Ventricular tachycardia (VT) is a life-threatening heart disease that occurs frequently in patients with myocardial infarction (MI), and one of the most prominent causes of sudden cardiac death (SCD) (Kusumoto et al., 2018). After acute MI, the myocardium in the center of infarct area is replaced by electric insulated fibrotic tissue, while some active myocardial cells extend into the dense fibrosis to form a slow concoction area which usually is called as gray zone (GZ). Thus, the heart tissue in patients with MI can be divided into three categories: non-infarct tissue, core scar, and GZ (Wu, 2017). The heterogeneity in the GZ slows down the electrical conduction in this part of the tissue, which in turn causes unidirectional conduction block and predisposes to reentry, leading to arrhythmias and increasing the risk of infarction in patients.

Clinical and experimental studies have shown that reentries in patients with VT can be sustained by anatomic reentry or functional reentry (Aguilar and Nattel, 2015; Martin et al., 2018). The anatomical reentry is very stable and always rotates around a fixed area (Fernández-Armenta et al., 2013; Josephson et al., 2014; Soto-Iglesias et al., 2020). For the functional reentry, the electric impulse proceeds as a single wavefront through a constrained region known as the central common pathway or isthmus of the reentrant circuit (Ciaccio et al., 2016; Martin et al., 2018; Crinion et al., 2020). In clinic, the critical isthmuses which sustain that the anatomical reentry can be measured by the high-density electroanatomic multipolar mapping system, but it is not easy to directly measure the critical isthmuses which sustain functional reentry (Martin et al., 2018; Crinion et al., 2020), especially for those unstable or non-sustained reentries (Nishimura et al., 2020).

Computational modeling has been widely used for the non-invasive investigation of lethal heart rhythm disorders and their treatment, including not only risk stratification of patients with MI (Behradfar et al., 2014; Deng et al., 2016; Lopez-Perez et al., 2019), the prediction of reentry location (Deng et al., 2015), but also guiding for VT ablation in clinic (Prakosa et al., 2018). Due to the high demand of computational resource of modeling, the reentries (both anatomic and functional) induced in the models are usually simulated for just a few seconds which is usually less than 5 s (Deng et al., 2015, 2016, 2019a,b; Arevalo et al., 2016; Prakosa et al., 2018; Ukwatta et al., 2018). In our previous work, we found that some reentries induced in the model were not stable, and become non-sustained before the end of simulation. In clinic, it is common that reentries last for more than several minutes and the morphologies of reentries are not always stable (Katritsis et al., 2012; Martin et al., 2018; Nishimura et al., 2020), so it is not clear that whether the simulation of reentries for just a few seconds is long enough to match the arrhythmias measured in clinic. Furthermore, as far as we know, still no work has been done to study whether the personalized cardiac modeling can reproduce most of the VT categories which include sustained stable reentry, sustained non-stable reentry, and non-sustained reentry measured in clinic.

In this study, we used personalized virtual heart models to: (1) find out an optimal dynamic stimulation protocol to save computational resources and make the simulation results more robust; and (2) study whether the personalized cardiac modeling can reproduce most of the VT categories measured in clinic.

Table 2 summarized the volumes of normal myocardium, GZ, and core scar in the reconstructed heart models from the MRI images of 5 patients. The mean ventricular volume was 176.4 cm³. The percentage of volume in the non-infarcted ventricular myocardium had a range from 86.4%∼96.4%, while the percentage of GZ volume and core scar ranged from 2.8%∼7.6% and 0.8%∼7.1%, respectively.

Our simulation results showed that 59.0% (23/39) of VTs induced in the 3 s simulation continued propagating to the end of 10 s simulation, but some of the induced VT morphologies had great difference before and after 3 s simulation. We divide the VTs into four different types based on the stable time of the final VT morphology (Tables 3, 4).

For type 1 VT, the VT location and morphology was not stable, it changed location in 1–3 cycles, but in the last 2–3 cycles, the VT location and morphology became stable. And in the extended 10 s simulation, the VT location and morphology was the same as the VT at the end of 3 s simulation. About 25.6% (10/39) VTs were type 1 reentry, and the time of VTs to be stable varied from 2.35 to 2.9 s.

Figure 3 showed one example of type 1 VT. The initial reentry took place in the front of mid anterior cavity of endocardium isthmus, and the breakthrough (Figure 3A) presented here was transmitted from epicardium, where the reentrant loop was large and lasted for 5 cycles. While the simulation reached the end of 3 s, a stable “figure of eight” reentry propagated two cycles at the middle part of anterior wall (Figure 3B). As shown in Figure 3C, with the extended 10 s simulation, the reentry kept rotating without any changes.

