Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiac disease that affects young adults and has a prevalence estimated as high as 1 in 1000 (Sen-Chowdhry et al., 2010). ARVC is a major cause of ventricular tachycardia (VT), a life-threatening fast heart rhythm that can lead to sudden cardiac death (SCD) and accounts for up to 10% of unexplained SCD cases in people younger than 65 years old (Calkins et al., 2017; Thiene et al., 2007). Catheter ablation, which delivers energy to disrupt abnormal electrical conduction, is a mainstay in treating sustained VT in ARVC, but the success rate of ablation in ARVC patients is only 50–80% (Mathew et al., 2019; Arbelo and Josephson, 2010) and there is a high rate of VT recurrence (Waintraub and Gandjbakhch, 2020). Determining all the locations that could sustain VTs is always challenging in treating ARVC and requires extensive mapping. Difficulty in identifying and targeting all VT circuits could lead to VT recurrence as early as a few months after the initial procedure (Souissi et al., 2018; Mathew et al., 2019). If all VT circuits could be localized and defined prior to ablation, procedural duration could be potentially shortened, and the effectiveness of the procedure in ARVC patients could be significantly enhanced.

Most ARVC cases are associated with mutations in desmosomal genes, including plakophilin-2 (PKP2), desmoglein-2 (DSG2), desmocollin-2 (DSC2), plakoglobin (JUP), and desmoplakin (DSP) (Dalal et al., 2005). Among these, PKP2 is the most common genotype, observed in 60% to 78% of ARVC patients with known pathogenic variants (Mahdieh et al., 2018; Protonotarios et al., 2022; Corrado et al., 2020). The PKP2 protein plays a significant role in maintaining the structural integrity of the ventricular myocardium and in facilitating signal transduction pathways, thus PKP2 pathogenic variants lead to fibrotic remodeling and subsequently to distorted electrical conduction (Vimalanathan et al., 2018). Interestingly, around one-third of all ARVC patients do not have known causal pathogenic variants (Protonotarios et al., 2022; Corrado et al., 2020; James et al., 2021). The pathogenesis of these gene-elusive (GE) patients is highly associated with frequent high-intensity exercises that lead to fibrosis formation on the RV (Benito et al., 2011; Breuckmann et al., 2009; Sawant et al., 2014). Overall, both structural and genotype-modulated electrical abnormalities serve as essential VT substrates in ARVC (El-Battrawy et al., 2018); however, their specific roles in VT circuit formation remain unexplored.

Previous studies from our group have successfully employed patient-specific heart digital twins in investigating VT mechanisms and in predicting VT locations and morphologies to support ablation targeting in ischemic heart diseases (Prakosa et al., 2018; Deng et al., 2019; Sung et al., 2020; Sung et al., 2021). Here, we develop a new personalized genotype-specific heart digital twin approach, Geno-DT, which combines image-based structural information with genotype-specific EP properties at the cell and organ levels. We use the approach to investigate the role of pathophysiological remodeling in ARVC in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of the two main genotypes, PKP2 and GE. The results of this study advance the understanding of arrhythmogenesis in ARVC and offer a pathway to improving VT targeting by ablation.

The Geno-DT approach involves creating three-dimensional (3D) patient-specific electrophysiological (EP) ventricular models incorporating both personalized structural remodeling (diffuse fibrosis and dense scar) constructed from late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) and genotype-specific (GE and PKP2) cellular-level membrane kinetics developed here based on the patient’s genetic testing results. For 16 ARVC patients of the two genotypes, PKP2 and GE, we analyzed VT induction in each personalized heart model to understand the arrhythmogenic mechanisms, and specifically, the contributions of structural remodeling and genotype-modulated EP alterations to sustaining VT reentrant circuits in ARVC. The Geno-DT approach was used to predict rapid-pacing induced VT circuit locations in these patients, which would constitute the ideal targets for ablation. The accuracy of the predictions was demonstrated by comparison to the VT circuits induced in clinical EP studies (EPS). An overview of our study is presented in Figure 1.

Figure 1

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EP properties of the PKP2 cell model

Our Geno-DT approach incorporates, in the patient-specific computational models, cellular EP that is distinct between the two genotypes, PKP2 and GE. The cell-level electrical behavior for the GE genotype was modeled based on the ten Tusscher and Panfilov, 2006 (TT2) human ventricular model (see Methods). Since there is currently no cell model representation of PKP2 pathogenic variant that can be used in organ-level simulation in terms of computational tractability, we developed one here, by modifying the GE cell model to represent remodeling in the sodium currents and calcium-handling as reported in experimental studies (Sato et al., 2009; Kim et al., 2019; Lyon et al., 2021). Details on the PKP2 cell model implementation can be found in Materials and methods.

The new PKP2 model includes a downscaled maximum conductance for the sodium current (I_Na), resulting in a peak inward sodium current at a membrane potential of –36 mV that is truncated from –502.71 pA/pF in the GE model to –171.56 pA/pF in the PKP2 model (Figure 2A). Additionally, the PKP2 cell model includes various calcium currents, each modified from the baseline GE model (see Methods). These changes collectively produce an altered calcium transient (CaT) with a larger area under the curve and a faster decay, giving it a more acute shape (Figure 2B). The CaT in the PKP2 cell model has a longer time to peak (32 vs 27 ms), a significantly higher peak (0.0023 vs 0.00084 mM), and a shorter time to 90% return from peak (319 vs 379 ms).

Figure 2

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The resulting action potential (AP) shape of the PKP2 cell is distinct from that of the GE cell, as depicted in Figure 2C. The downregulation of I_Na results in a lower upstroke peak and slower maximum upstroke velocity (V_max). The changes to calcium handling led to an elevated resting membrane potential and longer AP duration at 90% depolarization (APD₉₀). These differences in AP behavior between the two cells underlie the organ-level EP differences between PKP2 and GE genotypes.

Since in this study we used a rapid pacing protocol to induce VTs in the personalized ARVC models, cell-level restitution properties, and specifically the difference in restitution properties between the PKP2 and GE cell models, have important consequences for ARVC arrhythmogenesis. Figure 3 examines these restitution properties. The GE cell model exhibited a decrease in APD₉₀ with diastolic interval (DI) shortening (Figure 3A), consistent with normal calcium-handling behavior (Franz, 2003). In contrast, the PKP2 cell model’s AP showed some shortening of phase two duration but with very little reduction in plateau amplitude, resulting in more triangular APs at fast pacing without much change in total duration.

