1. Computational and Systems Biology
  2. Medicine

Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations

  1. Xin Zhou
  2. Zhinuo Jenny Wang  Is a corresponding author
  3. Julia Camps
  4. Jakub Tomek
  5. Alfonso Santiago
  6. Adria Quintanas
  7. Mariano Vazquez
  8. Marmar Vaseghi
  9. Blanca Rodriguez
  1. Department of Computer Science, University of Oxford, United Kingdom
  2. Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom
  3. Department of Computer Applications in Science and Engineering, Barcelona Supercomputing Centre (BSC), Spain
  4. ELEM Biotech, Spain
  5. UCLA Cardiac Arrhythmia Center, University of California, Los Angeles, United States
  6. Neurocardiology Research Center of Excellence, University of California, Los Angeles, United States
https://doi.org/10.7554/eLife.93002.3
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Cite this article
  1. Xin Zhou
  2. Zhinuo Jenny Wang
  3. Julia Camps
  4. Jakub Tomek
  5. Alfonso Santiago
  6. Adria Quintanas
  7. Mariano Vazquez
  8. Marmar Vaseghi
  9. Blanca Rodriguez
(2024)
Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations
eLife 13:RP93002.
https://doi.org/10.7554/eLife.93002.3
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31 figures, 12 tables and 1 additional file

Figures

Figure 1
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Agreement between simulated and clinical ECGs demonstrating variability in clinical phenotypes in acute and chronic post-myocardial infarction (post-MI).

(A) In acute MI, simulated ECGs show T-wave inversion (border zone model 1 (BZ1)), Brugada phenocopy (BZ2), and normal phenotypes (BZ3), in accordance with phenotypes found in clinical databases. (B)…

Figure 2
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Multiscale explanation of ST and T-wave phenotypes in acute MI.

(A) Activation time maps reveal conduction delay in acute border zone in T-wave inversion and normal ST-T phenotypes, and conduction block in Brugada phenocopy, as well as large repolarisation …

Figure 3
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Multiscale explanation of QT and T-wave phenotypes in chronic MI.

(A) Conduction delay in chronic border zone occurs in slight QT prolongation and large T-wave phenotypes, while large repolarisation dispersion exists only in large T-wave. Red in activation map …

Figure 4
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Reduced LVEF and heterogeneous systolic deformation caused by ionic current remodelling in both acute and chronic post-myocardial infarction.

Pressure-volume loops are shown in black (control) or red (post-MI) traces for the baseline model, and in gray (control) or pink (post-MI) traces for the population of models. (A) Reduced LVEF in …

Figure 5
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T-wave alternans in simulations underpinned by APD and calcium alternans at fast pacing (120 bpm), albeit with preserved left ventricular ejection fraction (LVEF = 49%) at rest (75 bpm) for the chronic MI phenotype (A).

(B) Simulated APD and calcium traces in midmyocardial population of models with remote zone 2 (RZ2) remodelling. (C) Large action potential and calcium transient alternans were caused by EADs in …

Figure 6
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Prolonged QT and preserved LVEF at rest can manifest as severely abnormal ECG at fast heart rates in chronic MI with RZ2.

(A), due to electrotonically-triggered EADs across the border zone (B). In (B), membrane potential changes for the first 1010ms of fast pacing simulation, showing ectopic wave generation driven by …

Figure 7
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Human-based multi-scale modelling and simulation in acute and chronic myocardial infarction.

(A) Simulations using a population of ventricular models (n=17) to produce ECGs (light blue traces) and pressure-volume (PV) loops (grey traces) superimposed with the baseline ventricular model (ECG …

Appendix 1—figure 1
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The ToR-ORd-SK model produced similar action potential (AP) traces as published human experimental data (O’Hara et al., 2011).
Appendix 1—figure 2
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The AP and CaT traces of the population of NZ population of models.

The blue and red traces are the initial and the accepted population, respectively. The black trace is the baseline endocardial model.

Appendix 1—figure 3
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Effects of apex-to-base gradient (A) on pressure-volume, LVEF (B), and ECG morphology (C).
Appendix 1—figure 4
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Effects of transmural electrophysiological heterogeneity (A) on pressure-volume, LVEF (B), and ECG morphology (C).
Appendix 1—figure 5
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Effects of troponin calcium sensitivity on pressure-volume, LVEF (A), and ECG morphology (B).
Appendix 1—figure 6
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Effects of sheet activation percentage on pressure-volume, LVEF (A), and ECG morphology (B).
Appendix 1—figure 7
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Simulated acute stage 12-lead ECGs for Acute BZ1-3.

