Primary Colorectal Tumor Location and Predictors for Metastasis to the Brain.

Introduction Although rectal cancer is thought to have a higher rate of metastasis to the brain compared with colon cancer, there is limited and contradictory data on the subject. This study aims to determine the prevalence of brain metastasis for colon and rectal cancers (CRC), and to explore associations and predictors of brain metastasis (BM). Methods The 2010-2016 National Cancer Database (NCDB) was queried for patients with stage IV CRC. Patients with missing data on site of metastasis and primary tumor location were excluded. Chi-square test was used for categorical data and multivariate logistic regression analysis was performed to evaluate the predictors of BM. Results Of 108,540 stage IV CRC patients, the prevalence of BM was 1.21% from the right colon, 1.29% from the left colon, and 1.59% from the rectal adenocarcinoma (p<0.001). The presence of lung, bone, and liver metastases were the strongest predictors for BM. Bone and lung metastases increased the odds for BM by 3.87 (95% CI: 3.36-4.46) and 3.38 (95% CI: 3.01-3.80), respectively while the presence of liver metastasis decreased odds for BM by 55% (OR: 0.45; 95% CI: 0.40-0.50). On multivariate analysis, primary tumor location was not predictive of BM. Discussion This study helps to characterize the prevalence and associations of BM from CRC using the NCDB. The correlation between BM and bone and lung metastases, along with negative association of liver metastasis further supports the hypothesis of systemic transmission of tumor cells. Further identification of predictors and correlations with BM may help guide surveillance among patients with advanced CRC.

Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models.

AIMS: Direct remuscularization with pluripotent stem cell-derived cardiomyocytes (PSC-CMs) seeks to address the onset of heart failure post-myocardial infarction (MI) by treating the persistent muscle deficiency that underlies it. However, direct remuscularization with PSC-CMs could potentially be arrhythmogenic. We investigated two possible mechanisms of arrhythmogenesis-focal vs. re-entrant-arising from direct remuscularization with PSC-CM patches in two personalized, human ventricular computer models of post-MI. Moreover, we developed a principled approach for evaluating arrhythmogenicity of direct remuscularization that factors in the VT propensity of the patient-specific post-MI fibrotic substrate and use it to investigate different conditions of patch remuscularization. METHODS AND RESULTS: Two personalized, human ventricular models of post-MI (P1 and P2) were constructed from late gadolinium enhanced (LGE)-magnetic resonance images (MRIs). In each model, remuscularization with PSC-CM patches was simulated under different treatment conditions that included patch engraftment, patch myofibril orientation, remuscularization site, patch size (thickness and diameter), and patch maturation. To determine arrhythmogenicity of treatment conditions, VT burden of heart models was quantified prior to and after simulated remuscularization and compared. VT burden was quantified based on inducibility (i.e. weighted sum of pacing sites that induced) and severity (i.e. the number of distinct VT morphologies induced). Prior to remuscularization, VT burden was significant in P1 (0.275) and not in P2 (0.0, not VT inducible). We highlight that re-entrant VT mechanisms would dominate over focal mechanisms; spontaneous beats emerging from PSC-CM grafts were always a fraction of resting sinus rate. Moreover, incomplete patch engraftment can be particularly arrhythmogenic, giving rise to particularly aberrant electrical activation and conduction slowing across the PSC-CM patches along with elevated VT burden when compared with complete engraftment. Under conditions of complete patch engraftment, remuscularization was almost always arrhythmogenic in P2 but certain treatment conditions could be anti-arrhythmogenic in P1. Moreover, the remuscularization site was the most important factor affecting VT burden in both P1 and P2. Complete maturation of PSC-CM patches, both ionically and electrotonically, at the appropriate site could completely alleviate VT burden. CONCLUSION: We identified that re-entrant VT would be the primary VT mechanism in patch remuscularization. To evaluate the arrhythmogenicity of remuscularization, we developed a principled approach that factors in the propensity of the patient-specific fibrotic substrate for VT. We showed that arrhythmogenicity is sensitive to the patient-specific fibrotic substrate and remuscularization site. We demonstrate that targeted remuscularization can be safe in the appropriate individual and holds the potential to non-destructively eliminate VT post-MI in addition to addressing muscle deficiency underlying heart failure progression.

Computationally guided personalized targeted ablation of persistent atrial fibrillation.

Atrial fibrillation (AF)-the most common arrhythmia-significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations, and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. First, we show that a computational model of the atria of patients identifies fibrotic tissue that, if ablated, will not sustain AF. Then, we report the results of integrating the target ablation sites in a clinical mapping system and testing its feasibility in ten patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients while eliminating the need for repeat procedures.

