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.

Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation.

AIMS: Atrial fibrillation (AF) is sustained by re-entrant activation patterns. Ablation strategies have been proposed that target regions of tissue that may support re-entrant activation patterns. We aimed to characterize the tissue properties associated with regions that tether re-entrant activation patterns in a validated virtual patient cohort. METHODS AND RESULTS: Atrial fibrillation patient-specific models (seven paroxysmal and three persistent) were generated and validated against local activation time (LAT) measurements during an S1-S2 pacing protocol from the coronary sinus and high right atrium, respectively. Atrial models were stimulated with burst pacing from three locations in the proximity of each pulmonary vein to initiate re-entrant activation patterns. Five atria exhibited sustained activation patterns for at least 80 s. Models with short maximum action potential durations (APDs) were associated with sustained activation. Phase singularities were mapped across the atria sustained activation patterns. Regions with a low maximum conduction velocity (CV) were associated with tethering of phase singularities. A support vector machine (SVM) was trained on maximum local conduction velocity and action potential duration to identify regions that tether phase singularities. The SVM identified regions of tissue that could support tethering with 91% accuracy. This accuracy increased to 95% when the SVM was also trained on surface area. CONCLUSION: In a virtual patient cohort, local tissue properties, that can be measured (CV) or estimated (APD; using effective refractory period as a surrogate) clinically, identified regions of tissue that tether phase singularities. Combing CV and APD with atrial surface area further improved the accuracy in identifying regions that tether phase singularities.

An audit of uncertainty in multi-scale cardiac electrophysiology models.

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.

In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Cardiac CT assessment of tissue thickness at the ostium of the left atrial appendage predicts acute success of radiofrequency ablation.

BACKGROUND: Tissue thickness at the site of ablation is a determinant of lesion transmurality. We reported the feasibility, safety, and efficacy of longstanding persistent atrial fibrillation ablation, incorporating deliberate left atrial appendage (LAA) isolation and occlusion, and identified systematic differences in ostial LAA tissue thickness in a matched cohort of cadaveric specimens. METHODS: Preprocedural cardiac computed tomography (CCT) scans were acquired from 22 patients undergoing LAA isolation and subsequent occlusion. Using a novel CCT wall thickness algorithm, LAA ostial wall thickness was assessed in vivo, compared with measurements from the cadaveric specimens, and analyzed for differences between regions that demonstrated acute electrical reconnection and those that did not. RESULTS: Mean tissue thickness calculated for each LAA ostial quadrant was 2.1 (±0.6) mm (anterior quadrant), 1.9 (±0.4) mm (superior quadrant), 1.5 (±0.4) mm (posterior quadrant), and 1.8 (±0.7) mm (inferior quadrant). Tissue was significantly thicker in the anterior (P  =  0.004) and superior quadrants (P  =  0.014) than the posterior quadrant. Higher thickness measurements were recorded from quadrants demonstrated to be thicker from histology. Tissue was significantly thicker in regions that demonstrated acute electrical reconnection (1.9 (±0.6) mm) when compared with those that did not (1.6 (±0.5) mm) (P  =  0.008). CONCLUSIONS: CCT imaging may be used to detect differences in wall thickness at different atrial locations and success of LAA ablation may be affected by local tissue thickness. Atrial wall thickness may need to be considered as a metric to guide titration of radiofrequency energy for safe and successful ablation.

Fibro-Lipo-Lymph-Aspiration With a Lymph Vessel Sparing Procedure to Treat Advanced Lymphedema After Multiple Lymphatic-Venous Anastomoses: The Complete Treatment Protocol.

BACKGROUND: In lymphedema, excess adipose tissue occurs with progression of the disease because of chronic lymph stasis, impeding lymphatic flow. Recently, liposuction has been used as a less-invasive procedure to remove this excess tissue. Given the existing poor lymph drainage in patients with lymphatic diseases, extra caution should be taken to avoid damaging lymphatic vessels during liposuction. We developed a new technique (Fibro-Lipo-Lymph-Aspiration with a Lymph Vessel Sparing Procedure [FLLA-LVSP]) to improve chronic swelling in patients with advanced lymphedema. The FLLA-LSVP highlights the superficial lymphatic pathways in the treated limb. This visibility allows surgeons to avoid these pathways, while removing the maximum amount of excess tissue. METHOD: One hundred forty-six patients with primary or secondary lymphedema that had already been treated by lymphatic microsurgery, in Genoa, Italy, were included in this retrospective study. All patients had residual fibrotic/adipose tissue, resistant to conservative treatments. Indocyanine green fluorescent dye and Blue Patent Violet dye were injected laterally/medially to the main superficial veins at the wrist/ankle of the limb to be treated. Using a photodynamic camera, the superficial lymphatic network was made visible and sketched onto the skin in indelible ink. After the microlymphography, the excess adipose tissue was carefully aspirated. Preoperative and postoperative excess limb volume was calculated using circumferential measurements and the formula of a frustum. RESULTS: For the upper limb, 0.80 L, on average, and 2.42 L for the lower limb were removed with the FLLA-LVSP. For the upper limb, there was an average presurgery excess volume of 20.19%, which reduced to 2.68% after the FLLA-LVSP (Z score = -6.90, P < 0.001). Similarly, for the lower limb, there was an average presurgery excess limb volume of 21.24% and a reduction to 2.64% postoperatively (Z score = -3.57, P < 0.01). Immediate postoperative microlymphography and Blue Patent Violet test confirmed no lymphatic complications. No episodes of postoperative infection occurred. CONCLUSIONS: The FLLA-LVSP is efficient. An entire leg can be completed within 90 minutes. Recovery time is short, and cosmetic results are immediate. More importantly, the removal of excess tissue is completed without further damage to lymphatic vessels. When used after microsurgery, FLLA-LVSP offers the possibility of removing almost all obstacles to lymphatic flow.