Acute BZ1 caused T wave inversion in precordial leads of V3 and V4, where the QT prolongation was more significant. Acute BZ2 caused Brugada phenocopy in leads V3-V5, while Acute BZ3 produced …

Appendix 1—figure 8
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Simulated chronic stage 12-lead ECGs for Chronic RZ1 and Chronic RZ2, both combined with Chronic BZ.

Both produced normal ECG morphology, and T waves are wider and taller in the anterior leads (V2–V4) of Chronic RZ2.

Appendix 1—figure 9
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Simulated acute stage 12-lead ECGs for Acute BZ1-3 with contractility turned off in the BZs.

Acute BZ1 caused T wave inversion in precordial leads of V3 and V4, where the QT prolongation was more significant. Acute BZ2 caused Brugada phenocopy in leads V3-V5, while Acute BZ3 produced …

Appendix 1—figure 10
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Transmural activation time (AT), repolarisation time (RT), and action potential duration at 90% repolarisation (APD90) are shown for the acute (top panel) and chronic (bottom panel) phenotypes.

A mid-ventricular anterior transmural slice is taken from the left ventricle that shows the cross-section of the anterior infarction and border zones. The transmural gradient is evaluated as the …

Appendix 1—figure 11
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Single cell simulations of the action potential (left), L-type calcium ionic current (middle) and sodium ionic current (right) compared between the scar region and the acute BZ2 help to explain the activation pattern in BZ2.
Appendix 1—figure 12
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The effects of the BZ and RZ remodelling of the acute and chronic stages on alternans generation in the population of 245 population of models at CL = 500ms, 400ms and 300ms.
Appendix 1—figure 13
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Ten representative alternans in the epicardial population of Chronic RZ1, showing calcium-driven repolarization alternans without EADs.
Appendix 1—figure 14
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Ten representative alternans in the midmyocardial population of Chronic BZ, showing EAD as a major cause of big alternans.
Appendix 1—figure 15
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Inhibition of Jup and slower calcium release (Jrel) caused by enhanced CaMKII activity promoted alternans.
Appendix 1—figure 16
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Effects of IKCa enhancement on alternans generation in the chronic stage.

Left: switching IKCa activity back to normal (black traces) caused AP prolongation and bigger alternans. Weaker IKCa also led to stronger CaT (bottom right, black solid line) and larger calcium …

Appendix 1—figure 17
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CaMKII and IKCa had opposite roles on alternans inducibility in chronic ionic current remodelling.

Enhanced IKCa tended to inhibit alternans generation (A), whereas augmented CaMKII promoted alternans (B).

Appendix 1—figure 18
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With chronic post-MI ionic current remodelling, alternans models needed stronger GCaL and more preserved PJup to enable alternans generation.
Appendix 1—figure 19
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Alternans models had bigger CaTmax than the non-alternating models in the epicardial populations with chronic post-MI remodelling (all with P<0.001).

In addition, alternans models tended to have smaller CaTmin in epicardial populations of Chronic RZ1 (P<0.001) and RZ2 (P<0.05), while the difference was not statistically significant for Chronic BZ.

Appendix 1—figure 20
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Midmyocardium was most prone for the development of EAD under chronic post-MI remodelling.
Appendix 1—figure 21
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Chronic ionic current remodelling promotes EAD generation through the enhanced INaL and suppressed IKr, which facilitate ICaL reactivation.
Appendix 1—figure 22
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EAD models tended to have stronger CaTmax and weaker CaTmin in the population of Chronic BZ, RZ1 and RZ2 models (all with P<0.001).
Appendix 1—figure 23
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Chronic BZ remodelling induced EAD alternans in the ten representative midmyocardial models, but when these models were simulated without IKr and INaL remodelling, neither EAD nor alternans occurred.
Appendix 1—figure 24
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Chronic BZ remodelling induced EAD alternans in the ten representative midmyocardial models, but when these models were simulated without PJup (SERCA) and CaMKII remodelling, neither EAD nor alternans occurred.

