Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement

[EN] Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naive Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.This work was supported by grants from the "Ministerio de Economia y Competitividad"[DPI2015-70821-R], "Instituto de Salud Carlos III " and FEDER "Union Europea, Una forma de hacer Europa"[PI14/01477, PI15/00748, PI18/01582, CIBERCV] and La Fe Biobank [PT17/0 015/0043].Vives-Gilabert, Y.; Zorio, E.; Sanz-Sánchez, J.; Calvillo-Batllés, P.; Millet Roig, J.; Castells, F. (2020). Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement. Computer Methods and Programs in Biomedicine. 188:1-9. https://doi.org/10.1016/j.cmpb.2019.105296S19188Bielza, C., & Larrañaga, P. (2014). Discrete Bayesian Network Classifiers. ACM Computing Surveys, 47(1), 1-43. doi:10.1145/2576868Bourfiss, M., Vigneault, D. 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cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

Background\ud Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.\ud \ud Methods\ud We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.\ud \ud Results\ud The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.\ud \ud Conclusions\ud Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters

How to evaluate resting ECG and imaging in children practising sport: a critical review and proposal of an algorithm for ECG interpretation

The athlete's heart is a well-known phenomenon in adults practising competitive sports. Unfortunately, to date, most of the studies on training-induced cardiac remodelling have been conducted in adults and the current recommendations refer mainly to adult individuals. However, an appropriate interpretation of resting ECG and imaging in children practising sports is crucial, given the possibility of early detect life-threatening conditions and managing therapy and eligibility to sports competitions in the rapidly growing paediatric athlete population. While several articles have been published on this topic in adult athletes, a practical guide for the clinical evaluation of paediatric athletes is still missing. In this critical review, we provided a comprehensive description of the current evidence on training-induced remodelling in paediatric athletes with a practical approach for clinicians on how to interpret the resting 12-lead ECG and cardiac imaging in the paediatric athlete. Indeed, given that training may mimic potential cardiovascular disorders, clinicians evaluating children practising sports should pay attention to the risk of missing a diagnosis of a life-threatening condition. However, this risk should be balanced with the risk of overdiagnosis and unwarranted disqualification from sports practice, when interpreting an ECG as pathological while, on the contrary, it may represent a physiological expression of athlete's heart. Accordingly, we proposed an algorithm for the evaluation of normal, borderline, and abnormal ECG findings that can be useful for the readers for their daily clinical practice

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

Modelling the interaction between induced pluripotent stem cells derived cardiomyocytes patches and the recipient hearts

Cardiovascular diseases are the main cause of death worldwide. The single biggest killer is represented by ischemic heart disease. Myocardial infarction causes the formation of non-conductive and non-contractile, scar-like tissue in the heart, which can hamper the heart's physiological function and cause pathologies ranging from arrhythmias to heart failure. The heart can not recover the tissue lost due to myocardial infarction due to the myocardium's limited ability to regenerate. The only available treatment is heart transpalant, which is limited by the number of donors and can elicit an adverse response from the recipients immune system. Recently, regenerative medicine has been proposed as an alternative approach to help post-myocardial infarction hearts recover their functionality. Among the various techniques, the application of cardiac patches of engineered heart tissue in combination with electroactive materials constitutes a promising technology. However, many challenges need to be faced in the development of this treatment. One of the main concerns is represented by the immature phenotype of the stem cells-derived cardiomyocytes used to fabricate the engineered heart tissue. Their electrophysiological differences with respect to the host myocardium may contribute to an increased arrhythmia risk. A large number of animal experiments are needed to optimize the patches' characteristics and to better understand the implications of the electrical interaction between patches and host myocardium. In this Thesis we leveraged cardiac computational modelling to simulate \emph{in silico} electrical propagation in scarred heart tissue in the presence of a patch of engineered heart tissue and conductive polymer engrafted at the epicardium. This work is composed by two studies. In the first study we designed a tissue model with simplified geometry and used machine learning and global sensitivity analysis techniques to identify engineered heart tissue patch design variables that are important for restoring physiological electrophysiology in the host myocardium. Additionally, we showed how engineered heart tissue properties could be tuned to restore physiological activation while reducing arrhythmic risk. In the second study we moved to more realistic geometries and we devised a way to manipulate ventricle meshes obtained from magnetic resonance images to apply \emph{in silico} engineered heart tissue epicardial patches. We then investigated how patches with different conduction velocity and action potential duration influence the host ventricle electrophysiology. Specifically, we showed that appropriately located patches can reduce the predisposition to anatomical isthmus mediated re-entry and that patches with a physiological action potential duration and higher conduction velocity were most effective in reducing this risk. We also demonstrated that patches with conduction velocity and action potential duration typical of immature stem cells-derived cardiomyocytes were associated with the onset of sustained functional re-entry in an ischemic cardiomyopathy model with a large transmural scar. Finally, we demonstrated that patches electrically coupled to host myocardium reduce the likelihood of propagation of focal ectopic impulses. This Thesis demonstrates how computational modelling can be successfully applied to the field of regenerative medicine and constitutes the first step towards the creation of patient-specific models for developing and testing patches for cardiac regeneration.Open Acces