Mastering Lymphatic Microsurgery: A New Training Model in Living Tissue.

BACKGROUND: Advanced microsurgical techniques have emerged as a promising approach for the treatment of lymphedema, but achieving international standards is limited by a scarcity of adequate training models. The purpose of this report is to describe our in vivo porcine training model for microsurgery. STUDY DESIGN: Five female common-breed pigs (Sus scrofa domesticus) weighing 20 to 28 kg were placed under general anesthesia, and blue patent violet dye was injected to highlight lymphatic structures and prepare the pigs for anatomical exploration and microsurgery. The number and type of patent anastomoses achieved and lymph node flaps created and any anatomical differences between porcine and human vessels were noted, in light of evaluating the use of pigs as a training model for microsurgery in living tissue. RESULTS: Multiple lymphatic-venous anastomoses were created at the site of a single incision made at the subinguinal region, running medial and parallel to the saphenous vessels. Ten multiple lymphatic-venous anastomoses were created in total, and all were demonstrated to be patent. Four lymph node flaps were prepared for lymph node transfer. The superficial lymphatic collector system in the caudal limb of the pig was identified and described with particular reference to the superficial, medial (dominant), and lateral branches along the saphenous vein and its accessory. CONCLUSIONS: The authors present a safe and adaptable in vivo experimental microsurgical porcine model that provides the opportunity to practice several advanced lymphatic microsurgical techniques in the same animal. The ideal lymph node transfer training model can be developed from this anatomical detail, giving the opportunity to use it for artery-to-artery anastomoses, vein-to-vein anastomoses, and lymphatic-to-lymphatic anastomoses.

LYMPHA Technique to Prevent Secondary Lower Limb Lymphedema.

BACKGROUND: Inguinofemoral lymphadenectomy carries a high risk of lower limb lymphedema. This report describes the feasibility of performing multiple lymphatic-venous anastomoses (MLVA) after inguinofemoral lymph node completion (LYMPHA technique) and the possible benefit of LYMPHA for preventing lymphedema. METHODS: Between February, 2011 and October, 2014, 11 patients with vulvar cancer and 16 patients with melanoma of the trunk requiring inguinofemoral lymphadenectomy underwent lymph node dissection and the LYMPHA technique. Blue dye was injected into the thigh 10 min before surgery. Lymphatics afferent to the blue nodes were used to perform MLVA using a collateral branch of the great saphenous vein. RESULTS: The mean age of patients in the vulvar cancer group was 52 years (range, 48-75 years). The melanoma group comprised seven men and nine women with a mean age of 41 years (range, 37-56 years). Of the 16 patients, 5 with vulvar cancer underwent bilateral inguinofemoral lymphadenectomy, whereas the remaining 6 patients with vulvar cancer and all 16 patients with melanoma of the trunk had unilateral node dissection. All the patients were treated by the LYMPHA technique. No lymphocele or infectious complications occurred. Transient lower-extremity edema occurred for one melanoma patient (6.25 %), which resolved after 2 months, and permanent lower-extremity edema occurred for one patient (9 %) with vulvar cancer. CONCLUSIONS: The LYMPHA technique appears to be feasible, safe, and effective for the prevention of lower limb lymphedema, thereby improving the patient's quality of life and decreasing health care costs.

A Single-Site Technique of Multiple Lymphatic-Venous Anastomoses for the Treatment of Peripheral Lymphedema: Long-Term Clinical Outcome.

BACKGROUND: The authors' vast surgical experience in the treatment of primary and secondary peripheral lymphedemas using microsurgical procedures at the Centre of Lymphatic Surgery and Microsurgery of the University of Genoa, Italy, is reported. The objective is to describe the techniques and the long-lasting clinical outcomes based on 40 years' experience and research, with particular reference to advanced derivative and reconstructive lymphatic microsurgery at a single site. METHODS: More than 2,600 patients affected by upper and/or lower limb lymphedema, between 1973 and 2013, underwent lymphatic microsurgery. Derivative multiple lymphatic-venous anastomoses (MLVA) or lymphatic pathway reconstruction using interpositioned vein-grafted shunts multiple lymphatic venous lymphatic anastomoses (MLVLA) were performed at a single site, either the axillary or inguinal-crural region. Patients were followed up for a minimum of 5 years to over 20 years. Clinical outcomes included excess limb volume (ELV), frequency of dermatolymphangioadenitis (DLA) attacks, and use of conservative therapies. RESULTS: Compared with preoperative conditions, patients obtained significant reductions in ELV of over 84%, with an average follow-up of 10 years or more. Over 86% of patients with earlier stages of disease (stage IB or IIA) progressively stopped using conservative therapies and 42% of patients with later stages (stages IIB and III) decreased the frequency of physical therapies. DLA attacks considerably reduced by over 91%. CONCLUSION: MLVA or MLVLA techniques when performed at a single site produce excellent outcomes in the treatment of both primary and secondary lymphedemas, giving the possibility of a complete restoration of lymphatic flow in early stages of disease when tissue changes are minimal.

