Cardiac length-dependent activation driven by force-dependent thick-filament dynamics.

The length-dependent activation (LDA) of maximum force and calcium sensitivity are established features of cardiac muscle contraction but the dominant underlying mechanisms remain to be fully clarified. Alongside the well-documented regulation of contraction via the thin filaments, experiments have identified an additional force-dependent thick-filament activation, whereby myosin heads parked in a so-called off state become available to generate force. This process produces a feedback effect that may potentially drive LDA. Using biomechanical modeling of a human left-ventricular myocyte, this study investigates the extent to which the off-state dynamics could, by itself, plausibly account for LDA, depending on the specific mathematical formulation of the feedback. We hypothesized four different models of the off-state regulatory feedback based on (A) total force, (B) active force, (C) sarcomere strain, and (D) passive force. We tested if these models could reproduce the isometric steady-state and dynamic LDA features predicted by an earlier published model of a human left-ventricle myocyte featuring purely phenomenological length dependences. The results suggest that only total-force feedback (A) is capable of reproducing the expected behaviors, but that passive tension could provide a length-dependent signal on which to initiate the feedback. Furthermore, by attributing LDA to off-state dynamics, our proposed model also qualitatively reproduces experimentally observed effects of the off-state-stabilizing drug mavacamten. Taken together, these results support off-state dynamics as a plausible primary mechanism underlying LDA.

Structural phenotyping in atrial fibrillation with combined cardiac CT and atrial MRI: Identifying and differentiating individual structural remodelling types in AF.

INTRODUCTION: Atrial remodelling (AR) is the persistent change in atrial structure and/or function and contributes to the initiation, maintenance and progression of atrial fibrillation (AF) in a reciprocal self-perpetuating relationship. Left atrial (LA) size, geometry, fibrosis, wall thickness (LAWT) and ejection fraction (LAEF) have all been shown to vary with pathological atrial remodelling. The association of these global remodelling markers with each other for differentiating structural phenotypes in AF is not well investigated. METHOD: Patients referred for first-time AF ablation and controls without AF were prospectively recruited to undergo cardiac computed tomographic angiography (CCTA) and magnetic resonance imaging (MRI) with 3D atrial late-gadolinium enhanced (LGE) sequences. LAWT, atrial myocardial mass, LA volume and sphericity were calculated from CT. Biplane LA EF and LA fibrosis burden were derived from atrial MRI. Results were compared between patients with AF and controls. RESULTS: Forty two AF patients (64.3% male, age 64.6 ± 10.2 years, CHA2DS2-VASc 2.48 ± 1.5, 69.0% paroxysmal AF, 31% persistent AF, LVEF 57.9 ± 10.5%) and 37 controls (64.9% male, age 56.6 ± 7.2, CHA2DS2-VASc 1.54 ± 1.1, LVEF 60.4 ± 4.9%) were recruited. Patients with AF had a significantly higher LAWT (1.45 ± 0.52 mm vs 1.12 ± 0.42 mm, p = 0.003), tissue mass (15.81 ± 6.53 g vs. 12.18 ± 5.01 g, p = 0.011), fibrosis burden (9.33 ± 8.35% vs 2.41 ± 3.60%, p = 0.013), left atrial size/volume (95.68 ± 26.63 mL vs 81.22 ± 20.64 mL, p = 0.011) and lower LAEF (50.3 ± 15.3% vs 65.2 ± 8.6%, p < 0.001) compared to controls. There was no significant correlation between % fibrosis with LAWT (p = 0.29), mass (p = 0.89), volume (p = 0.49) or sphericity (p = 0.79). LAWT had a statistically significant weak positive correlation with LA volume (r = 0.25, p = .041), but not with sphericity (p = 0.86). LAEF had a statistically significant but weak negative correlation with fibrosis (r = -0.33, p = 0.008) and LAWT (r = -0.24, p = 0.07). CONCLUSION: AF is associated with significant quantifiable structural changes that are evident in LA size, tissue thickness, total LA tissue mass and fibrosis. These individual remodelling markers do not or only weakly correlate with each other suggesting different remodelling subtypes exist (e.g. fibrotic vs hypertrophic vs dilated). If confirmed, such a detailed understanding of the structural changes observed has the potential to inform clinical management strategies targeting individual mechanisms underlying the disease process.

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.

Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging. A patient-specific anatomical heart model, or digital twin, was created. Cellular ionic dynamics and contraction were simulated with the Courtemanche-Land and the ToR-ORd-Land models for the atria and the ventricles, respectively. Whole heart contraction was coupled with the circulatory system, simulated with CircAdapt, while accounting for the effect of the pericardium on cardiac motion. The four-chamber electromechanics framework resulted in 117 parameters of interest. The model was broken into five hierarchical sub-models: tissue electrophysiology, ToR-ORd-Land model, Courtemanche-Land model, passive mechanics and CircAdapt. For each sub-model, we trained Gaussian processes emulators (GPEs) that were then used to perform a global sensitivity analysis (GSA) to retain parameters explaining 90% of the total sensitivity for subsequent analysis. We identified 45 out of 117 parameters that were important for whole heart function. We performed a GSA over these 45 parameters and identified the systemic and pulmonary peripheral resistance as being critical parameters for a wide range of volumetric and hemodynamic cardiac indexes across all four chambers. We have shown that GPEs provide a robust method for mapping between cellular properties and clinical measurements. This could be applied to identify parameters that can be calibrated in patient-specific models or digital twins, and to link cellular function to clinical indexes.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Effect of scar and His-Purkinje and myocardium conduction on response to conduction system pacing.

INTRODUCTION: Conduction system pacing (CSP), in the form of His bundle pacing (HBP) or left bundle branch pacing (LBBP), is emerging as a valuable cardiac resynchronization therapy (CRT) delivery method. However, patient selection and therapy personalization for CSP delivery remain poorly characterized. We aim to compare pacing-induced electrical synchrony during CRT, HBP, LBBP, HBP with left ventricular (LV) epicardial lead (His-optimized CRT [HOT-CRT]), and LBBP with LV epicardial lead (LBBP-optimized CRT [LOT-CRT]) in patients with different conduction disease presentations using computational modeling. METHODS: We simulated ventricular activation on 24 four-chamber heart geometries, including His-Purkinje systems with proximal left bundle branch block (LBBB). We simulated septal scar, LV lateral wall scar, and mild and severe myocardium and LV His-Purkinje system conduction disease by decreasing the conduction velocity (CV) down to 70% and 35% of the healthy CV. Electrical synchrony was measured by the shortest interval to activate 90% of the ventricles (90% of biventricular activation time [BIVAT-90]). RESULTS: Severe LV His-Purkinje conduction disease favored CRT (BIVAT-90: HBP 101.5 ± 7.8 ms vs. CRT 93.0 ± 8.9 ms, p < .05), with additional electrical synchrony induced by HOT-CRT (87.6 ± 6.7 ms, p < .05) and LOT-CRT (73.9 ± 7.6 ms, p < .05). Patients with slow myocardium CV benefit more from CSP compared to CRT (BIVAT-90: CRT 134.5 ± 24.1 ms; HBP 97.1 ± 9.9 ms, p < .01; LBBP: 101.5 ± 10.7 ms, p < .01). Septal but not lateral wall scar made CSP ineffective, while CRT was able to resynchronize the ventricles in the presence of septal scar (BIVAT-90: baseline 119.1 ± 10.8 ms vs. CRT 85.1 ± 14.9 ms, p < .01). CONCLUSION: Severe LV His-Purkinje conduction disease attenuates the benefits of CSP, with additional improvements achieved with HOT-CRT and LOT-CRT. Septal but not lateral wall scars make CSP ineffective.

Cost-effectiveness analysis of leadless cardiac resynchronization therapy.

BACKGROUND: The Wireless Stimulation Endocardially for CRT (WiSE-CRT) system is a novel technology used to treat patients with dyssynchronous heart failure (HF) by providing leadless cardiac resynchronization therapy (CRT). Observational studies have demonstrated its safety and efficacy profile, however, the treatment cost-effectiveness has not previously been examined. METHODS: A cost-effectiveness evaluation of the WiSE-CRT System was performed using a cohort-based economic model adopting a "proportion in state" structure. In addition to the primary analysis, scenario analyses and sensitivity analyses were performed to test for uncertainty in input parameters. Outcomes were quantified in terms of quality-adjusted life year (QALY) differences. RESULTS: The primary analysis demonstrated that treatment with the WiSE-CRT system is likely to be cost-effective over a lifetime horizon at a QALY reimbursement threshold of £20 000, with a net monetary benefit (NMB) of £3781 per QALY. Cost-effectiveness declines at time horizons shorter than 10 years. Sensitivity analyses demonstrated that average system battery life had the largest impact on potential cost-effectiveness. CONCLUSION: Within the model limitations, these findings support the use of WiSE-CRT in indicated patients from an economic standpoint. However, improving battery technology should be prioritized to maximize cost-effectiveness in times when health services are under significant financial pressures.