Automated Segmentation of Left and Right Ventricles in MRI and Classification of the Myocarfium Abnormalities

A fundamental step in diagnosis of cardiovascular diseases, automated left and right ventricle (LV and RV) segmentation in cardiac magnetic resonance images (MRI) is still acknowledged to be a difficult problem. Although algorithms for LV segmentation do exist, they require either extensive training or intensive user inputs. RV segmentation in MRI has yet to be solved and is still acknowledged a completely unsolved problem because its shape is not symmetric and circular, its deformations are complex and varies extensively over the cardiac phases, and it includes papillary muscles. In this thesis, I investigate fast detection of the LV endo- and epi-cardium surfaces (3D) and contours (2D) in cardiac MRI via convex relaxation and distribution matching. A rapid 3D segmentation of the RV in cardiac MRI via distribution matching constraints on segment shape and appearance is also investigated. These algorithms only require a single subject for training and a very simple user input, which amounts to one click. The solution is sought following the optimization of functionals containing probability product kernel constraints on the distributions of intensity and geometric features. The formulations lead to challenging optimization problems, which are not directly amenable to convex-optimization techniques. For each functional, the problem is split into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Finally, an information-theoretic based artificial neural network (ANN) is proposed for normal/abnormal LV myocardium motion classification. Using the LV segmentation results, the LV cavity points is estimated via a Kalman filter and a recursive dynamic Bayesian filter. However, due to the similarities between the statistical information of normal and abnormal points, differentiating between distributions of abnormal and normal points is a challenging problem. The problem was investigated with a global measure based on the Shannon\u27s differential entropy (SDE) and further examined with two other information-theoretic criteria, one based on Renyi entropy and the other on Fisher information. Unlike the existing information-theoretic studies, the approach addresses explicitly the overlap between the distributions of normal and abnormal cases, thereby yielding a competitive performance. I further propose an algorithm based on a supervised 3-layer ANN to differentiate between the distributions farther. The ANN is trained and tested by five different information measures of radial distance and velocity for points on endocardial boundary