Tables

Table 1
Linking clinical ECG and left ventricular ejection fraction (LVEF) phenotypes to tissue heterogeneities and subcellular ionic current remodelling in acute and chronic post-myocardial infarction.
Clinical PhenotypesTissue or Cell Level phenomenaCorresponding Post Infarction Ionic Current Remodelling
Acute MI T-wave inversion in ECGReversed transmural repolarisation gradient due to delayed activation and repolarisation in the epicardial border zoneInhibition of potassium currents in the border zone as well as the slower transmural conduction velocity
Acute MI Brugada phenocopy in ECGDelayed repolarisation, as well as a small region of activation failure in the epicardial border zoneStrong inhibitions of sodium, calcium and potassium ionic currents in the border zone
Chronic MI upright tall T-waves in ECGLarge repolarisation time gradient between remote and border zones caused by more severe delay of repolarisation in the remote zoneMore severe potassium channel suppression in the remote zone
Chronic MI T-wave alternansCellular repolarisation alternans or early afterdepolarisationSuppressed SERCA and augmented CaMKII activity for alternans; Enhanced late sodium current and suppressed hERG current for early afterdepolarisation
Acute MI reduction in LVEFReduced calcium amplitude and/or regional conduction blockInhibitions of calcium and sodium currents
Chronic MI reduction in LVEFReduced calcium amplitudeDecreased SERCA activity
Appendix 1—table 1
Experimental ranges of AP and calcium transient (CaT) biomarkers used to calibrate the normal zone (NZ) endocardial population of models at a pacing cycle length (CL) of 1000ms based on human cardiomyocyte experiments (Coppini et al., 2013; Britton et al., 2017).
Biomakers at 1 Hzminimummaximum
Vmax (mV)755
RMP (mV)–95–80
dvdtmax (mV/ms)1001000
APD90 (ms)180440
APD50 (ms)350
APD40 (ms)85320
APD90-APD40 (ms)50150
CaTD90 (ms)220750
CaTD90 (ms)120420
CaTamp (mM)2e-46e-4
CaTmax(mM)2e-410e-4
CaTmin (mM)04e-4
Appendix 1—table 2
Calibrated electromechanical parameters for healthy baseline model, and modified parameters for post myocardial infarction models.
NameParameterValueUnit
Healthy baseline electromechanical parameters
diffusivity in fibre, sheet and sheet normal directionsdf0.00335cm/mS
ds0.000723cm/mS
dn0.000153cm/mS
active mechanics: scaling parameter for active tensionTscale12
bulk modulusK12185000Ba
passive mechanics: exponential term in isotropic matrix, fibre, sheet and normal directiona20000Ba
b9.242
af30000Ba
bf15.972
as20000Ba
bs10.446
afs10000Ba
bfs11.602
Scar and border zone diffusion parameters
diffusivity in fibre, sheet and sheet normal directionsdf0.0012cm/mS
ds0.00023cm/mS
dn0.000003cm/mS
Scar mechanical parameters
active mechanics: scaling parameter for active tensionTscale0
bulk modulusK12185000Ba
passive mechanics: exponential term in isotropic matrix, fibre, sheet and normal directiona200000Ba
b9.242
af300000Ba
bf15.972
as200000Ba
bs10.446
afs100000Ba
bfs11.602
Appendix 1—table 3
Parameters for boundary conditions and phase control at resting heart rate and fast pacing (in brackets).
NameParameterLVRVUnit
Pericardial stiffnessKepi10000Ba cm–1
Time to initial pressuret00.020.02s
Initial pressureP050005000Ba
Duration of passive diastolic fillingtdiastole0.08 (0.03)0.08 (0.03)s
Pressure at end of diastolePendd1500015000Ba
Arterial complianceC0.000559080.00055908cm3 Ba−1
Arterial resistanceR250100Ba s cm−3
Aortic pressurePart09000020000Ba