Figure 3

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Figure 3B highlights the differences in restitution curves for the PKP2 and GE cell models. Notably, at the fast-pacing intervals of the steep initial phase (25–160 ms), the two restitution curves diverge significantly. While the GE cell APD₉₀ continued to decrease to 148ms before loss-of-capture, the PKP2 cell APD₉₀ only decreased to 208 ms. The maximum slopes of the restitution curves were measured to be 1.16 and 0.59 for the GE and PKP2 cell models, respectively. The substantially lower slope for the PKP2 cell’s restitution curve indicated a reduced ability of its AP to adapt to fast pacing rates.

The GE and PKP2 cell models described above were incorporated in LGE-based geometrical models of the patients’ ventricles from the respective patient groups. In addition to EP remodeling, ARVC is also characterized by structural remodeling, i.e the presence of dense scar and diffuse fibrosis. While replacement scar is non-conductive, myocardium in the diffuse fibrosis regions exhibits altered EP properties, which were represented in the personalized ventricular models by additional alterations in cell-level properties in these regions, as we have done previously (Shade et al., 2020; O’Hara et al., 2022); see Materials and methods for detail. The resulting heart models of ARVC patients thus represented both EP and structural remodeling in ARVC.

Analysis of structural remodeling distribution and its relationship with VT inducibility for different genotype patient groups

Here, we first utilized the LGE-reconstructed ventricular geometrical models to examine the distribution of structural remodeling in various regions of the RV and its relationship to VT. We found that across the entire ARVC cohort, the diffuse fibrosis was most abundant at the base of the RV and least abundant at the RV apex (Figure 4A). There were significant differences in the amount of diffuse fibrosis between the basal, mid, and apical regions (14.01 ± 4.37% for basal RV vs 5.41 ± 2.61% for mid RV vs 1.13 ± 1.71% for apical RV, p < 0.0001 for pairwise comparisons). A similar trend was observed for dense scar amounts with significant differences between the basal, mid, and apical regions (5.51 ± 2.71% vs 1.74 ± 1.99% vs 0.42 ± 0.90%, p < 0.05 for pairwise comparisons; Figure 4B). There was no significant difference when comparing the amounts of structural remodeling between anterior, lateral, and posterior regions.

Figure 4

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We found that the distribution of diffuse fibrosis was different in the two patient groups, with GE patients having a significantly higher amount of diffuse fibrosis burden than PKP2 patients in the basal anterior wall (6.07 ± 3.18% vs 1.85 ± 0.017%, p < 0.01 for pairwise comparisons) and basal lateral wall (5.77 ± 2.34% vs 3.24 ± 1.40%, p < 0.05 for pairwise comparisons), and a significantly lower amount in the basal posterior wall (3.36 ± 2.92% vs 7.73 ± 4.38%, p < 0.05 for pairwise comparisons; Figure 4C). There was no statistical significance in the dense scar distribution between the two genotype groups.

Next, we performed simulations, with the personalized Geno-DT heart models, of VT induction following rapid pacing (see Materials and methods) and evaluated the induced VT circuits in the entire cohort. We performed a total of 144 ventricular simulations (16 patients ✕ 9 pacing locations) to determine the induced VT circuit morphologies. We examined the correlations between the structural remodeling features (diffuse fibrosis volume [DF], dense scar volume [DS], total fibrotic remodeling volume (both scar and diffuse fibrosis) [TFV]) and the number of unique VT morphologies induced during EPS across the different RV AHA segments. Figure 5 summarizes pairwise correlations between these variables for both GE and PKP2 patient groups. Our analysis revealed a highly positive correlation between the number of VTs induced during EPS and the volume of each type of structural remodeling (both DF and DS) and total fibrotic volume (TFV) in the GE patient group (r = 0.91, 0.91, 0.92 for DF, DS and TFV respectively), while in the PKP2 group, we observed only moderate correlation (r = 0.65, 0.7, 0.7 for DF, DS and TFV respectively).

Figure 5

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Importantly, we found a strong correlation between VTs induced during EPS and those induced in simulations within the different RV regions in both patient groups (r = 0.99 for both groups), indicating that our heart digital twins effectively capture the occurrence pattern of EPS-induced VTs. Notably, although the structural remodeling in the PKP2 group was less correlated with EPS VTs as compared to the GE group, the VTs induced in the PKP2 models still exhibited high correlation with the VTs recorded during EPS.

These findings suggest that structural remodeling plays a more important role in VT arrhythmogenesis in GE patients than in PKP2 patients. The moderate correlation in the PKP2 group indicates that additional factors contribute to VT, specifically the EP remodeling associated with the PKP2 pathogenic variants.

Predicting VT circuits using Geno-DT

We next assessed the predictive capability of the Geno-DT approach in determining accurately the VT circuit numbers and locations in the two ARVC genotype groups. In Figure 6, the bullseye plots summarize the number of unique VT morphologies induced during EPS and those induced in Geno-DTs for each RV AHA segment in all 16 ARVC patients. In the GE patient group, Geno-DT resulted in 28 distinct VTs being induced, 25 of which were observed during EPS. The PKP2 group showed comparable results, with 25 VT morphologies induced in PKP2 ARVC models and 25 reported in the EPS record. In terms of VT circuit locations, Geno-DT captured all VT locations that were observed during EPS except for one VT on the mid lateral RV wall in the GE group. In the PKP2 group, Geno-DT predicted all the VT locations recorded during EPS, except for one VT on the basal anterior RV wall and two on the basal posterior wall. Hence, in both cases, Geno-DT accurately predicted the locations of nearly all the VTs induced during EPS.

Figure 6

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Additionally, for each individual ARVC patient, we compared the AHA segment of every VT circuit induced in each Geno-DT to the patient’s EPS VT record obtained from EPS. In Table 2, we summarized the capability of Geno-DT in predicting VT locations. The results showed outstanding accuracy, sensitivity, and specificity in both GE and PKP2 patient groups.