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS: Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION: Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF. CONCLUSIONS: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.

Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar.

Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and "every-patient" error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility.

Modelling the interaction between stem cells derived cardiomyocytes patches and host myocardium to aid non-arrhythmic engineered heart tissue design.

Application of epicardial patches constructed from human-induced pluripotent stem cell- derived cardiomyocytes (hiPSC-CMs) has been proposed as a long-term therapy to treat scarred hearts post myocardial infarction (MI). Understanding electrical interaction between engineered heart tissue patches (EHT) and host myocardium represents a key step toward a successful patch engraftment. EHT retain different electrical properties with respect to the host heart tissue due to the hiPSC-CMs immature phenotype, which may lead to increased arrhythmia risk. We developed a modelling framework to examine the influence of patch design on electrical activation at the engraftment site. We performed an in silico investigation of different patch design approaches to restore pre-MI activation properties and evaluated the associated arrhythmic risk. We developed an in silico cardiac electrophysiology model of a transmural cross section of host myocardium. The model featured an infarct region, an epicardial patch spanning the infarct region and a bath region. The patch is modelled as a layer of hiPSC-CM, combined with a layer of conductive polymer (CP). Tissue and patch geometrical dimensions and conductivities were incorporated through 10 modifiable model parameters. We validated our model against 4 independent experimental studies and showed that it can qualitatively reproduce their findings. We performed a global sensitivity analysis (GSA) to isolate the most important parameters, showing that the stimulus propagation is mainly governed by the scar depth, radius and conductivity when the scar is not transmural, and by the EHT patch conductivity when the scar is transmural. We assessed the relevance of small animal studies to humans by comparing simulations of rat, rabbit and human myocardium. We found that stimulus propagation paths and GSA sensitivity indices are consistent across species. We explored which EHT design variables have the potential to restore physiological propagation. Simulations predict that increasing EHT conductivity from 0.28 to 1-1.1 S/m recovered physiological activation in rat, rabbit and human. Finally, we assessed arrhythmia risk related to increasing EHT conductivity and tested increasing the EHT Na+ channel density as an alternative strategy to match healthy activation. Our results revealed a greater arrhythmia risk linked to increased EHT conductivity compared to increased Na+ channel density. We demonstrated that our modeling framework could capture the interaction between host and EHT patches observed in in vitro experiments. We showed that large (patch and tissue dimensions) and small (cardiac myocyte electrophysiology) scale differences between small animals and humans do not alter EHT patch effect on infarcted tissue. Our model revealed that only when the scar is transmural do EHT properties impact activation times and isolated the EHT conductivity as the main parameter influencing propagation. We predicted that restoring physiological activation by tuning EHT conductivity is possible but may promote arrhythmic behavior. Finally, our model suggests that acting on hiPSC-CMs low action potential upstroke velocity and lack of IK1 may restore pre-MI activation while not promoting arrhythmia.

Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation.

Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and that non-invasive electrical recordings, like electrocardiograms (ECGs) or body surface potential maps (BSPMs), could be used to detect FS and AF sustainability. Computer simulations were performed on five bi-atrial geometries. FS were induced by pacing at cycle lengths of 120-270 ms from 32 atrial sites and four pulmonary veins. Self-sustained reentrant activities were also initiated around the same 32 atrial sites with inexcitable cores of radii of 0, 0.5 and 1 cm. FS fired for two seconds and then AF inducibility was tested by whether activation was sustained for another second. ECGs and BSPMs were simulated. Equivalent atrial sources were extracted using second-order blind source separation, and their cycle length, periodicity and contribution, were used as features for random forest classifiers. Longer rotor duration during FS-driven episodes indicates higher AF inducibility (area under ROC curve = 0.83). Our method had accuracy of 90.6±1.0% and 90.6±0.6% in detecting FS presence, and 93.1±0.6% and 94.2±1.2% in identifying AF sustainability, and 80.0±6.6% and 61.0±5.2% in determining the atrium of the focal site, from BSPMs and ECGs of five atria. The detection of FS presence and AF sustainability were insensitive to vest placement (±9.6%). On pre-operative BSPMs of 52 paroxysmal AF patients, patients classified with initiator-type FS on a single atrium resulted in improved two-to-three-year AF-free likelihoods (p-value < 0.01, logrank tests). Detection of FS and arrhythmogenic substrate can be performed from ECGs and BSPMs, enabling non-invasive mapping towards mechanism-targeted AF treatment, and malignant ectopic beat detection with likely AF progression.