Pressure at end of isovolumetric relaxationPpost1000010000Ba
Penalty parameters for isovolumetric contractionCv11cm3 s–1 Ba–1
Penalty parameters for isovolumetric relaxationCv0.20.2cm3 s–1 Ba–1
Penalty parameters for passive fillingCp, Cv0.1,0.30.1,0.3cm3 Ba–1, cm3 s–1 Ba–1
Appendix 1—table 4
Ionic current remodelling for the acute and chronic stage BZ and RZs.
ScalingAcute BZ1Acute BZ2Acute BZ3Chronic BZChronic RZ1Chronic RZ2Infarct
GNa0.4 Hund et al., 2008; Decker and Rudy, 20100.38 Arevalo et al., 20160.4 Tomek et al., 20170.43 Valdivia et al., 20050.43 Valdivia et al., 20050.43 Valdivia et al., 20050.4
GNaL1.275 Hegyi et al., 20181.413 Hegyi et al., 20182 Valdivia et al., 2005; Maltsev et al., 2007
Gto0.1 Hund et al., 2008; Decker and Rudy, 20100 Tomek et al., 20170.6 Beuckelmann et al., 1993; Li et al., 20040
GCaL0.64 Hund et al., 2008; Decker and Rudy, 20100.31 Arevalo et al., 20160.64 Tomek et al., 20170.7 Hegyi et al., 20180.64
GKr0.7 Hund et al., 2008; Decker and Rudy, 20100.3 Arevalo et al., 20160.89 Hegyi et al., 20180.87 Hegyi et al., 20180.6 Ambrosi et al., 20130.7
GKs0.2 Hund et al., 2008; Decker and Rudy, 20100.2 Arevalo et al., 20160.4 Li et al., 2004
GK10.3 Hund et al., 2008; Decker and Rudy, 20100.6 Tomek et al., 20170.76 Hegyi et al., 20180.6 Beuckelmann et al., 1993; Li et al., 20040.6
GNaK0.6 Schwinger et al., 1999
PJup0.4 Jiang et al., 2002; Høydal et al., 20180.4 Jiang et al., 2002; Høydal et al., 20180.3 Jiang et al., 2002; Høydal et al., 2018
GKCa2 Hegyi et al., 20182 Hegyi et al., 20183.75 Chang et al., 2013
GClCa1.25 Hegyi et al., 20181.25 Hegyi et al., 20181.25 Hegyi et al., 2018
aCaMK1.5 Tomek et al., 20171.5 Hoch et al., 1999; Hund et al., 20081.5 Hoch et al., 1999; Hund et al., 20081.5 Hoch et al., 1999; Hund et al., 20081.5
Taurelp6 Maier et al., 20036 Maier et al., 20036 Maier et al., 20036 Maier et al., 20036
GCab1.33 Tomek et al., 20171.33
Appendix 1—table 5
Comparison of the simulated AP, CaT and active tension (Ta) biomarkers with post myocardial infarction (MI) acute stage canine experimental data and chronic stage human experimental data.
BiomarkersExperimental valuesSimulated values
Acute Post-MI StageCanine epi NZ APD (ms) (mean ± SD)295±34 Lue and Boyden, 1992 210±15 Gardner et al., 1985 219±39 Spear et al., 1983 Overall: [180, 329]227
Canine epi BZ APD (ms) (mean ± SD)346±60 Lue and Boyden, 1992 170±15 Gardner et al., 1985 220±26 Spear et al., 1983 Overall: [194, 406]BZ1: 284, BZ2: 256, BZ3: 208
Canine epi BZ Systolic Cai EBZ/NZ (%)74% Licata et al., 1997NZ: 686, BZ1: 517 (75%), BZ2: 133 (20%), BZ3: 457 (67%),
Canine epi BZ Voltage Clamp Cai at 0 mV EBZ/NZ53% Pu et al., 2000
Canine Cell shortening EBZ/NZ %12% Licata et al., 1997NZ systolic Ta: 40, BZ1: 24 (60%), BZ2: 0.37 (1%), BZ3: 20 (50%)
Chronic Post-MI StageHuman Mid Systolic Cai Failing/Non-Failing (%)49% Piacentino et al., 2003NZ (800 ms CL):1219, RZ1: 744 (60%), RZ2: 608 (50%)
Human Mid Systolic Cai MI/normal (%) 1 Hz37.5% Høydal et al., 2018
Human Mid Diastolic Cai Failing/Non-Failing (%)96% Piacentino et al., 2003NZ (800 ms CL): 80, RZ1: 37 (46%), RZ2: 39 (49%) NZ (500 ms CL): 86, RZ1: 52 (61%), RZ2: 62 (72%)
Human Mid Diastolic Cai MI/normal (%) 1 Hz115% Høydal et al., 2018
Human Mid Cell shortening MI/normal (%) 1 Hz33% Høydal et al., 2018NZ systolic Ta (800 ms CL): 65, RZ1: 58 (89%), RZ2: 45 (70%)
Appendix 1—table 6
Simulated AP, CaT and Ta biomarkers from baseline NZ, BZ and RZ epi-, mid-, and endocardial single cell models.