Table 2

	GE patient group (n = 8)	PKP2 patient group (n = 8)
Sensitivity	1.00	0.86
Specificity	0.94	0.90
Accuracy	0.96	0.89
Error rate	0.042	0.11
F1 score	0.89	0.83
MCC	0.90	0.76

Figure 7A and B illustrate the above findings with two examples, one from GE and one from PKP2 group. For the GE patient (Figure 7A), the genotype-matched model induced three VT morphologies: two figure-of-eights on the anterolateral and posterolateral RV wall and a single-reentry VT on the posterior RV wall. This GE patient had two VT circuits observed during the clinical EPS, each one on the basal and mid lateral wall of the patient’s RV. Although the clinical EPS report did not mention VT induced on the posterior RV wall, it did mention that fractionated potentials, which are considered as VT circuit indicators, were observed in the posterior RV, and thus, clinical ablation was done there. Therefore, we consider all our predicted VTs in this patient in agreement with the clinical findings. Similarly, for the PKP2 patient, the genotype-matched model induced five VTs: a figure-of-eight reentry on the anterolateral wall, a single-reentry VT and two figure-of-eight VTs on the lateral RV wall, as well a single-reentry VT on the posterior RV wall (Figure 7B). This PKP2 patient underwent two clinical ablation procedures within one year. The first ablation identified three VTs, one in the mid lateral region, one in the basal posterolateral region and one in the basal posterior region. The EPS procedure during the second ablation identified two sustained VTs on the anterolateral and lateral wall. These examples showcase the excellent correspondence between Geno-DT VT circuit predictions and clinical observations.

Figure 7

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The role of genotype-specific EP remodeling in VT propensity

Once we ascertained the ability of Geno-DT to correctly predict VT circuits in ARVC patients, we investigated the contribution of pathogenic variants (via the corresponding genotype-specific EP properties) in creating propensity to VT. To do so, we switched the cellular models between GE and PKP2 patient groups to introduce a mismatch between personalized structural remodeling and genotype-specific EP properties. We then repeated the simulations under these mismatched conditions. Figure 8C provides a summary of the findings. We also present examples in Figure 8A and B, which are the same two patients’ ventricular models included in Figure 7 but simulated under genotype-mismatched conditions. Evident from the figure is that VT reentrant circuits were also induced under genotype-mismatched conditions, however, they are distinctly different from those in the genotype-matched models, and very different from the VTs induced during EPS. In Figure 8A, the mismatched GE DT had only one VT circuit induced on the posterolateral RV wall, and in Figure 8B the mismatched PKP2 DT presented only one VT in the anterolateral RV wall. For both the GE and PKP2 examples, the genotype-mismatched DTs missed many VT circuits induced during EPS. The bullseye plots in Figure 8C emphasize the underprediction of the genotype-mismatched Geno-DTs, in comparison to those in Figure 6.

Figure 8

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Mechanisms by which genotype-specific EP remodeling alters VT circuits

To gain a better understanding of the mechanisms underlying the induction of different reentrant circuits resulting from EP remodeling, we analyzed in detail the VT circuits simulated under genotype-matched and genotype-mismatched conditions. Two examples of this analysis are presented in Figure 9 for the PKP2 ventricular model from Figure 7B. Animation of the VT circuits propagation is available in Figure 9—video 1.

Figure 9 with 1 supplement see all

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Reentrant circuit 1 is the VT induced in the lateral RV wall of the PKP2 (genotype-matched) DT (Figure 9A). This circuit involved two dense scar islands surrounded by diffuse fibrosis, creating an isthmus of low conductivity in between. The initial wavefront elicited from a basal posterior pacing site propagated around the dense scar and through the isthmus. As the exiting wavefront encountered non-fibrotic myocardium, it had a slow conduction due to the lowered upstroke velocity and elevated resting membrane potential resulting from PKP2 EP remodeling. Consequently, downstream tissue failed to be excited due to source-sink mismatch. Two other wavefronts propagated from either side of the dense scar islands into this unexcited myocardium and collided. In the time it took for the two opposing wavefronts to meet, the non-fibrotic myocardium at the entrance of the isthmus remained refractory (PKP2 cell model has extended refractoriness), thereby allowing the collided wavefront to travel back up the isthmus, meet refractory tissue and ultimately form the figure-of-eight reentry. In contrast, no reentry was induced at this same location with the incorporation of GE EP properties (Figure 9B). Specifically, as the wavefront left the isthmus and encountered non-fibrotic myocardium, its propagation speed was higher due to the GE cell model’s rapid upstroke velocity and unchanged resting membrane potential. Because of the fast conduction velocity, the isthmus did not have time to recover, thereby excitation occurred only of tissue ahead of the wavefront.

Reentrant circuit 2 was induced in the posterior RV wall of the PKP2 genotype-matched DT (Figure 9C). The wavefront elicited from the same basal posterior pacing site had slowed down significantly while traveling through a band of diffuse fibrosis. In areas where the band was wide, the conduction velocity decreased to the point where the wavefront was unable to excite non-fibrotic myocardium due to source-sink mismatch. This was a result of the fact that the PKP2 cells have slowed upstroke velocity and elevated resting membrane potential. An adjacent wavefront was thereby able to turn into this unexcited region and initiate reentry. In contrast, no reentry occurred at this location in the GE DT (Figure 9D). The GE cell’s rapid upstroke velocity allowed the wavefront to excite the non-fibrotic myocardium after exiting the wide diffuse fibrosis band. Consequently, the wavefront leaving the diffuse fibrosis was more planar, avoiding source-sink mismatch, and did not result in reentry.

Together, these results help illustrate the contribution of genotype-based EP differences to the variability of organ-level wavefront propagation patterns.

This study presents a novel heart digital-twin approach called Geno-DT, tailored to predict VT reentrant circuits in patients with different ARVC genotypes. We demonstrated that Geno-DT has excellent capability in predicting VT reentrant circuits non-invasively for patients with both gene-elusive and PKP2 positive genotypes. In predicting VT circuits, the study also reveals new mechanistic insights regarding VT arrhythmogenesis in ARVC. By comparing the VT circuits predicted by Geno-DT to the clinical EPS observations, we demonstrated that Geno-DT has high sensitivity, specificity, and accuracy in predicting non-invasively VT circuits and their locations in both GE and PKP2 genotypes (100%, 94%, 96% in GE patient group; 86%, 90%, 89% in PKP2 patient group). The Geno-DT approach thus has the potential to improve pre-ablation planning and to lead to tailored personalized ablation strategies. Our study further charts a pathway towards bringing computational modeling in clinical decision-making thus augmenting precision medicine approaches in cardiology.