Correction: Linking statistical shape models and simulated function in the healthy adult human heart.

[This corrects the article DOI: 10.1371/journal.pcbi.1008851.].

Long-term survival following transvenous lead extraction: unpicking differences according to sex.

AIMS: Female sex is a recognized risk factor for procedure-related major complications including in-hospital mortality following transvenous lead extraction (TLE). Long-term outcomes following TLE stratified by sex are unclear. The purpose of this study was to evaluate factors influencing long-term survival in patients undergoing TLE according to sex. METHODS AND RESULTS: Clinical data from consecutive patients undergoing TLE in the reference centre between 2000 and 2019 were prospectively collected. The total cohort was divided into groups based on sex. We evaluated the association of demographic, clinical, device-related, and procedure-related factors on long-term mortality. A total of 1151 patients were included, with mean 66-month follow-up and mortality of 34.2% (n = 392). The majority of patients were male (n = 834, 72.4%) and 312 (37.4%) died. Males were more likely to die on follow-up [hazard ratio (HR) = 1.58 (1.23-2.02), P < 0.001]. Males had a higher mean age at explant (66.2 ± 13.9 vs. 61.3 ± 16.3 years, P < 0.001), greater mean co-morbidity burden (2.14 vs. 1.27, P < 0.001), and lower mean left ventricular ejection fraction (LVEF) (43.4 ± 14.0 vs. 50.8 ± 12.7, P = 0.001). For the female cohort, age > 75 years [HR = 3.45 (1.99-5.96), P < 0.001], estimated glomerular filtration rate < 60 [HR = 1.80 (1.03-3.11), P = 0.037], increasing co-morbidities (HR = 1.29 (1.06-1.56), P = 0.011), and LVEF per percentage increase [HR = 0.97 (0.95-0.99), P = 0.005] were all significant factors predicting mortality. The same factors influenced mortality in the male cohort; however, the HRs were lower. CONCLUSION: Female patients undergoing TLE have more favourable long-term outcomes than males with lower long-term mortality. Similar factors influenced mortality in both groups.

Anatomic, histologic, and mechanical features of the right atrium: implications for leadless atrial pacemaker implantation.

BACKGROUND: Leadless pacemakers (LPs) may mitigate the risk of lead failure and pocket infection related to conventional transvenous pacemakers. Atrial LPs are currently being investigated. However, the optimal and safest implant site is not known. OBJECTIVES: We aimed to evaluate the right atrial (RA) anatomy and the adjacent structures using complementary analytic models [gross anatomy, cardiac magnetic resonance imaging (MRI), and computer simulation], to identify the optimal safest location to implant an atrial LP human. METHODS AND RESULTS: Wall thickness and anatomic relationships of the RA were studied in 45 formalin-preserved human hearts. In vivo RA anatomy was assessed in 100 cardiac MRI scans. Finally, 3D collision modelling was undertaken assessing for mechanical device interaction. Three potential locations for an atrial LP were identified; the right atrial appendage (RAA) base, apex, and RA lateral wall. The RAA base had a wall thickness of 2.7 ± 1.6 mm, with a low incidence of collision in virtual implants. The anteromedial recess of the RAA apex had a wall thickness of only 1.3 ± 0.4 mm and minimal interaction in the collision modelling. The RA lateral wall thickness was 2.6 ± 0.9 mm but is in close proximity to the phrenic nerve and sinoatrial artery. CONCLUSIONS: Based on anatomical review and 3D modelling, the best compromise for an atrial LP implantation may be the RAA base (low incidence of collision, relatively thick myocardial tissue, and without proximity to relevant epicardial structures); the anteromedial recess of the RAA apex and lateral wall are alternate sites. The mid-RAA, RA/superior vena cava junction, and septum appear to be sub-optimal fixation locations.

Increased atrial effectiveness of flecainide conferred by altered biophysical properties of sodium channels.