Alterations in Myocardial Function and Electrocardiology in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy with a highly variable phenotype. The assessment of arrhythmogenic potential in HCM patients and identification of early signs of the disease in relatives of HCM patients is challenging. The aim of this thesis was to characterize the mechanical and electrical changes in the left ventricle of carriers of either the MYBPC3-Q1061X or TPM1-D175N mutation for hypertrophic cardiomyopathy and to identify novel imaging and electrocardiographic parameters with the potential to enhance sudden cardiac death risk stratification and follow-up. A total of 140 subjects carrying a pathogenic variant for HCM were recruited for these studies from three centers in Finland, divided into two groups: those with left ventricular hypertrophy (G+/LVH+ n = 98) and those without hypertrophy (G+/LVH- n = 42). We studied the association of ventricular arrhythmias on 24h ambulatory electrocardiograms to 2D strain echocardiographic findings and cardiac magnetic resonance imaging variables in 31 G+/LVH+ HCM patients. Mechanical dispersion was significantly increased in HCM patients with episodes of ventricular arrhythmia on ambulatory ECGs and was a better predictor of these episodes than global longitudinal strain or late gadolinium enhancement. Mechanical dispersion may be a useful marker of arrhythmogenic potential in HCM patients. We evaluated conventional and novel ECG parameters in the whole cohort of mutation carriers. An abnormal ECG was present in 97% of G+/LVH+ and 86% of G+/LVH- subjects. The combination criteria of RV1RV3 + Q waves and septal remodeling identified G+/LVH- subjects with a 64% sensitivity and 97% specificity. The proposed novel ECG criteria may increase the efficacy of using electrocardiography in identification of G+/LVH- subjects. A group of 46 HCM patients was assessed with a 24h ambulatory ECG with comprehensive repolarization analysis and these findings were compared with imaging findings. Rate adapted QTe interval was prolonged in HCM patients. Maximal wall thickness was associated with longer maximal QTe and median T wave peak to T wave end interval. HCM patients with late gadolinium enhancement had a steeper QTe/RR slope compared to HCM patients without LGE and control subjects. The presence of LGE may independently affect the repolarization dynamics in HCM. The metabolome of carriers of the MYBPC3-Q1061X mutation was investigated with comprehensive laboratory assays. Concentrations of branched chain amino acids, triglycerides and ether phospholipids were increased in mutation carriers with hypertrophy as compared to controls and non-hypertrophic mutation carriers, and correlated with echocardiographic LVH and signs of diastolic and systolic dysfunction.Hypertrofinen kardiomyopatia (HCM) on yleisin perinnöllinen sydänlihassairaus. Sairauteen liittyy äkkikuoleman riski osalla potilaista. Tämän riskin arvioiminen on tunnetusti vaikeaa myös moderneilla riskinarviomenetelmillä ja tarve diagnostisille työkaluille, jotka tätä parantaisivat, on ilmeinen. Taudin periytyvyyden arvioimiseksi tarvitaan geenidiagnostiikan lisäksi hyviä menetelmiä varhaisten tautimuotojen havaitsemiseen seurannan kohdentamiseksi. Tämän väitöskirjan tavoite on ollut sydänlihaksen mekaanisten ja sähköisten muutosten arvioiminen ja parempien diagnostisen työkalujen löytäminen rytmihäiriöriskin ja sairauden esimuutosten todentamiseen. Potilasmateriaali koostui 140 suomalaisesta, jotka kantavat joko MYBPC3-Q1061X tai TPM1-D175N geenimutaatiota, jotka aiheuttavat hypertrofista kardiomyopatiaa. Potilaat jaettiin kahteen ryhmään: Ensimmäisessä ryhmässä olivat mutaatiokantajat, joilla todettiin vasemman kammion seinämähypertrofia eli hypertrofinen kardiomyopatia (n = 98) ja toisessa ryhmässä mutaatiokantajat, joilla ei ole hypertrofiaa (n = 42). Ensimmäisessä osatyössä tutkittiin lyhyiden kammioarytmioiden esiintymisen korrelaatiota kuvantamislöydöksiin 31:llä HCM-potilaalla. Totesimme ultraäänitutkimuksella mitatun mekaanisen dispersion olevan selvästi suurentunut HCM-potilailla, joilla esiintyi kammioarytmioita holter-seurannassa. Mekaaninen dispersio ennusti rytmihäiriöitä paremmin kuin pitkittäinen strain ultraäänessä tai jälkitehostuma MRI:ssä. Toisessa osatyössä kartoitimme koko mutaatiokantajakohortin EKG-löydöksiä ja niiden yhteyttä kuvantamislöydöksiin. Ehdottamamme uudet EKG-parametrit RV1RV3 ja väliseinän uudelleenmuovautuminen olivat hyvin spesifejä mutaatiokantajille ja erottelivat hyvin ei-hypertrofiset mutaatiokantajat kontrolliryhmästä. Yhdistelemällä näitä uusia EKG-löydöksiä vanhojen hyväksi havaittujen mittausten kanssa päästiin aikaisempaa parempaan erottelukykyyn ei-hypertrofisten mutaatiokantajien ja kontrolliryhmän välillä. Käyttämällä uusia EKG-löydöksiä voidaan yksilöiden seurantaa mahdollisesti kohdentaa paremmin. Kolmannessa osatyössä tutkimme holter-rytminseurannan repolarisaatiomuutosten yhteyttä kuvantamislöydöksiin 46:lla HCM-potilaalla. Totesimme QT-ajan olevan pidentynyt HCM-potilailla ja se piteni progressiivisesti matalammilla syketaajuuksilla suhteessa terveisiin verrokkeihin. QT-ajan pitenemä oli yhteydessä vasemman kammion seinämäpaksuuteen. Potilailla, joilla oli myöhäistehostumaa vasemmassa kammiossa, oli jyrkempi QT/RR kulmakerroin. Myöhäistehostuma voi olla itsenäinen tekijä repolarisaatiomuutoksissa ja vaikuttaa tätä kautta myös rytmihäiriöherkkyyteen. HCM-potilaiden metabolomia ei ole juurikaan aikaisemmin tutkittu. Selvitimme laajalla analyysillä HCM-potilaiden metabolomista profiilia. Löydöksenä oli haaraketjuisten aminohappojen, triglyseridien ja fosfolipidien konsentraatiomuutoksia verrattuna terveisiin kontrolleihin. Nämä muutokset assosioituivat kuvantamislöydöksiin eli taudin vaikeusasteeseen