Quantifying the impact of shape uncertainty on predicted arrhythmias.

BACKGROUND: Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD: We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS: We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS: We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds.

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.

Far Infrared Radiation Therapy for Gynecological Cancer-Related Lymphedema Is an Effective and Oncologically Safe Treatment: A Randomized-Controlled Trial.

Background: Gynecological cancer-related lymphedema (GCRL) is a devastating condition that adversely influences function, health, and quality of life. We conducted a randomized-controlled clinical study as well as in vitro experiments to investigate the efficacy and safety of far infrared radiation (FIR) to treat lymphedema in patients having previously undergone surgery for gynecological tumors. Materials and Methods: Seventy-four women with GCRL, cancer free for 5 years or more, were randomly allocated into two treatment groups: standard of care with bandage treatment and treatment with FIR plus bandage. Variations of fluid, circumference of lymphedematous limbs, serum tumor markers (cancer antigen 125 [CA125]), inguinal-pelvic lymph nodes, vagina, lungs, and adverse reactions were assessed after 1 year. In vitro experiments examined the effects on cell viability, proliferation, apoptosis, and the cell cycle of fibroblast, A2780, SKOV-3, HELA, and Ishikawa cells. Results: The FIR+bandage group showed significantly decreased tissue fluid and reduced limb circumference (p < 0.05) in comparison with the control group at 1 year. There was no increase of serum CA125 in both groups, and no recurrence of neoplasia or lymphadenopathy was detected. No adverse reactions were recorded. In addition, no changes were detected after FIR treatment for fibroblast, A2780, SKOV-3, HELA, and Ishikawa cells in cell viability, proliferation, apoptosis, and cell cycle. Conclusion: FIR can be used to treat patients with GCRL following gynecological cancer treatment. Following clinical and experimental studies, we confirm that FIR is an oncologically safe treatment for lymphedema in gynecological tumor patients.

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

Corrigendum: Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.

[This corrects the article DOI: 10.3389/fphys.2021.693015.].

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.

Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel "restitution curve emulators" as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.

Using cardiac ionic cell models to interpret clinical data.

For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.

Constructing a Human Atrial Fibre Atlas.

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.

Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images.

Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain ([Formula: see text], [Formula: see text]). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of [Formula: see text]. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with 10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for quantifying cardiac mechanics.

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.

BACKGROUND: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner. METHODS: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research. RESULTS: The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R 2 = 0.7726, P < 0.0001; Volume: R 2 = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R 2 = 0.8708, P < 0.001; local activation time R 2 = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies. CONCLUSIONS: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.

Standardised computed tomographic assessment of left atrial morphology and tissue thickness in humans.

AIMS: Left atrial (LA) remodelling is a common feature of many cardiovascular pathologies and is a sensitive marker of adverse cardiovascular outcomes. The aim of this study was to establish normal ranges for LA parameters derived from coronary computed tomographic angiography (CCTA) imaging using a standardised image processing pipeline to establish normal ranges in a previously described cohort. METHODS: CCTA imaging from 193 subjects recruited to the Budapest GLOBAL twin study was analysed. Indexed LA cavity volume (LACVi), LA surface area (LASAi), wall thickness and LA tissue volume (LATVi) were calculated. Wall thickness maps were combined into an atlas. Indexed LA parameters were compared with clinical variables to identify early markers of pathological remodelling. RESULTS: LACVi is similar between sexes (31 ml/m2 v 30 ml/m2) and increased in hypertension (33 ml/m2 v 29 ml/m2, p = 0.009). LASAi is greater in females than males (47.8 ml/m2 v 45.8 ml/m2 male, p = 0.031). Median LAWT was 1.45 mm. LAWT was lowest at the inferior portion of the posterior LA wall (1.14 mm) and greatest in the septum (median = 2.0 mm) (p < 0.001). Conditions known to predispose to the development of AF were not associated with differences in tissue thickness. CONCLUSIONS: The reported LACVi, LASAi, LATVi and tissue thickness derived from CCTA may serve as reference values for this age group and clinical characteristics for future studies. Increased LASAi in females in the absence of differences in LACVi or LATVi may indicate differential LA shape changes between the sexes. AF predisposing conditions, other than sex, were not associated with detectable changes in LAWT.Clinical trial registration:http://www.ClinicalTrials.gov/NCT01738828.