Atrial fibrillation (AF) affects over 1% of the population and is a leading cause of stroke and heart failure in the elderly. A feared side effect of sodium channel blocker therapy, ventricular pro-arrhythmia, appears to be relatively rare in patients with AF. The biophysical reasons for this relative safety of sodium blockers are not known. Our data demonstrates intrinsic differences between atrial and ventricular cardiac voltage-gated sodium currents (INa), leading to reduced maximum upstroke velocity of action potential and slower conduction, in left atria compared to ventricle. Reduced atrial INa is only detected at physiological membrane potentials and is driven by alterations in sodium channel biophysical properties and not by NaV1.5 protein expression. Flecainide displayed greater inhibition of atrial INa, greater reduction of maximum upstroke velocity of action potential, and slowed conduction in atrial cells and tissue. Our work highlights differences in biophysical properties of sodium channels in left atria and ventricles and their response to flecainide. These differences can explain the relative safety of sodium channel blocker therapy in patients with atrial fibrillation.

Quantitative mapping of force-pCa curves to whole-heart contraction and relaxation.

The force-pCa (F-pCa) curve is used to characterize steady-state contractile properties of cardiac muscle cells in different physiological, pathological and pharmacological conditions. This provides a reduced preparation in which to isolate sarcomere mechanisms. However, it is unclear how changes in the F-pCa curve impact emergent whole-heart mechanics quantitatively. We study the link between sarcomere and whole-heart function using a multiscale mathematical model of rat biventricular mechanics that describes sarcomere, tissue, anatomy, preload and afterload properties quantitatively. We first map individual cell-level changes in sarcomere-regulating parameters to organ-level changes in the left ventricular function described by pressure-volume loop characteristics (e.g. end-diastolic and end-systolic volumes, ejection fraction and isovolumetric relaxation time). We next map changes in the sarcomere-regulating parameters to changes in the F-pCa curve. We demonstrate that a change in the F-pCa curve can be caused by multiple different changes in sarcomere properties. We demonstrate that changes in sarcomere properties cause non-linear and, importantly, non-monotonic changes in left ventricular function. As a result, a change in sarcomere properties yielding changes in the F-pCa curve that improve contractility does not guarantee an improvement in whole-heart function. Likewise, a desired change in whole-heart function (i.e. ejection fraction or relaxation time) is not caused by a unique shift in the F-pCa curve. Changes in the F-pCa curve alone cannot be used to predict the impact of a compound on whole-heart function. KEY POINTS: The force-pCa (F-pCa) curve is used to assess myofilament calcium sensitivity after pharmacological modulation and to infer pharmacological effects on whole-heart function. We demonstrate that there is a non-unique mapping from changes in F-pCa curves to changes in left ventricular (LV) function. The effect of changes in F-pCa on LV function depend on the state of the heart and could be different for different pathological conditions. Screening of compounds to impact whole-heart function by F-pCa should be combined with active tension and calcium transient measurements to predict better how changes in muscle function will impact whole-heart physiology.

Hypokalemia Promotes Arrhythmia by Distinct Mechanisms in Atrial and Ventricular Myocytes.

RATIONALE: Hypokalemia occurs in up to 20% of hospitalized patients and is associated with increased incidence of ventricular and atrial fibrillation. It is unclear whether these differing types of arrhythmia result from direct and perhaps distinct effects of hypokalemia on cardiomyocytes. OBJECTIVE: To investigate proarrhythmic mechanisms of hypokalemia in ventricular and atrial myocytes. METHODS AND RESULTS: Experiments were performed in isolated rat myocytes exposed to simulated hypokalemia conditions (reduction of extracellular [K+] from 5.0 to 2.7 mmol/L) and supported by mathematical modeling studies. Ventricular cells subjected to hypokalemia exhibited Ca2+ overload and increased generation of both spontaneous Ca2+ waves and delayed afterdepolarizations. However, similar Ca2+-dependent spontaneous activity during hypokalemia was only observed in a minority of atrial cells that were observed to contain t-tubules. This effect was attributed to close functional pairing of the Na+-K+ ATPase and Na+-Ca2+ exchanger proteins within these structures, as reduction in Na+ pump activity locally inhibited Ca2+ extrusion. Ventricular myocytes and tubulated atrial myocytes additionally exhibited early afterdepolarizations during hypokalemia, associated with Ca2+ overload. However, early afterdepolarizations also occurred in untubulated atrial cells, despite Ca2+ quiescence. These phase-3 early afterdepolarizations were rather linked to reactivation of nonequilibrium Na+ current, as they were rapidly blocked by tetrodotoxin. Na+ current-driven early afterdepolarizations in untubulated atrial cells were enabled by membrane hyperpolarization during hypokalemia and short action potential configurations. Brief action potentials were in turn maintained by ultra-rapid K+ current (IKur); a current which was found to be absent in tubulated atrial myocytes and ventricular myocytes. CONCLUSIONS: Distinct mechanisms underlie hypokalemia-induced arrhythmia in the ventricle and atrium but also vary between atrial myocytes depending on subcellular structure and electrophysiology.