For type 2 VT, the VT location and morphology was not stable, it changed locations during the entire 3 s simulation. But in the extended 10 s simulation, the VT location stabilized to a fixed location and the morphology did not change. About 10.3% (4/39) VTs were type 2 reentry, and the time of VTs to be stable varied from 3.3 to 7.3 s.

Figure 4 showed one example of type 2 VT. During the 3 s simulation, several reentries were observed between myocardium and epicardium. The electrical propagation progress began with 3 cycles of one reentry with breakthrough at the epicardial surface. Then, the reentry changed to the anterior basal part of LV, and followed by changing to a new location with 2 cycles (Figure 4A). In the extended 10 s simulation, the chaotic reentry disappeared, and a new stable reentry formed at the anterior wall which lasted to the end of 10 s simulation (Figure 4B).

For type 3 VT, the VT location and morphology was stable in the 3 s simulation, and it did not change location and morphology in the extended 10 s simulation. Approximately, 23.1% (9/39) VTs were type 3 reentry, and the time of VTs to be stable varied from 1.4 to 2.3 s.

Figure 5 showed one example of type 3 VT. Figure 5A showed one reentry induced in a model in the 3 s simulation. The reentry located at the middle part of anterior wall, and it was a circulator. When the simulation was extended, there were little changes of morphology and position over time.

For type 4 VT, the VT location and morphology may change or be stable at the end of 3 s simulation, but they disappeared in the extended 10 s simulation. About 41.0% (9/39) VTs were type 4 reentry, and the time of VTs to be disappeared varied from 3.02 to 8.9 s.

Figure 6 showed one example of type 4 VT. The reentry induced in the 3 s simulation disappeared in the extending 10 s simulation. In the 3 s simulation, the reentry propagated from epicardial surface into the interior wall and backed to the surface. A total of six reentry cycles occurred, as shown in Figures 6A,B. When the simulation time was extended, the reentry at middle part of LV lateral wall in the 3 s simulation disappeared. A new reentry emerged at the posterior basal wall, and it lasted for six cycles and then disappeared (Figure 6C).

Table 5 summarized the clinical measurements and the 3 and 5 s simulation results of all five patients. For patient 1, the clinical diagnosis showed that there was no VT event when the patient was in hospital. Two sustained VTs were induced in the corresponding model of patient 1 in the 3 s simulation. In the 10 s simulation, 1 VT was terminated and the other survived to 10 s. Although 1 VT was induced in the 10 s simulation, the VT inducing ratio (5.3%) was very low, suggesting that the patient had a low probability to induce VT. Therefore, the result of 10 s simulation matched better than the 3 s simulation with the clinical diagnosis.

For patient 2, VT events were monitored four times and the location of VT was at the middle lateral wall of the LV. There were three different VTs induced in the 3 s simulation, one VT was related to the clinical measured location. The rest 2 VTs were not related to the clinical location. Two different VTs were induced in the 10 s simulation, one VT induced by 10 of 19 pacing sites was related to the clinical measured location. The rest one induced in only 1 pacing site was in another location.

For patient 3, VT events were monitored and the VT was epicardial reentry close to apex. Three sustained VTs were induced in the corresponding model of patient 3 in the 3 s simulation. One was related to the clinical measurement, and the other two VTs induced in 6 pacing sites were not related to the clinical location and terminated in the 10 s simulation. There was only one sustained VT induced in 6 pacing sites in the 10 s simulation which was related to the clinical measurement.

For patient 4, the clinical diagnosis showed that there was no infarct related VT event when the patient was in hospital, and this patient only had ventricular premature beat. There was one sustained VT induced in 4 pacing sites in the 3 s simulation, but the VT only lasted to maximum 5 s in the 10 s simulation. There was no sustained reentry induced in the 10 s simulation, it was corresponded to the clinical diagnosis.

For patient 5, the clinical diagnosis showed that there was non-sustained VT event when the patient was in hospital, and VT was not inducible in this patient during implantable cardioverter defibrillator (ICD) implantation procedure. There were 2 sustained VTs induced in 5 pacing sites in the 3 s simulation, but all these VTs terminated in the 10 s simulation. One VT lasted to 8.3 s in the 10 s simulation, this was corresponding to the clinical diagnosis of non-sustained VT.

Based on the comparison of results in 3 and 10 s simulation with clinical measurements (as shown in Table 5), the results showed that sustained VTs induced in 16 pacing sites in 3 s simulation were terminated in the 10 s simulation, this meant that 41% (16/39) of the sustained VTs in the 3 s simulation became non-stained in the 10 s simulation. The clinical measurements of patient 4 and 5 showed that the simulation results in 10 s simulation were more accurate than the 3 s simulation. In the 3 s simulation, more VTs were induced than the 10 s simulation, and some of VTs were not corresponding to clinical measurements.

