Epidemiology, Pathophysiology, and Management of Native Atrioventricular Valve Regurgitation in Heart Failure Patients

Martin, Anne-Céline; Bories, Marie-Cécile; Tence, Noémie; Baudinaud, Pierre; Pechmajou, Louis; Puscas, Tania; Marijon, Éloi; Achouh, Paul; Karam, Nicole

doi:10.3389/fcvm.2021.713658

Cited by 4 publications

(3 citation statements)

References 63 publications

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“…HF occurs under different pathological circumstances, including MI ( 23 ), valvular inadequacy ( 24 ), hypertension ( 25 ), cardiomyopathy ( 26 ), diabetes ( 27 ), and autoimmune diseases ( 28 ). Heart loss of function is primarily due to abnormal remodeling after the defects in cardiomyocytes' cellular metabolisms ( 29 – 31 ).…”

Section: Pathophysiology Of Cardiac Hypertrophy and Heart Failurementioning

confidence: 99%

Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms

Hedayati,

Yaghoobi,

Salami

et al. 2023

Front. Cardiovasc. Med.

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Polyphenols are abundant in regular diets and possess antioxidant, anti-inflammatory, anti-cancer, neuroprotective, and cardioprotective effects. Regarding the inadequacy of the current treatments in preventing cardiac remodeling following cardiovascular diseases, attention has been focused on improving cardiac function with potential alternatives such as polyphenols. The following online databases were searched for relevant orginial published from 2000 to 2023: EMBASE, MEDLINE, and Web of Science databases. The search strategy aimed to assess the effects of polyphenols on heart failure and keywords were “heart failure” and “polyphenols” and “cardiac hypertrophy” and “molecular mechanisms”. Our results indicated polyphenols are repeatedly indicated to regulate various heart failure-related vital molecules and signaling pathways, such as inactivating fibrotic and hypertrophic factors, preventing mitochondrial dysfunction and free radical production, the underlying causes of apoptosis, and also improving lipid profile and cellular metabolism. In the current study, we aimed to review the most recent literature and investigations on the underlying mechanism of actions of different polyphenols subclasses in cardiac hypertrophy and heart failure to provide deep insight into novel mechanistic treatments and direct future studies in this context. Moreover, due to polyphenols' low bioavailability from conventional oral and intravenous administration routes, in this study, we have also investigated the currently accessible nano-drug delivery methods to optimize the treatment outcomes by providing sufficient drug delivery, targeted therapy, and less off-target effects, as desired by precision medicine standards.

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Section: Pathophysiology Of Cardiac Hypertrophy and Heart Failurementioning

confidence: 99%

Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms

Hedayati,

Yaghoobi,

Salami

et al. 2023

Front. Cardiovasc. Med.

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“…With the increasing prevalence of MR in an aging population with co-morbid heart failure, AI could aid in MR screening and surveillance. [21][22][23][24][25] In this study, we developed and evaluated a deep learning pipeline's performance in automating identification of MR from standard transthoracic echocardiogram studies. We hypothesized that a deep learning approach can identify color Doppler apical-4-chamber videos and assess MR severity with high-throughput automation, and this automated pipeline was evaluated in two geographically distinct cohorts (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

High Throughput Deep Learning Detection of Mitral Regurgitation

Vrudhula,

Duffy,

Vukadinovic

et al. 2024

Preprint

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Background: Diagnosis of mitral regurgitation (MR) requires careful evaluation of echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical-4-chamber view videos with color Doppler and detection of clinically significant (moderate or severe) mitral regurgitation from transthoracic echocardiography studies. Methods: A total of 58,614 studies (2,587,538 videos) from Cedars-Sinai Medical Center (CSMC) were used to develop and test an automated pipeline to identify apical-4-chamber view videos with color Doppler across the mitral valve and then assess mitral valve regurgitation severity. The model was tested on an internal test set of 1,800 studies (80,833 videos) from CSMC and externally evaluated in a geographically distinct cohort of 915 studies (46,890 videos) from Stanford Healthcare (SHC). Results: In the held-out CSMC test set, the view classifier demonstrated an AUC of 0.998 (0.998 - 0.999) and correctly identified 3,452 of 3,539 MR color Doppler videos (sensitivity of 0.975 (0.968-0.982) and specificity of 0.999 (0.999-0.999) compared with manually curated videos). In the external test cohort from SHC, the view classifier correctly identified 1,051 of 1,055 MR color Doppler videos (sensitivity of 0.996 (0.990 ? 1.000) and specificity of 0.999 (0.999 ? 0.999) compared with manually curated videos). For evaluating clinically significant MR, in the CSMC test cohort, moderate-or-severe MR was detected with AUC of 0.916 (0.899 - 0.932) and severe MR was detected with an AUC of 0.934 (0.913 - 0.953). In the SHC test cohort, the model detected moderate-or-severe MR with an AUC of 0.951 (0.924 - 0.973) and severe MR with an AUC of 0.969 (0.946 - 0.987). Conclusions: In this study, we developed and validated an automated pipeline for identifying clinically significant MR from transthoracic echocardiography studies. Such an approach has potential for automated screening of MR and precision evaluation for surveillance.