With its incorporation of genetic EP information, Geno-DT is a novel development in heart digital twin applications. Although previous studies have included cellular remodeling in predicting VT risk for non-ischemic cardiac diseases such as hypertrophic cardiomyopathy and Tetralogy of Fallot (Shade et al., 2020; O’Hara et al., 2022), personal genetics were never considered. ARVC is associated with several genotypes, each causing specific pathological alterations to ion channels (Ohno, 2016). However, it has remained unclear how the altered cellular EP is reflected at the organ and how it affects the likelihood of VT occurrence. Our study addresses these questions by developing a PKP2 cell model that incorporates the EP effects of pathogenic variants and is compatible with whole-heart modeling. While a previous study had incorporated PKP2 cell EP properties in a computational cell model (Lyon et al., 2021), the model was too complex to be incorporated into whole-heart simulations due to its focus on describing subcellular phenomena.

Our study offers new insights into the mechanisms underlying VT propensity in patients with ARVC. While previous clinical observations have suggested that sustained VTs are strongly associated with fibrotic remodeling in the basal anterior RV, it has been unclear whether this association holds true between the GE and PKP2 genotypes (Basso et al., 2008; Corrado et al., 2005). Studies of ARVC structural substrates have typically relied on electroanatomical mapping and biopsy, which do not provide a complete characterization of the 3D fibrosis distribution in the right ventricle (Te Riele et al., 2013; Basso et al., 2009; Corrado et al., 2005). Here, we reconstructed heart digital twins using LGE-CMR to quantify the extent and distribution of fibrotic remodeling in each patient’s heart. We found that PKP2 patients with clinical sustained VTs had more fibrotic remodeling in the basal posterior region of RV than the anterior, while the fibrosis distribution in the GE group was consistent with previous findings. These results demonstrate that different ARVC genotypes exhibit distinct distributions of fibrotic remodeling. Furthermore, while the number of induced VT morphologies was similar between GE and PKP2 patients, our analysis revealed a weaker correlation between the locations of VTs induced during EPS and distribution of fibrosis in the PKP2 group compared to the GE group. This suggests that, in addition to structural remodeling, unique PKP2-specific changes in cellular EP properties may contribute to the development of VT in PKP2 patients.

Our findings further elucidate the important role of genotype-specific EP remodeling in sustaining VT circuits in ARVC. Previous study has shown that a decrease in V_max directly affects the conduction velocity in the cardiac tissue (Issa et al., 2019). Consistent with this relationship, we observed that the reduced V_max in the PKP2 cell model, caused by downregulated I_Na, resulted in slower wavefront conduction, particularly in non-fibrotic regions adjacent to the dense fibrosis. The different restitution properties of the PKP2 cell model resulting from the altered calcium handling also contributed to the formation of reentry. The rapid pacing rates used to induce VT corresponded to the steep initial phase of the restitution curves, where PKP2 cell model had extended refractoriness compared to the GE cell. Hence in PKP2 hearts, extended refractoriness and decreased conduction velocity resulted in wavefronts of higher curvature, subsequent source-sink mismatch and conduction block, setting the stage for arrhythmogenesis.

We identify several potential benefits of using Geno-DT in ARVC clinical management. Firstly, it could save time during the ablation procedure as all possible locations that sustain VTs could be known prior to the procedure. Secondly, it provides a new approach for substrate modification in ARVC ablation, in which the substrate is targeted at predicted VT locations instead of ablating fibrotic tissue or all regions with voltage abnormalities on electroanatomical mapping, as not all of these are arrhythmogenic (Rottmann et al., 2019). The latter ablation approaches could lead to excessive lesions, which have negative consequences on cardiac function, especially in ARVC, as it is characterized by abnormal inflammation response (Asatryan et al., 2021; Briceño et al., 2020; Santangeli et al., 2015). Lastly, our study demonstrated that the underlying mechanisms of sustaining VT reentrant circuits differ among various ARVC genotypes. This finding underscores the importance of genotype-specific ARVC management in clinical practice, which can be facilitated by the Geno-DT approach.

The success of our Geno-DT approach in predicting VT circuit locations underscores its potential for translation to the clinical setting. As the understanding of human genetic variation underlying cardiovascular diseases continues to evolve with advances in functional genomics (Li et al., 2022), Geno-DT could provide a generalized framework to integrate multimodal clinical data and improve precision health for each individual patient.

Patient-derived data including CMR images are not publicly available to respect patient privacy. Interested parties wishing to obtain these data for non-commercial reuse should contact the corresponding author via email; the request will need to be approved by IRB. The image processing software CardioViz3D can be freely obtained from http://www-sop.inria.fr/asclepios/software/CardioViz3D. The cell models are freely available from the repository CellML (https://models.cellml.org/exposure/de5058f16f829f91a1e4e5990a10ed71). Documentation and instructions on the use of the openCARP cardiac electrophysiology simulator and meshalyzer visualization software are available via https://opencarp.org/.

1. 1. Arbelo E
   2. Josephson ME

   (2010) Ablation of ventricular arrhythmias in arrhythmogenic right ventricular dysplasia

   Journal of Cardiovascular Electrophysiology 21:473–486.

   https://doi.org/10.1111/j.1540-8167.2009.01694.x

   • PubMed
   • Google Scholar
2. 1. Arevalo HJ
   2. Vadakkumpadan F
   3. Guallar E
   4. Jebb A
   5. Malamas P
   6. Wu KC
   7. Trayanova NA

   (2016) Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models

   Nature Communications 7:11437.

   https://doi.org/10.1038/ncomms11437

   • PubMed
   • Google Scholar
3. 1. Asatryan B
   2. Asimaki A
   3. Landstrom AP
   4. Khanji MY
   5. Odening KE
   6. Cooper LT
   7. Marchlinski FE
   8. Gelzer AR
   9. Semsarian C
   10. Reichlin T
   11. Owens AT
   12. Chahal CAA

   (2021) Inflammation and immune response in arrhythmogenic cardiomyopathy: State-of-the-art review