For the acute stage, the Acute BZ1 and BZ2 induced significant APD prolongation, while the BZ3 led to mild APD shortening. The Acute BZ1 and BZ3 had similar degree of reduction in systolic Ca and …

TypeAPD90 (ms)CaTD90 (ms)Diastolic Ca (nM)Systolic Ca (nM)Diastolic Ta (kPa)Systolic Ta (kPa)
Control (800ms)epi: 227, mid: 336,
endo: 263		epi: 298, mid: 333,
endo: 336		epi: 62.32,
mid: 79.74,
endo: 70.70		epi: 686.27,
mid: 1218.83, endo: 477.71		epi: 0.06, mid: 0.10,
endo: 0.07		epi: 40.00,
mid: 65.47,
endo: 23.87
Acute BZ1epi: 284, mid: 391,
endo: 315		epi: 289, mid: 339,
endo: 325		epi: 59.63,
mid: 63.52,
endo: 65.62		epi: 517.13,
mid: 691.30,
endo: 342.98		epi: 0.05, mid: 0.06,
endo: 0.06		epi: 24.33,
mid: 44.78,
endo: 10.46
Acute BZ2epi: 256, mid: 373,
endo: 341		epi: 266, mid: 342,
endo: 334		epi: 45.11,
mid: 57.08,
endo: 57.98		epi: 133.09,
mid: 326.57,
endo: 184.93		epi: 0.03, mid: 0.05,
endo: 0.05		epi: 0.37, mid: 8.97,
endo: 1.48
Acute BZ3epi: 208, mid: 316,
endo: 247		epi: 256, mid: 295,
endo: 288		epi: 49.05,
mid: 60.49,
endo: 59.79		epi: 457.49,
mid: 834.58,
endo: 352.71		epi: 0.03, mid: 0.05,
endo: 0.05		epi: 19.66,
mid: 52.08,
endo: 11.18
Chronic BZepi: 235, mid: 362,
endo: 293		epi: 419, mid: 444, endo: 474epi: 41.90, mid: 40.80, endo: 50.59epi: 324.87, mid: 499.97, endo: 285.67epi: 0.03, mid: 0.03, endo: 0.04epi: 10.44, mid: 31.40, endo: 7.76
Chronic RZ1epi: 247, mid: 411,
endo: 313		epi: 426, mid: 462, endo: 478epi: 39.64, mid: 37.47, endo: 49.34epi: 459.72, mid: 744.07, endo: 387.91epi: 0.03, mid: 0.05, endo: 0.04epi: 25.67, mid: 57.56, endo: 18.40
Chronic RZ2epi: 392, mid: 591,
endo: 467		epi: 498, mid: 569, endo: 557epi: 39.77, mid: 38.61, endo: 49.51epi: 444.67, mid: 607.59, endo: 361.11epi: 0.03, mid: 0.09, endo: 0.05epi: 25.14, mid: 44.80, endo: 16.17
Acute and (chronic) scarepi: 250, mid: 366,
endo: 295		epi: 261, mid: 304, endo: 296epi: 49.17, mid: 62.49, endo: 59.70epi: 502.85, mid: 883.02, endo: 375.70epi: 0.03 (0), mid: 0.06 (0), endo: 0.05 (0)epi: 24.23 (0), mid: 53.21 (0), endo: 13.28 (0)
Control (500ms)epi: 210, mid: 306,
endo: 240		epi: 265, mid: 276, endo: 298epi: 58.61, mid: 85.60, endo: 67.91epi: 760.33, mid: 1740.38, endo: 531.49epi: 0.38, mid: 1.81, endo: 0.33epi: 42.77, mid: 68.04, endo: 27.54
RZ1 (500ms)epi: 226, mid: 296, endo: 271epi: 370, mid: 384, endo: 396epi: 47.10, mid: 52.52, endo: 65.07epi: 470.50, mid: 712.75, endo: 385.73epi: 0.50, mid: 2.04, endo: 0.51epi: 25.84, mid: 51.69, endo: 17.28
RZ2 (500ms)epi: 316, mid: 395, endo: 366epi: 389, mid: 401, endo: 410epi: 50.88, mid: 61.53, endo: 65.99epi: 425.66, mid: 605.85, endo: 337.64epi: 0.52, mid: 1.94, endo: 0.46epi: 21.20, mid: 41.79, endo: 12.23
Appendix 1—table 7
Comparison of the ECG and mechanical biomarkers from biventricular electromechanical simulations against literature values at resting heart rate.