A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart.

Direct remuscularization approaches to cell-based heart repair seek to restore ventricular contractility following myocardial infarction (MI) by introducing new cardiomyocytes (CMs) to replace lost or injured ones. However, despite promising improvements in cardiac function, high incidences of ventricular arrhythmias have been observed in animal models of MI injected with pluripotent stem cell-derived cardiomyocytes (PSC-CMs). The mechanisms of arrhythmogenesis remain unclear. Here, we present a comprehensive framework for computational modeling of direct remuscularization approaches to cell therapy. Our multiscale 3D whole-heart modeling framework integrates realistic representations of cell delivery and transdifferentiation therapy modalities as well as representation of spatial distributions of engrafted cells, enabling simulation of clinical therapy and the prediction of emergent electrophysiological behavior and arrhythmogenensis. We employ this framework to explore how varying parameters of cell delivery and transdifferentiation could result in three mechanisms of arrhythmogenesis: focal ectopy, heart block, and reentry.

New insights on the cardiac safety factor: Unraveling the relationship between conduction velocity and robustness of propagation.

Cardiac conduction disturbances are linked with arrhythmia development. The concept of safety factor (SF) has been derived to describe the robustness of conduction, but the usefulness of this metric has been constrained by several limitations. For example, due to the difficulty of measuring the necessary input variables, SF calculations have only been applied to synthetic data. Moreover, quantitative validation of SF is lacking; specifically, the practical meaning of particular SF values is unclear, aside from the fact that propagation failure (i.e., conduction block) is characterized by SF < 1. This study aims to resolve these limitations for our previously published SF formulation and explore its relationship to relevant electrophysiological properties of cardiac tissue. First, HL-1 cardiomyocyte monolayers were grown on multi-electrode arrays and the robustness of propagation was estimated using extracellular potential recordings. SF values reconstructed purely from experimental data were largely between 1 and 5 (up to 89.1% of sites characterized). This range is consistent with values derived from synthetic data, proving that the formulation is sound and its applicability is not limited to analysis of computational models. Second, for simulations conducted in 1-, 2-, and 3-dimensional tissue blocks, we calculated true SF values at locations surrounding the site of current injection for sub- and supra-threshold stimuli and found that they differed from values estimated by our SF formulation by <10%. Finally, we examined SF dynamics under conditions relevant to arrhythmia development in order to provide physiological insight. Our analysis shows that reduced conduction velocity (Θ) caused by impaired intrinsic cell-scale excitability (e.g., due to sodium current a loss-of-function mutation) is associated with less robust conduction (i.e., lower SF); however, intriguingly, Θ variability resulting from modulation of tissue scale conductivity has no effect on SF. These findings are supported by analytic derivation of the relevant relationships from first principles. We conclude that our SF formulation, which can be applied to both experimental and synthetic data, produces values that vary linearly with the excess charge needed for propagation. SF calculations can provide insights helpful in understanding the initiation and perpetuation of cardiac arrhythmia.

The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping.

Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases "latent" RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.

Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation.

Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RDsim/RDECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RDECGI ablation. For the same cohort, we simulated AF and identified RDsim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RDsim- and RDECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RDECGI/RDsim were analyzed. RDsim were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RDECGI-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RDsim and RDECGI per individual was not significantly correlated (R = 0.46, P = ns). The overall rate of regional agreement was fair (modified Cohen's κ0 statistic = 0.11), as expected, based on the different mechanistic underpinning of RDsim- and RDECGI. nineteen regions were found to harbor both RDsim and RDECGI, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RDECGI perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RDECGI (i.e., targets in regions that also harbored RDsim) trended toward a higher rate of positive response compared to ablation of other RDECGI targets (57 vs. 41%, P = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.

Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), which promote fibrillatory activity. Targeted catheter ablation of RD-harboring tissues has shown promise as a clinical treatment for PsAF, but the outcomes remain sub-par. Personalized computational modeling has been proposed as a means of non-invasively predicting optimal ablation targets in individual PsAF patients, but it remains unclear how RD localization dynamics are influenced by inter-patient variability in the spatial distribution of atrial fibrosis, action potential duration (APD), and conduction velocity (CV). Here, we conduct simulations in computational models of fibrotic atria derived from the clinical imaging of PsAF patients to characterize the sensitivity of RD locations to these three factors. We show that RDs consistently anchor to boundaries between fibrotic and non-fibrotic tissues, as delineated by late gadolinium-enhanced magnetic resonance imaging, but those changes in APD/CV can enhance or attenuate the likelihood that an RD will anchor to a specific site. These findings show that the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e., incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodeled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not produce the desired result.