   Circulation 144:1646–1655.

   https://doi.org/10.1161/CIRCULATIONAHA.121.055890

   • PubMed
   • Google Scholar
4. 1. Basso C
   2. Ronco F
   3. Marcus F
   4. Abudureheman A
   5. Rizzo S
   6. Frigo AC
   7. Bauce B
   8. Maddalena F
   9. Nava A
   10. Corrado D
   11. Grigoletto F
   12. Thiene G

   (2008) Quantitative assessment of endomyocardial biopsy in arrhythmogenic right ventricular cardiomyopathy/dysplasia: an in vitro validation of diagnostic criteria

   European Heart Journal 29:2760–2771.

   https://doi.org/10.1093/eurheartj/ehn415

   • PubMed
   • Google Scholar
5. 1. Basso C
   2. Corrado D
   3. Marcus FI
   4. Nava A
   5. Thiene G

   (2009) Arrhythmogenic right ventricular cardiomyopathy

   Lancet 373:1289–1300.

   https://doi.org/10.1016/S0140-6736(09)60256-7

   • PubMed
   • Google Scholar
6. 1. Bayer JD
   2. Blake RC
   3. Plank G
   4. Trayanova NA

   (2012) A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models

   Annals of Biomedical Engineering 40:2243–2254.

   https://doi.org/10.1007/s10439-012-0593-5

   • PubMed
   • Google Scholar
7. 1. Benito B
   2. Gay-Jordi G
   3. Serrano-Mollar A
   4. Guasch E
   5. Shi Y
   6. Tardif J-C
   7. Brugada J
   8. Nattel S
   9. Mont L

   (2011) Cardiac arrhythmogenic remodeling in a rat model of long-term intensive exercise training

   Circulation 123:13–22.

   https://doi.org/10.1161/CIRCULATIONAHA.110.938282

   • PubMed
   • Google Scholar
8. 1. Breuckmann F
   2. Möhlenkamp S
   3. Nassenstein K
   4. Lehmann N
   5. Ladd S
   6. Schmermund A
   7. Sievers B
   8. Schlosser T
   9. Jöckel K-H
   10. Heusch G
   11. Erbel R
   12. Barkhausen J

   (2009) Myocardial late gadolinium enhancement: prevalence, pattern, and prognostic relevance in marathon runners

   Radiology 251:50–57.

   https://doi.org/10.1148/radiol.2511081118

   • PubMed
   • Google Scholar
9. 1. Briceño DF
   2. Liang JJ
   3. Shirai Y
   4. Markman TM
   5. Chahal A
   6. Tschabrunn C
   7. Zado E
   8. Hyman MC
   9. Kumareswaran R
   10. Arkles JS
   11. Santangeli P
   12. Schaller RD
   13. Supple GE
   14. Frankel DS
   15. Deo R
   16. Riley MP
   17. Nazarian S
   18. Lin D
   19. Epstein AE
   20. Garcia FC
   21. Dixit S
   22. Callans DJ
   23. Marchlinski FE

   (2020) Characterization of structural changes in arrhythmogenic right ventricular cardiomyopathy with recurrent ventricular tachycardia after ablation: Insights from repeat electroanatomic voltage mapping

   Circulation. Arrhythmia and Electrophysiology 13:e007611.

   https://doi.org/10.1161/CIRCEP.119.007611

   • PubMed
   • Google Scholar
10. 1. Calkins H
    2. Corrado D
    3. Marcus F

    (2017) Risk stratification in arrhythmogenic right ventricular cardiomyopathy

    Circulation 136:2068–2082.

    https://doi.org/10.1161/CIRCULATIONAHA.117.030792

    • PubMed
    • Google Scholar
11. 1. Cartoski MJ
    2. Nikolov PP
    3. Prakosa A
    4. Boyle PM
    5. Spevak PJ
    6. Trayanova NA

    (2019) Computational identification of ventricular arrhythmia risk in pediatric myocarditis

    Pediatric Cardiology 40:857–864.

    https://doi.org/10.1007/s00246-019-02082-7

    • PubMed
    • Google Scholar
12. 1. Coppini R
    2. Ferrantini C
    3. Del Lungo M
    4. Stillitano F
    5. Sartiani L
    6. Tosi B
    7. Suffredini S
    8. Tesi C
    9. Poggesi C
    10. Cerbai E
    11. Mugelli A
    12. Yao L
    13. Fan P
    14. Belardinelli L
    15. Yacoub M
    16. Olivotto I

    (2013) Response to letter regarding article, “Late sodium current inhibition reverses electromechanical dysfunction in human hypertrophic cardiomyopathy.”

    Circulation 128:575–584.

    https://doi.org/10.1161/CIRCULATIONAHA.113.004016

    • PubMed
    • Google Scholar
13. 1. Corrado D
    2. Basso C
    3. Leoni L
    4. Tokajuk B
    5. Bauce B
    6. Frigo G
    7. Tarantini G
    8. Napodano M
    9. Turrini P
    10. Ramondo A
    11. Daliento L
    12. Nava A
    13. Buja G
    14. Iliceto S
    15. Thiene G

    (2005) Three-dimensional electroanatomic voltage mapping increases accuracy of diagnosing arrhythmogenic right ventricular cardiomyopathy/dysplasia

    Circulation 111:3042–3050.

    https://doi.org/10.1161/CIRCULATIONAHA.104.486977

    • PubMed
    • Google Scholar
14. 1. Corrado D
    2. Perazzolo Marra M
    3. Zorzi A
    4. Beffagna G
    5. Cipriani A
    6. Lazzari MD
    7. Migliore F
    8. Pilichou K
    9. Rampazzo A
    10. Rigato I
    11. Rizzo S
    12. Thiene G
    13. Anastasakis A
    14. Asimaki A
    15. Bucciarelli-Ducci C
    16. Haugaa KH
    17. Marchlinski FE
    18. Mazzanti A
    19. McKenna WJ
    20. Pantazis A
    21. Pelliccia A
    22. Schmied C
    23. Sharma S
    24. Wichter T
    25. Bauce B
    26. Basso C

    (2020) Diagnosis of arrhythmogenic cardiomyopathy: the padua criteria

    International Journal of Cardiology 319:106–114.

