Current Understanding of the Pathophysiology of Myocardial Fibrosis and Its Quantitative Assessment in Heart Failure

Liu, Tong; Song, Dongmei; Dong, Jianzeng; Zhu, Pinghui; Liu, Jie; Liu, Wei; Ma, Xiaohai; Zhao, Lei; Ling, Shukuan

doi:10.3389/fphys.2017.00238

Cited by 168 publications

(131 citation statements)

References 99 publications

(127 reference statements)

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“…In this work, we covered the fibrosis, which is the apparition of non-conductive (fibrous) tissue patches among the normal myocardium tissue. These patches act as obstacles in the normal myocardium electrical conduction and favor the risk of suffering different kinds of arrhythmias [4], even those ones that cause sudden cardiac death. On the other hand, recent research [5][6][7] sugged that the presence of fragmentation, which is a ECG feature manifesting as a number of extra peaks and deflections in QRS complex, is related to the presence of myocardial fibrosis.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

et al. 2019

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Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.

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Section: Introductionmentioning

confidence: 99%

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

et al. 2019

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“…For years myocardial remodeling was regarded as a direct result of myocardial fibrosis . In contrast T2* was regarded as a potential marker of myocardial iron load since it is seldom affected by myocardial fibrosis .…”

Section: Discussionmentioning

confidence: 99%

“…For years myocardial remodeling was regarded as a direct result of myocardial fibrosis. 12 In contrast T2* was regarded as a potential marker of myocardial iron load since it is seldom affected by myocardial fibrosis. 13 This research has shown a close relationship of left ventric- SPI is used to evaluate LV geometry and higher SPI value indicates more globular shape of LV.…”

Section: Discussionmentioning

confidence: 99%

Can myocardial remodeling be a useful surrogate predictor of myocardial iron load? A 3D echocardiographic multicentric study

Agha

AbdelMassih

AbdelRahman

et al. 2018

Pediatric Blood & Cancer

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The relationship between myocardial iron load and eccentric myocardial remodeling remains an under-investigated area; it was thought that remodeling is rather linked to fibrosis. This study aims to determine whether or not measures of remodeling can be used as predictors of myocardial iron. For this purpose, 60 patients with thalassemia were studied with 3D echocardiography and myocardial relaxometry (T2*) by Cardiac MRI. 3D derived sphericity index was significantly higher in patients with myocardial iron load. It was correlated with T2* with a 100% sensitivity and specificity (cut-off value of 0.34) to discriminate between patients with and without myocardial iron overload.

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“…[7,14,15] recently, myocardial fibrosis is detected by serum markers, cardiac magnetic resonance imaging (CMR) with late gadolinium enhancement (LGE), and endomyocardial biopsy.Though histology of endomyocardial biopsy is regarded as the gold standard, LGE is an established tool to assess the extent of myocardial fibrosis. [16] The main potential mechanism of MF among long long-distance athletes especially marathoners remains to be unknown since not all marathoners develop MF even after a lifelong participation. [6], [17]- [19] Schoor et al hypothesize four different pathways which may cause the development of MF among athletes [7].…”

Section: Mf In Marathonersmentioning

confidence: 99%

Long-term effect of marathon to cardiac health – potential myocardial fibrosis

Mukti¹,

Verhagen²,

Jorstad³

2018

Proceedings of the 2nd Yogyakarta International Seminar on Health, Physical Education, and Sport Science (YISHPESS 2018) and 1s

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towards the awareness of healthy living, people are likely to participate in long-distance running activities, for example, the marathon. Though, despite the helpful cardiovascular effect on marathon running, little is known about the negative effect of long-term recurring marathon running to the heart. Cardiac markers among marathoners show potential myocardial injury that is suspected because of prolonged hypoxemia during the long long-distance running which might predispose to myocardial fibrosis (MF). In spite of the concerns about the possible effect of MF among runners, studies found that the occurrence is variables. Considering the increasing participation of marathon among amateur as well as recreational athletes of the productive population, there is a need for awareness and also further evaluation on the possibility of MF among marathoners. A pre-participation examination among older age adults with the potential of underlying heart and coronary disease should be considered before marathon participation.

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Current Understanding of the Pathophysiology of Myocardial Fibrosis and Its Quantitative Assessment in Heart Failure

Cited by 168 publications

References 99 publications

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

Can myocardial remodeling be a useful surrogate predictor of myocardial iron load? A 3D echocardiographic multicentric study

Long-term effect of marathon to cardiac health – potential myocardial fibrosis

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