。我们将不同铅子集的单独基线分类器与我们新提出的共享分类器进行了比较。不同的模型在医生注释的269,72612导联心电图数据集上进行预训练。对挑战数据集进行了微调，该数据集由88,243个心电图组成。尽管不同的模型在内部预训练数据集上显示出很好的结果，但基线模型在隐藏测试集上获得了最优分数。我们的UMCU团队在12-lead, 6-lead, 4-lead, 3-lead和2-lead版本的隐藏测试集中获得了0.47,0.40,0.41,0.41和0.41的分数(在39个团队中排名第14,17,17,17和16)。
{"title":"Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier","authors":"H. Jessen, R. V. D. Leur, P. Doevendans, R. V. Es","doi":"10.23919/cinc53138.2021.9662872","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662872","url":null,"abstract":"Portable ECG devices with a reduced number of leads are increasingly being used in clinical practice. As part of the PhysioNet/Computing in Cardiology Challenge 2021, this study aims to develop an algorithm for automated diagnosis of reduced-lead ECGs. We compared separate baseline classifiers for the different lead-subsets with our newly proposed shared classifier. The different models were pre-trained on a physician-annotated dataset of 269,72612-lead ECGs. Fine-tuning was done on the challenge dataset, consisting of 88,243 ECGs. Even though different models showed promising results on the internal pre-training dataset, optimal scores were achieved by the baseline model on the hidden test set. Our team, UMCU, received scores of 0.47, 0.40, 0.41, 0.41, and 0.41 (ranked 14th, 17th, 17th, 17th, and 16th out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127895556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662906
B. D. Maria, F. Perego, G. Cassetti, V. Bari, B. Cairo, F. Gelpi, Monica Parati, L. Vecchia, A. Porta
Among the analytical methods estimating the complexity of the heart period (HP), the linear model-based multiscale complexity (MSC) approach allows the estimation of the complexity over time scales linked to the cardiac autonomic control, i.e. in the low frequency (LF, 0.04-0.15 Hz) and high frequency $(HF, 0.15-0.4 Hz)$ bands. In this study we exploited MSC to evaluate the differences in the HP variability complexity during daytime (DAY) and nighttime (NIGHT) in 23 healthy females (WOMEN, age $36pm 6yrs)$ ) and 21 males (MEN, age $35pm 5yrs)$ performing a 24-hour Holter electrocardiogram. Parametric power spectral analysis was applied as well for comparison. Complexity indexes were computed regardless of the temporal scale (CI) and in the LF and HF bands ( $CI_{LF}$ and $CI_{HF}$, respectively). We found that the power spectral indexes did not differentiate WOMEN and MEN, while CI and $CI_{LF}$ were higher in WOMEN during DAY. The higher HP complexity in females could be explained by a lower sympathetic drive and more complex hormonal regulation than males. We conclude that MSC was more powerful than power spectral analysis in detecting gender differences in HP variability. In addition, as cardiac control differs between females and males, preventive and therapeutic interventions should take gender differences into account.
{"title":"Gender Differences in Short-Term Multiscale Complexity of the Heart Rate Variability","authors":"B. D. Maria, F. Perego, G. Cassetti, V. Bari, B. Cairo, F. Gelpi, Monica Parati, L. Vecchia, A. Porta","doi":"10.23919/cinc53138.2021.9662906","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662906","url":null,"abstract":"Among the analytical methods estimating the complexity of the heart period (HP), the linear model-based multiscale complexity (MSC) approach allows the estimation of the complexity over time scales linked to the cardiac autonomic control, i.e. in the low frequency (LF, 0.04-0.15 Hz) and high frequency $(HF, 0.15-0.4 Hz)$ bands. In this study we exploited MSC to evaluate the differences in the HP variability complexity during daytime (DAY) and nighttime (NIGHT) in 23 healthy females (WOMEN, age $36pm 6yrs)$ ) and 21 males (MEN, age $35pm 5yrs)$ performing a 24-hour Holter electrocardiogram. Parametric power spectral analysis was applied as well for comparison. Complexity indexes were computed regardless of the temporal scale (CI) and in the LF and HF bands ( $CI_{LF}$ and $CI_{HF}$, respectively). We found that the power spectral indexes did not differentiate WOMEN and MEN, while CI and $CI_{LF}$ were higher in WOMEN during DAY. The higher HP complexity in females could be explained by a lower sympathetic drive and more complex hormonal regulation than males. We conclude that MSC was more powerful than power spectral analysis in detecting gender differences in HP variability. In addition, as cardiac control differs between females and males, preventive and therapeutic interventions should take gender differences into account.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130430927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662747
M. Dik, Resi M. Schoonderwoerd, S. Man, A. Maan, C. A. Swenne
Introduction. Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. We sought to validate this tenet by intra-individual comparison of the VG of sinus and ectopic beats, thus assessing both the effects of altered ventricular conduction and of restitution (caused by varying ectopic prematurity). Methods. We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles, who had either normally conducted supraventricular ectopic beats ($SN, N=6$), aberrantly conducted supraventricular ectopic beats ($SA, N=20$), or ventricular ectopic beats ($V, N=92$). We computed the ventricular gradient vectors of the predominant beat, VGp, of the ectopic beat, VGe, the VG difference vector, VGpe, and compared their sizes. Results. The VGe vectors of the SA and $V$ ectopic beats were significantly larger than the VGp vectors. The VGpe vectors were three times larger than the difference in size of the VGe and VGp vectors, demonstrating differences in the VGp and VGe spatial directions. Ectopic prematurity had no influence on these results. Discussion. Electrotonic interactions during repolarization form the likely explanation of our findings. Because of this electrophysiological mechanism, the concept of a conduction-independent ventricular gradient is untenable and cannot be used in ECG diagnostics.