In this study, we used personalized computational MI models which were reconstructed from LGE-MRI images to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to get a stable location and morphology, and whether these characteristics have difference with varied simulation time. Based on the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding is that 35.9% reentries were not stable when using 3 s simulation, which means that an extended simulation time is necessary.

For type 1 and 2 VT, because it is not stable when the simulation time is over, we should extend the simulation to a longer time to make sure the reentry become stable. But it is hard to give a consistent value for the simulation time based on the large variation of VT termination time. It should be possible to use a dynamic simulation time for each induced reentry, that is, 3, or 5, or 7, until 10 s. This will not only save the simulation resources, and make the simulation results more robust. These two types of VT may be related to the clinical hemodynamically unstable tachycardia (Nishimura et al., 2020). These kind of VTs are more likely sustained by functional reentry, this means that the involved VT is sustained by reentries through conduction channels around an area of functional block (Aguilar and Nattel, 2015; Deng et al., 2019a). The morphology and location of this kind of reentry were complex. The unstable characteristics of functional reentry was due to the intrinsic nature of the cardiac single cells (action potential duration adaptation, conductivity adaptation, etc.) (Chang et al., 2009; Lawson et al., 2020).

For type 3 VT, because the reentry gets stable at very early stage, and it does not change location and morphology in the extended simulation, thus for these reentries 3 s simulation is sufficient to get a robust result. This will save a lot of simulation resources and analysis time. This kind of VT is more likely sustained by anatomical reentry (Aguilar and Nattel, 2015), this means that the VT is sustained by reentries through conduction channels surrounded by scar on all sides. This kind of reentry was reported in other personalized heart modeling (Deng et al., 2019a; Kruithof et al., 2021) and clinical measurements (Anter et al., 2016; Nishimura et al., 2020). Our simulation results showed that type 3 VT was very stable, its morphology and location did not change during the entire 10 s simulation after reentry was initiated.

For type 4 VT, because the reentry gets disappeared in the extended simulation, this will not only spend more simulation resources to get robust results, but also make the results analysis more complex. It is very hard to recognize this kind of reentry as sustained reentry or non-sustained reentry as compared with the clinal definition (Katritsis et al., 2012). Besides, here rises a question that should this reentry be treated as an ablation target? These questions will be studied in the future when the clinical data of these patients become available.

For type 1, 2, and 4 VTs, the simulation results showed that simulation time must be extended to get the final stable reentry. Furthermore, the sustained reentry in 3 s simulation may become non-sustained reentry (or disappear in other word), this is consistent with clinical measurements that some reentries were unstable and may disappear in a few seconds (Ukwatta et al., 2015). In addition, the simulation results showed similar findings that no big morphological differences in circuit dimensions between stable and unstable VTs with the clinical measurements (Nishimura et al., 2020). We found that the CL of non-sustained reentry (type 4 VT) is shorter than other type of reentry, and the CL of type 1, 2, and 4 VTs did not have significant differences.

In the current work, we not only quantitatively analyzed the commonly clinical measured phenomena which were some arrhythmias lasted for more than several minutes but others lasted only a couple of cycles or seconds, and some arrhythmias had no stable morphology or location, and the morphologies of reentries were not always stable, but also proposed a dynamic stimulation protocol to save computational resources and make simulation results more robust. We believe that the findings of this study not only improve the simulation accuracy, but also reduce unnecessary simulation time to achieve the optimal use of computer resources, thus improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

One limitation of the current study is the small sample size of five patients, but it could demonstrate the findings in the conclusion section. Another limitation is that we did not compare the non-sustained reentry with the clinical definition, but we have compared the simulation results with clinical diagnosis. We define the sustained and non-sustained reentry only based on the lasting time of reentry within 10 s. We will improve the comparison when we get available clinical data in the future. The third limitation is that we have not done the quantitative comparison of VT characteristics in the model with clinical data, this is because that we do not have the corresponding patient data. But our simulation results can reproduce a lot of clinical measured phenomenon, this demonstrate that the personalized virtual heart simulation approach may provide a useful tool to help in understanding the underling mechanism of VT and assist in clinical decisions to identify and ablate the reentrant circuit(s).

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

The studies involving human participants were reviewed and approved by the Beijing Anzhen Hospital. Written informed consent for participation was not required for this study in accordance with the National Legislation and the Institutional Requirements.

CZ, RD, ZFW, NZ, and XW provided the human MRI and clinical data. DD, CZ, and LX designed the idea of the manuscript. ZF, ZHW, LT, NZ, BC, and YS did the manual segmentation. ZF, LT, ZHW, BC, and YS generated the figures. ZF, LT, DD, DZ, and LX wrote and revised the manuscript. All authors discussed the results and commented on the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.