    https://doi.org/10.1016/j.ijcard.2020.06.005

    • PubMed
    • Google Scholar
15. 1. Dalal D
    2. Nasir K
    3. Bomma C
    4. Prakasa K
    5. Tandri H
    6. Piccini J
    7. Roguin A
    8. Tichnell C
    9. James C
    10. Russell SD
    11. Judge DP
    12. Abraham T
    13. Spevak PJ
    14. Bluemke DA
    15. Calkins H

    (2005) Arrhythmogenic right ventricular dysplasia: a United States experience

    Circulation 112:3823–3832.

    https://doi.org/10.1161/CIRCULATIONAHA.105.542266

    • PubMed
    • Google Scholar
16. 1. Deng D
    2. Prakosa A
    3. Shade J
    4. Nikolov P
    5. Trayanova NA

    (2019) Characterizing conduction channels in postinfarction patients using a personalized virtual heart

    Biophysical Journal 117:2287–2294.

    https://doi.org/10.1016/j.bpj.2019.07.024

    • PubMed
    • Google Scholar
17. 1. El-Battrawy I
    2. Zhao Z
    3. Lan H
    4. Cyganek L
    5. Tombers C
    6. Li X
    7. Buljubasic F
    8. Lang S
    9. Tiburcy M
    10. Zimmermann W-H
    11. Utikal J
    12. Wieland T
    13. Borggrefe M
    14. Zhou X-B
    15. Akin I

    (2018) Electrical dysfunctions in human-induced pluripotent stem cell-derived cardiomyocytes from a patient with an arrhythmogenic right ventricular cardiomyopathy

    Europace 20:f46–f56.

    https://doi.org/10.1093/europace/euy042

    • PubMed
    • Google Scholar
18. 1. Franz MR

    (2003) The electrical restitution curve revisited

    Journal of Cardiovascular Electrophysiology 14:S140–S147.

    https://doi.org/10.1046/j.1540.8167.90303.x

    • Google Scholar
19. Book

    1. Issa ZF
    2. Miller JM
    3. Zipes DP

    (2019)

    1 - molecular mechanisms of cardiac electrical

    In: Issa ZF, Miller JM, Zipes DP, editors. Clinical Arrhythmology and Electrophysiology. Philadelphia: Elsevier. pp. 1–14.

    • Google Scholar
20. 1. James CA
    2. Jongbloed JDH
    3. Hershberger RE
    4. Morales A
    5. Judge DP
    6. Syrris P
    7. Pilichou K
    8. Domingo AM
    9. Murray B
    10. Cadrin-Tourigny J
    11. Lekanne Deprez R
    12. Celeghin R
    13. Protonotarios A
    14. Asatryan B
    15. Brown E
    16. Jordan E
    17. McGlaughon J
    18. Thaxton C
    19. Kurtz CL
    20. van Tintelen JP

    (2021) International evidence based reappraisal of genes associated with arrhythmogenic right ventricular cardiomyopathy using the clinical genome resource framework

    Circulation. Genomic and Precision Medicine 14:e003273.

    https://doi.org/10.1161/CIRCGEN.120.003273

    • PubMed
    • Google Scholar
21. 1. Kim J-C
    2. Pérez-Hernández M
    3. Alvarado FJ
    4. Maurya SR
    5. Montnach J
    6. Yin Y
    7. Zhang M
    8. Lin X
    9. Vasquez C
    10. Heguy A
    11. Liang F-X
    12. Woo S-H
    13. Morley GE
    14. Rothenberg E
    15. Lundby A
    16. Valdivia HH
    17. Cerrone M
    18. Delmar M

    (2019) Disruption of Ca²⁺_i Homeostasis and Connexin 43 Hemichannel Function in the Right Ventricle Precedes Overt Arrhythmogenic Cardiomyopathy in Plakophilin-2-Deficient Mice

    Circulation 140:1015–1030.

    https://doi.org/10.1161/CIRCULATIONAHA.119.039710

    • PubMed
    • Google Scholar
22. 1. Li T
    2. Ersaro N
    3. Strober BJ
    4. Arvanitis M
    5. Duong T
    6. Brody J
    7. Lin HJ
    8. Taylor KD
    9. Rotter JI
    10. Rich SS
    11. Sootedehnia N
    12. Arking DE
    13. Montgomery SB
    14. Battle A
    15. NHLBI TOPMed Consortium

    (2022) Abstract 11946: Prioritization of Multiomic Rare Variants (RVs) Underlying Electrocardiogram (EKG) Traits Using Bayesian Hierarchical Modeling (“Watershed”)

    Circulation 146:A11946.

    https://doi.org/10.1161/circ.146.suppl_1.11946

    • Google Scholar
23. 1. Lyon A
    2. van Opbergen CJM
    3. Delmar M
    4. Heijman J
    5. van Veen TAB

    (2021) In silico identification of disrupted myocardial calcium homeostasis as proarrhythmic trigger in arrhythmogenic cardiomyopathy

    Frontiers in Physiology 12:732573.

    https://doi.org/10.3389/fphys.2021.732573

    • PubMed
    • Google Scholar
24. 1. Mahdieh N
    2. Saedi S
    3. Soveizi M
    4. Rabbani B
    5. Najafi N
    6. Maleki M

    (2018) A novel PKP2 mutation and intrafamilial phenotypic variability in ARVC/D

    Medical Journal of the Islamic Republic of Iran 32:5.

    https://doi.org/10.14196/mjiri.32.5

    • PubMed
    • Google Scholar
25. 1. Marchlinski DF
    2. Tschabrunn CM
    3. Zado ES
    4. Santangeli P
    5. Marchlinski FE

    (2021) Right bundle branch block ventricular tachycardia in arrhythmogenic right ventricular cardiomyopathy more commonly originates from the right ventricle: criteria for identifying chamber of origin

    Heart Rhythm 18:163–171.

    https://doi.org/10.1016/j.hrthm.2020.08.016

    • PubMed
    • Google Scholar
26. 1. Mathew S
    2. Saguner AM
    3. Schenker N
    4. Kaiser L
    5. Zhang P
    6. Yashuiro Y
    7. Lemes C
    8. Fink T
    9. Maurer T
    10. Santoro F
    11. Wohlmuth P
    12. Reißmann B
    13. Heeger CH
    14. Tilz R
    15. Wissner E
    16. Rillig A
    17. Metzner A
    18. Kuck K-H
    19. Ouyang F