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“…Application of deep learning for tricuspid regurgitation has lagged behind, with most machine learning approaches using structured tabular data to characterize and prognosticate TR rather than evaluating the underlying images themselves 14,15,16,17 . AI guidance has been developed for both image acquisition and interpretation 9,18 , and given the increasing prevalence of TR in an aging population with co-morbid heart failure, AI could aid in TR screening and surveillance [19][20][21][22][23] .…”

mentioning

confidence: 99%

Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography

Vrudhula,

Vukadinovic,

Haeffle

et al. 2024

Preprint

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Background and AimsDiagnosis of tricuspid regurgitation (TR) requires careful expert evaluation. This study developed an automated deep learning pipeline for assessing TR from transthoracic echocardiography.MethodsAn automated deep learning workflow was developed using 47,312 studies (2,079,898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. The pipeline was tested on a temporally distinct test set of 2,462 studies (108,138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5,549 studies (278,377 videos) from Stanford Healthcare (SHC).ResultsIn the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (0.999 – 1.000) and identified at least one A4C video with colour Doppler across the tricuspid valve in 2,410 of 2,462 studies with a sensitivity of 0.975 (0.968-0.982) and a specificity of 1.000 (1.00-1.000). In the CSMC test cohort, moderate-or-severe TR was detected with an AUC of 0.928 (0.913 - 0.943) and severe TR was detected with an AUC of 0.956 (0.940 - 0.969). In the SHC cohort, the view classifier correctly identified at least one TR colour Doppler video in 5,268 of the 5,549 studies, resulting in an AUC of 0.999 (0.998 – 0.999), a sensitivity of 0.949 (0.944 - 0.955) and specificity of 0.999 (0.999 – 0.999). The AI model detected moderate-or-severe TR with an AUC of 0.951 (0.938 - 0.962) and severe TR with an AUC of 0.980 (0.966 - 0.988).ConclusionsWe developed an automated pipeline to identify clinically significant TR with excellent performance. This approach carries potential for automated TR detection and stratification for surveillance and screening.Structured Graphical AbstractComputer Vision Based Tricuspid Regurgitation (TR) Detection: An automated deep learning pipeline was trained to stratify tricuspid regurgitation severity using large-scale data in the form of A4C TTE videos with colour Doppler across the tricuspid valve. The pipeline generalized across two geographically distinct test sets from CSMC and SHC, demonstrating the pipeline’s ability to detect clinically significant TR using single-view TTE videos with Doppler information. These results open the door to potential TR point-of-care screening.Key QuestionCan an automated deep learning model assess tricuspid regurgitation severity from echocardiography?Key FindingWe developed and validated an automated tricuspid regurgitation detection algorithm pipeline across two healthcare systems with high volume echocardiography labs. The algorithm correctly identifies apical-4-chamber view videos with colour Doppler across the tricuspid valve and grades clinically significant TR with strong agreement to expert clinical readers.Take Home messageA deep learning pipeline could automate TR screening, facilitating reproducible accurate assessment of TR severity, allowing rapid triage or re-review and expand access in low-resource or primary care settings.

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Epidemiology, Pathophysiology, and Management of Native Atrioventricular Valve Regurgitation in Heart Failure Patients

Cited by 4 publications

References 63 publications

Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms

Impact of polyphenols on heart failure and cardiac hypertrophy: clinical effects and molecular mechanisms

High Throughput Deep Learning Detection of Mitral Regurgitation

Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography

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