QTc was calculated using Bazett’s formula from the simulated QT intervals. Post-MI RVEF values were from ST-segment elevation myocardial infarction patients whose culprit and chronic total occlusion …

BiomarkersControlAcute Stage Post-MIChronic Stage Post-MI
Electrophysiological BiomarkersLiteratureSimulationLiteratureSimulationLiteratureSimulation
QRS duration (ms)96 ± 9 in men, 85 ± 6 in women Carlsson et al., 200679 ± 288 ± 35 Yerra et al., 200691±5, 95±9, 92±6Max 127 ± 16 without VT
Min 81 ± 15 without VT Perkiömäki et al., 1995 
Max 137 ± 25 with VT
Min 89 ± 20 with VT Perkiömäki et al., 1995		94 ± 6, 93 ± 5
QTc interval (Bazett formula) (ms)350–440 Johnson and Ackerman, 2009360 ± 1423 ± 50 without VA Ahnve, 1985
460±40 with VA Ahnve, 1985		398 ± 27, 415 ± 4, 376 ± 5Max 448 ± 39 without VT
Min 383 ± 20 without VT Perkiömäki et al., 1995
Max 493 ± 51 with VT
Min 388 ± 30 with VT ve stiffness parameters were calibrated based on Perkiömäki et al., 1995		430 ± 4, 578 ± 3
Mechanical BiomarkersLiteratureSimulationLiteratureSimulationLiteratureSimulation
LVEDV (mL)142 ± 21 (SSFP-CMR) Maceira et al., 2006a129116 ± 15 Uslu et al., 2013124–125106 ± 12 Uslu et al., 2013126
RVEDV (mL)144 ± 23 (SSFP-CMR) Maceira et al., 2006b131129 ± 28 with SDB Buchner et al., 2015
132 ± 28 without SDB Buchner et al., 2015		131143 ± 29 with SDB Buchner et al., 2015
132 ± 31 without SDB Buchner et al., 2015		133
LVESV (mL)47 ± 10 (SSFP-CMR) Maceira et al., 2006a6061 ± 12 Uslu et al., 201365~7252 ± 10 Uslu et al., 201365
RVESV (mL)50±14 (SSFP-CMR) Maceira et al., 2006b6356 21 with SDB Buchner et al., 2015
53 ± 16 without SDB Buchner et al., 2015		6458 ± 21 with SDB Buchner et al., 2015
51 ± 15 without SDB Buchner et al., 2015		64
LVEF (%)67 ± 4.6 (SSFP-CMR) Maceira et al., 2006a, 62±7 (RNV) Nemerovski et al., 19825348 ± 8 Uslu et al., 201343~4752 ± 7 Uslu et al., 201348
RVEF (%)48 ± 5 (RNV) Nemerovski et al., 19825253.0 ± 7.1 van Veelen et al., 20225155.9 ± 5.4 van Veelen et al., 202252
Appendix 1—table 8
Simulated ECG biomarkers from biventricular electromechanical simulations for the acute and the chronic post-MI stages.

For the acute stage, Acute BZ1 and BZ2 caused significant QT prolongation, longer T peak to T end, whereas the Acute BZ3 induced milder effects. For the chronic stage, both Chronic RZ1 and RZ2 led …

ECG biomarkersControlAcute BZ1Acute BZ2Acute BZ3Chronic RZ1Chronic RZ2Control CL = 500msChronic RZ1 CL = 500msChronic RZ2 CL = 500ms
QRS duration
(ms)		79±291±595±992±694±693±586±786±786±7
T duration (ms)96±17163±40-113±18122±42291±2097±19100±15140±37
T peak to T end (ms)59±10101±31122±4668±868±11153±3057±855±1282±17
T start to T peak (ms)38±861±30-45±1054±32138±1240±1145±658±22
QT interval (ms)322±1356±24371±3336±5385±4578±3305±2344±4419±4
QT dispersion (precordial) (ms)3559777465
Appendix 1—table 9
Simulated pressure-volume mechanical biomarkers for each heart beat from biventricular electromechanical simulations for acute and chronic stages post-MI: left and right end diastolic volumes (EDVL, EDVR), left and right stroke volumes (SVL, SVR), left and right ventricular ejection fractions (LVEF, RVEF).