    (2019) Catheter ablation of ventricular tachycardia in patients with arrhythmogenic right ventricular cardiomyopathy/dysplasia: a sequential approach

    Journal of the American Heart Association 8:e010365.

    https://doi.org/10.1161/JAHA.118.010365

    • PubMed
    • Google Scholar
27. 1. O’Hara T
    2. Virág L
    3. Varró A
    4. Rudy Y

    (2011) Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation

    PLOS Computational Biology 7:e1002061.

    https://doi.org/10.1371/journal.pcbi.1002061

    • PubMed
    • Google Scholar
28. 1. O’Hara RP
    2. Binka E
    3. Prakosa A
    4. Zimmerman SL
    5. Cartoski MJ
    6. Abraham MR
    7. Lu D-Y
    8. Boyle PM
    9. Trayanova NA

    (2022) Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy

    eLife 11:e73325.

    https://doi.org/10.7554/eLife.73325

    • PubMed
    • Google Scholar
29. 1. Ohno S

    (2016) The genetic background of arrhythmogenic right ventricular cardiomyopathy

    Journal of Arrhythmia 32:398–403.

    https://doi.org/10.1016/j.joa.2016.01.006

    • PubMed
    • Google Scholar
30. 1. Plank G
    2. Loewe A
    3. Neic A
    4. Augustin C
    5. Huang YL
    6. Gsell MAF
    7. Karabelas E
    8. Nothstein M
    9. Prassl AJ
    10. Sánchez J
    11. Seemann G
    12. Vigmond EJ

    (2021) The openCARP simulation environment for cardiac electrophysiology

    Computer Methods and Programs in Biomedicine 208:106223.

    https://doi.org/10.1016/j.cmpb.2021.106223

    • PubMed
    • Google Scholar
31. 1. Prakosa A
    2. Arevalo HJ
    3. Deng D
    4. Boyle PM
    5. Nikolov PP
    6. Ashikaga H
    7. Blauer JJE
    8. Ghafoori E
    9. Park CJ
    10. Blake RC
    11. Han FT
    12. MacLeod RS
    13. Halperin HR
    14. Callans DJ
    15. Ranjan R
    16. Chrispin J
    17. Nazarian S
    18. Trayanova NA

    (2018) Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia

    Nature Biomedical Engineering 2:732–740.

    https://doi.org/10.1038/s41551-018-0282-2

    • PubMed
    • Google Scholar
32. 1. Protonotarios A
    2. Bariani R
    3. Cappelletto C
    4. Pavlou M
    5. García-García A
    6. Cipriani A
    7. Protonotarios I
    8. Rivas A
    9. Wittenberg R
    10. Graziosi M
    11. Xylouri Z
    12. Larrañaga-Moreira JM
    13. de Luca A
    14. Celeghin R
    15. Pilichou K
    16. Bakalakos A
    17. Lopes LR
    18. Savvatis K
    19. Stolfo D
    20. Dal Ferro M
    21. Merlo M
    22. Basso C
    23. Freire JL
    24. Rodriguez-Palomares JF
    25. Kubo T
    26. Ripoll-Vera T
    27. Barriales-Villa R
    28. Antoniades L
    29. Mogensen J
    30. Garcia-Pavia P
    31. Wahbi K
    32. Biagini E
    33. Anastasakis A
    34. Tsatsopoulou A
    35. Zorio E
    36. Gimeno JR
    37. Garcia-Pinilla JM
    38. Syrris P
    39. Sinagra G
    40. Bauce B
    41. Elliott PM

    (2022) Importance of genotype for risk stratification in arrhythmogenic right ventricular cardiomyopathy using the 2019 ARVC risk calculator

    European Heart Journal 43:3053–3067.

    https://doi.org/10.1093/eurheartj/ehac235

    • PubMed
    • Google Scholar
33. 1. Rottmann M
    2. Kleber AG
    3. Barkagan M
    4. Sroubek J
    5. Leshem E
    6. Shapira-Daniels A
    7. Buxton AE
    8. Anter E

    (2019) Activation during sinus rhythm in ventricles with healed infarction

    Circulation 12:e007879.

    https://doi.org/10.1161/CIRCEP.119.007879

    • Google Scholar
34. 1. Santangeli P
    2. Zado ES
    3. Supple GE
    4. Haqqani HM
    5. Garcia FC
    6. Tschabrunn CM
    7. Callans DJ
    8. Lin D
    9. Dixit S
    10. Hutchinson MD
    11. Riley MP
    12. Marchlinski FE

    (2015) Long-term outcome with catheter ablation of ventricular tachycardia in patients with arrhythmogenic right ventricular cardiomyopathy

    Circulation. Arrhythmia and Electrophysiology 8:1413–1421.

    https://doi.org/10.1161/CIRCEP.115.003562

    • PubMed
    • Google Scholar
35. 1. Sato PY
    2. Musa H
    3. Coombs W
    4. Guerrero-Serna G
    5. Patiño GA
    6. Taffet SM
    7. Isom LL
    8. Delmar M

    (2009) Loss of plakophilin-2 expression leads to decreased sodium current and slower conduction velocity in cultured cardiac myocytes

    Circulation Research 105:523–526.

    https://doi.org/10.1161/CIRCRESAHA.109.201418

    • PubMed
    • Google Scholar
36. 1. Sawant AC
    2. Bhonsale A
    3. te Riele ASJM
    4. Tichnell C
    5. Murray B
    6. Russell SD
    7. Tandri H
    8. Tedford RJ
    9. Judge DP
    10. Calkins H
    11. James CA

    (2014) Exercise has a disproportionate role in the pathogenesis of arrhythmogenic right ventricular dysplasia/cardiomyopathy in patients without desmosomal mutations

    Journal of the American Heart Association 3:e001471.

    https://doi.org/10.1161/JAHA.114.001471

    • PubMed
    • Google Scholar
37. 1. Sen-Chowdhry S
    2. Morgan RD
    3. Chambers JC
    4. McKenna WJ

    (2010) Arrhythmogenic cardiomyopathy: etiology, diagnosis, and treatment

    Annual Review of Medicine 61:233–253.