{"title":"Validation of the Ventricular Gradient Comparing Sinus Beats and Ectopic Beats","authors":"M. Dik, Resi M. Schoonderwoerd, S. Man, A. Maan, C. A. Swenne","doi":"10.23919/cinc53138.2021.9662747","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662747","url":null,"abstract":"Introduction. Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. We sought to validate this tenet by intra-individual comparison of the VG of sinus and ectopic beats, thus assessing both the effects of altered ventricular conduction and of restitution (caused by varying ectopic prematurity). Methods. We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles, who had either normally conducted supraventricular ectopic beats ($SN, N=6$), aberrantly conducted supraventricular ectopic beats ($SA, N=20$), or ventricular ectopic beats ($V, N=92$). We computed the ventricular gradient vectors of the predominant beat, VGp, of the ectopic beat, VGe, the VG difference vector, VGpe, and compared their sizes. Results. The VGe vectors of the SA and $V$ ectopic beats were significantly larger than the VGp vectors. The VGpe vectors were three times larger than the difference in size of the VGe and VGp vectors, demonstrating differences in the VGp and VGe spatial directions. Ectopic prematurity had no influence on these results. Discussion. Electrotonic interactions during repolarization form the likely explanation of our findings. Because of this electrophysiological mechanism, the concept of a conduction-independent ventricular gradient is untenable and cannot be used in ECG diagnostics.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129454472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.23919/cinc53138.2021.9662736
F. Margara, B. Rodríguez, Christopher N Toepfer, A. Bueno-Orovio
Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease characterised by hyperdynamic contraction and slowed relaxation. It has been proposed that cellular hypercontractility can derive from mutations that destabilise the energy-conserving myosin super relaxed state, SRX. A new drug, Mavacamten, has been shown to re-stabilise myosin SRX. Here we develop a human-based in-silico model to investigate how disease and drug-induced SRX changes alter cardiac contractility. We do this to mechanistically investigate how Mavacamten restores function in a HCM causing mutation. Our simulations show that hypercontractility is accounted for by an increased availability of crossbridges due to a reduced abundance of myosin SRX, but cellular diastolic dysfunction is only recapitulated if there is a direct crossbridge contribution to thin filament activation. Our model replicates reduced cellular contractility with Mavacamten treatment, which also rescues the hypercontractile phenotype in HCM Our model demonstrates that Mavacamten is effective in correcting HCM abnormalities caused by mutations that destabilise SRX. However, genotypes that cause HCM via other molecular pathways may be incompletely salvaged by Mavacamten.
{"title":"Mavacamten Efficacy in Mutation-specific Hypertrophic Cardiomyopathy: an In Silico Approach to Inform Precision Medicine","authors":"F. Margara, B. Rodríguez, Christopher N Toepfer, A. Bueno-Orovio","doi":"10.23919/cinc53138.2021.9662736","DOIUrl":"https://doi.org/10.23919/cinc53138.2021.9662736","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) is a common genetic heart disease characterised by hyperdynamic contraction and slowed relaxation. It has been proposed that cellular hypercontractility can derive from mutations that destabilise the energy-conserving myosin super relaxed state, SRX. A new drug, Mavacamten, has been shown to re-stabilise myosin SRX. Here we develop a human-based in-silico model to investigate how disease and drug-induced SRX changes alter cardiac contractility. We do this to mechanistically investigate how Mavacamten restores function in a HCM causing mutation. Our simulations show that hypercontractility is accounted for by an increased availability of crossbridges due to a reduced abundance of myosin SRX, but cellular diastolic dysfunction is only recapitulated if there is a direct crossbridge contribution to thin filament activation. Our model replicates reduced cellular contractility with Mavacamten treatment, which also rescues the hypercontractile phenotype in HCM Our model demonstrates that Mavacamten is effective in correcting HCM abnormalities caused by mutations that destabilise SRX. However, genotypes that cause HCM via other molecular pathways may be incompletely salvaged by Mavacamten.","PeriodicalId":126746,"journal":{"name":"2021 Computing in Cardiology (CinC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130471152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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