For the acute stage, Acute BZ1 and Acute BZ3 generated the same degree of reduction in SVL and LVEF, whereas the Acute BZ2 induced the smallest SVL and LVEF. For the chronic stage, both Chronic RZ1 …

Pressure-volume BiomarkersControlAcute BZ1Acute BZ2Acute BZ3Chronic RZ1Chronic RZ2
800 ms CLEDVL (mL)129, 129, 129124,124,124124,125,125124,124,124127, 126, 126127, 126, 126
EDVR (mL)130,131,131130,131,131130,131,131130,131,131132,133,133132,133,133
SVL (mL)68, 69, 6959,59,5953,53,5359,59,5962, 61, 6162, 61, 61
SVR (mL)68,68,6867,67,6767,67,6767,67,6769,69,6969,69,69
LVEF (%)53,53,5347,47,4743,43,4347,47,4749, 48, 4849, 48, 48
RVEF (%)52,52,5251,51,5151,51,5151,51,5152,52,5252,52,52
Peak left systolic pressure (kPa)12,12,1211,11,1111,11,1111,11,1111, 11, 1111, 11, 11
Peak right systolic pressure (kPa)4,4,44,4,44,4,44,4,44,4,44,4,4
500 ms CLEDVL (mL)111,112,113, 113,113,113NA114,107,111, 108,110,108116,108,111, 109,110,109
EDVR (mL)123,122,123, 123,123,123125,117,122, 119,121,119126,119,123, 120,121,120
SVL (mL)53,53,54, 54,54,5450,43,47, 44,45,4451,43,46, 44,45,44
SVR (mL)61,61,61, 61,61,6164,56,62, 56,60,5765,57,62, 59,60,59
LVEF (%)47,47,47, 47,47,4744,39,42, 40,41,4044,39,41, 40,41,40
RVEF (%)49,49,49, 49,49,4951,47,50, 47,50,4851,48,50, 49,49,49
Peak left systolic pressure (kPa)11,11,11, 11,11,1110,10,10, 10,10,1010,10,10, 10,10,10
Peak right systolic pressure (kPa)4,4,4, 4,4,44,4,4, 4,4,44,4,4, 4,4,4
Appendix 1—table 10
Number of alternans induced by three chronic remodelling at CL = 500, 400 and 300ms in endocardial, midmyocardial and epicardial population of models.
Population of models (n=245)No. of alternans at CL = 300msNo. of alternans at CL = 400msNo. of alternans at CL = 500msKey parameters for alternans
Chronic BZMid (110)>Epi (47)Mid only (84)Mid only (10)↑GCaL, ↑GKr, ↑PJup
Chronic RZ1Epi (195)>Mid (81)>Endo (60)Mid (158)>Epi (110)>Endo (4)Mid (67)>Epi (1)↑GCaL, ↑PJup
Chronic RZ2Epi (88)>Mid (48)>Endo (2)Mid (58)>Epi (42)Mid (37)>Endo (8)>Epi (3)↑GCaL, ↑GKr, ↓GNCX, ↑PJup, ↑PJrel
Appendix 1—table 11
Number of EADs and RFs induced by three chronic remodelling in endocardial, midmyocardial and epicardial population of models.
Population of models (n=245)No. of EADs and RFs at CL = 1000msKey parameters for EADs and RFs
Chronic BZMid only (11)↑GCaL, ↓GKr, ↑GNCX
Chronic RZ1Mid only (52)↑GCaL, ↓GKr, ↑GNCX
Chronic RZ2Mid (118)>Epi (9)>Endo (1)↑GCaL, ↓GKr, ↑GNCX, ↑PJup

Additional files

MDAR checklist
https://cdn.elifesciences.org/articles/93002/elife-93002-mdarchecklist1-v1.docx

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