    https://doi.org/10.1146/annurev.med.052208.130419

    • PubMed
    • Google Scholar
38. 1. Shade JK
    2. Cartoski MJ
    3. Nikolov P
    4. Prakosa A
    5. Doshi A
    6. Binka E
    7. Olivieri L
    8. Boyle PM
    9. Spevak PJ
    10. Trayanova NA

    (2020) Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models

    Heart Rhythm 17:408–414.

    https://doi.org/10.1016/j.hrthm.2019.10.002

    • PubMed
    • Google Scholar
39. 1. Shade JK
    2. Prakosa A
    3. Popescu DM
    4. Yu R
    5. Okada DR
    6. Chrispin J
    7. Trayanova NA

    (2021) Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier

    Science Advances 7:eabi8020.

    https://doi.org/10.1126/sciadv.abi8020

    • PubMed
    • Google Scholar
40. 1. Souissi Z
    2. Boulé S
    3. Hermida JS
    4. Doucy A
    5. Mabo P
    6. Pavin D
    7. Anselme F
    8. Auquier N
    9. Ninni S
    10. Coisne A
    11. Brigadeau F
    12. Deken-Delannoy V
    13. Klug D
    14. Lacroix D

    (2018) Catheter ablation reduces ventricular tachycardia burden in patients with arrhythmogenic right ventricular cardiomyopathy: insights from a north-western French multicentre registry

    Europace 20:362–369.

    https://doi.org/10.1093/europace/euw332

    • PubMed
    • Google Scholar
41. 1. Sung E
    2. Prakosa A
    3. Aronis KN
    4. Zhou S
    5. Zimmerman SL
    6. Tandri H
    7. Nazarian S
    8. Berger RD
    9. Chrispin J
    10. Trayanova NA

    (2020) Personalized digital-heart technology for ventricular tachycardia ablation targeting in hearts with infiltrating adiposity

    Circulation. Arrhythmia and Electrophysiology 13:e008912.

    https://doi.org/10.1161/CIRCEP.120.008912

    • PubMed
    • Google Scholar
42. 1. Sung E
    2. Etoz S
    3. Zhang Y
    4. Trayanova NA

    (2021) Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications

    Biophysics Reviews 2:031304.

    https://doi.org/10.1063/5.0058050

    • PubMed
    • Google Scholar
43. 1. ten Tusscher K
    2. Panfilov AV

    (2006) Alternans and spiral breakup in a human ventricular tissue model

    American Journal of Physiology. Heart and Circulatory Physiology 291:H1088–H1100.

    https://doi.org/10.1152/ajpheart.00109.2006

    • PubMed
    • Google Scholar
44. 1. Te Riele A
    2. James CA
    3. Philips B
    4. Rastegar N
    5. Bhonsale A
    6. Groeneweg JA
    7. Murray B
    8. Tichnell C
    9. Judge DP
    10. Van Der Heijden JF
    11. Cramer MJM
    12. Velthuis BK
    13. Bluemke DA
    14. Zimmerman SL
    15. Kamel IR
    16. Hauer RNW
    17. Calkins H
    18. Tandri H

    (2013) Mutation-positive arrhythmogenic right ventricular dysplasia/cardiomyopathy: the triangle of dysplasia displaced

    Journal of Cardiovascular Electrophysiology 24:1311–1320.

    https://doi.org/10.1111/jce.12222

    • PubMed
    • Google Scholar
45. 1. te Riele A
    2. Tandri H
    3. Bluemke DA

    (2014) Arrhythmogenic right ventricular cardiomyopathy (ARVC): cardiovascular magnetic resonance update

    Journal of Cardiovascular Magnetic Resonance 16:50.

    https://doi.org/10.1186/s12968-014-0050-8

    • PubMed
    • Google Scholar
46. 1. Thiene G
    2. Corrado D
    3. Basso C

    (2007) Arrhythmogenic right ventricular cardiomyopathy/dysplasia

    Orphanet Journal of Rare Diseases 2:45.

    https://doi.org/10.1186/1750-1172-2-45

    • PubMed
    • Google Scholar
47. 1. Vimalanathan AK
    2. Ehler E
    3. Gehmlich K

    (2018) Genetics of and pathogenic mechanisms in arrhythmogenic right ventricular cardiomyopathy

    Biophysical Reviews 10:973–982.

    https://doi.org/10.1007/s12551-018-0437-0

    • PubMed
    • Google Scholar
48. 1. Waintraub X
    2. Gandjbakhch E

    (2020) My approach to ventricular tachycardia ablation in patient with arrhythmogenic right ventricular cardiomyopathy/dysplasia

    HeartRhythm Case Reports 6:51–59.

    https://doi.org/10.1016/j.hrcr.2019.09.006

    • PubMed
    • Google Scholar
49. 1. Zhang Y
    2. Prakosa A
    3. Pandey P
    4. Zimmerman SL
    5. Tandri H
    6. James CA
    7. Trayanova N

    (2021) Abstract 9169: A Non-Invasive Virtual Heart Approach Predicts Reentrant Ventricular Arrythmia (VA) Circuits in Patients With Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC): A Proof-of-Concept Study

    Circulation 144:A9169.

    https://doi.org/10.1161/circ.144.suppl_1.9169

    • Google Scholar

Author details

1. Yingnan Zhang

   1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
   2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2070-6786

2. Kelly Zhang

   1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
   2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, United States

   Contribution

   Software, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8666-4569

3. Adityo Prakosa

   1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
   2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, United States

   Contribution

   Software, Funding acquisition, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1590-0322

4. Cynthia James

   Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

   Contribution

   Resources, Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

5. Stefan L Zimmerman

   Department of Radiology, Johns Hopkins University, Baltimore, United States

   Contribution

   Resources, Data curation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

6. Richard Carrick

   Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

   Contribution

   Resources, Data curation, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

7. Eric Sung

   1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
   2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, United States

   Contribution

   Conceptualization, Software, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Alessio Gasperetti

   Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

   Contribution

   Resources, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Crystal Tichnell

   Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

   Contribution

   Resources, Data curation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

10. Brittney Murray

    Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

    Contribution

    Resources, Data curation, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

11. Hugh Calkins

    Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

12. Natalia A Trayanova

    1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    2. Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    ntrayanova@jhu.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8661-063X

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