In silico identification of potential calcium dynamics and sarcomere targets for recovering left ventricular function in rat heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a complex disease associated with multiple co-morbidities, where impaired cardiac mechanics are often the end effect. At the cellular level, cardiac mechanics can be pharmacologically manipulated by altering calcium signalling and the sarcomere. However, the link between cellular level modulations and whole organ pump function is incompletely understood. Our goal is to develop and use a multi-scale computational cardiac mechanics model of the obese ZSF1 HFpEF rat to identify important biomechanical mechanisms that underpin impaired cardiac function and to predict how whole-heart mechanical function can be recovered through altering cellular calcium dynamics and/or cellular contraction. The rat heart was modelled using a 3D biventricular biomechanics model. Biomechanics were described by 16 parameters, corresponding to intracellular calcium transient, sarcomere dynamics, cardiac tissue and hemodynamics properties. The model simulated left ventricular (LV) pressure-volume loops that were described by 14 scalar features. We trained a Gaussian process emulator to map the 16 input parameters to each of the 14 outputs. A global sensitivity analysis was performed, and identified calcium dynamics and thin and thick filament kinetics as key determinants of the organ scale pump function. We employed Bayesian history matching to build a model of the ZSF1 rat heart. Next, we recovered the LV function, described by ejection fraction, peak pressure, maximum rate of pressure rise and isovolumetric relaxation time constant. We found that by manipulating calcium, thin and thick filament properties we can recover 34%, 28% and 24% of the LV function in the ZSF1 rat heart, respectively, and 39% if we manipulate all of them together. We demonstrated how a combination of biophysically based models and their derived emulators can be used to identify potential pharmacological targets. We predicted that cardiac function can be best recovered in ZSF1 rats by desensitising the myofilament and reducing the affinity to intracellular calcium concentration and overall prolonging the sarcomere staying in the active force generating state.

Linking statistical shape models and simulated function in the healthy adult human heart.

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.

Standard care vs. TRIVEntricular pacing in Heart Failure (STRIVE HF): a prospective multicentre randomized controlled trial of triventricular pacing vs. conventional biventricular pacing in patients with heart failure and intermediate QRS left bundle branch block.

AIMS: To determine whether triventricular (TriV) pacing is feasible and improves CRT response compared to conventional biventricular (BiV) pacing in patients with left bundle branch block (LBBB) and intermediate QRS prolongation (120-150 ms). METHODS AND RESULTS: Between October 2015 and November 2019, 99 patients were recruited from 11 UK centres. Ninety-five patients were randomized 1:1 to receive TriV or BiV pacing systems. The primary endpoint was feasibility of TriV pacing. Secondary endpoints assessed symptomatic and remodelling response to CRT. Baseline characteristics were balanced between groups. In the TriV group, 43/46 (93.5%) patients underwent successful implantation vs. 47/49 (95.9%) in the BiV group. Feasibility of maintaining CRT at 6 months was similar in the TriV vs. BiV group (90.0% vs. 97.7%, P = 0.191). All-cause mortality was similar between TriV vs. BiV groups (4.3% vs. 8.2%, P = 0.678). There were no significant differences in echocardiographic LV volumes or clinical composite scores from baseline to 6-month follow-up between groups. CONCLUSION: Implantation of two LV leads to deliver and maintain TriV pacing at 6 months is feasible without significant complications in the majority of patients. There was no evidence that TriV pacing improves CRT response or provides additional clinical benefit to patients with LBBB and intermediate QRS prolongation and cannot be recommended in this patient group. CLINICAL TRIAL REGISTRATION NUMBER: Clinicaltrials.gov: NCT02529410.