Comprehensive Assessment of Heart Failure with Preserved Ejection Fraction Using Cardiac MRI

Adauy, Julián Vega; Tok, Özge Özden; Çelik, Ahmet; Barutçu, Ahmet; Vannan, Mani A.

doi:10.1016/j.hfc.2021.03.006

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…Moreover, although reduction occurs in both longitudinal and circumferential shortening, the increased wall thickness or reduced diameter of the ventricles can maintain a preserved EF. Therefore, on the one hand, LVEF plays a key role in the management of almost any patients and its availability and reproducibility are unrivaled, and on the other hand, we know that EF alone is insufficient to identify and/or phenotype a disease ( 10 , 11 ).…”

Section: Definition and Etiology Of Hfpefmentioning

confidence: 99%

Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction

Torto

Guaricci²,

Pomarico³

et al. 2022

Front. Cardiovasc. Med.

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Heart failure with preserved ejection fraction (HFpEF) is a syndrome defined by the presence of heart failure symptoms and increased levels of circulating natriuretic peptide (NP) in patients with preserved left ventricular ejection fraction and various degrees of diastolic dysfunction (DD). HFpEF is a complex condition that encompasses a wide range of different etiologies. Cardiovascular imaging plays a pivotal role in diagnosing HFpEF, in identifying specific underlying etiologies, in prognostic stratification, and in therapeutic individualization. Echocardiography is the first line imaging modality with its wide availability; it has high spatial and temporal resolution and can reliably assess systolic and diastolic function. Cardiovascular magnetic resonance (CMR) is the gold standard for cardiac morphology and function assessment, and has superior contrast resolution to look in depth into tissue changes and help to identify specific HFpEF etiologies. Differently, the most important role of nuclear imaging [i.e., planar scintigraphy and/or single photon emission CT (SPECT)] consists in the screening and diagnosis of cardiac transthyretin amyloidosis (ATTR) in patients with HFpEF. Cardiac CT can accurately evaluate coronary artery disease both from an anatomical and functional point of view, but tissue characterization methods have also been developed. The aim of this review is to critically summarize the current uses and future perspectives of echocardiography, nuclear imaging, CT, and CMR in patients with HFpEF.

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Section: Definition and Etiology Of Hfpefmentioning

confidence: 99%

Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction

Torto

Guaricci²,

Pomarico³

et al. 2022

Front. Cardiovasc. Med.

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show abstract

“…EF can be assessed using a variety of imaging modalities and methods. Magnetic resonance imaging, while providing high accuracy, is logistically difficult to perform in most urgent or emergent situations[ 3 , 4 ]. The resultant effective criterion standard is EF calculation by comprehensive 2-D echocardiography, typically using the modified Simpson’s rule[ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

Blaivas¹,

Blaivas²

2022

WJEM

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BACKGROUND Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most. AIM To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations. METHODS A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%. RESULTS The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348. CONCLUSION This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.

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“…The diagnosis and classification of HFpEF to date have been challenging and imprecise due to the absence of a single diagnostic marker and the multiparametric evaluation needed. 5 Cardiac MRI is an indispensable addition in this regard. Cardiac MRI has long provided superior LA anatomic and functional assessment as well as enabling LV and RV tissue characterization-an area in which conventional echocardiographic techniques are limited.…”

mentioning

confidence: 99%

“…Cardiac MRI has long provided superior LA anatomic and functional assessment as well as enabling LV and RV tissue characterization-an area in which conventional echocardiographic techniques are limited. 5 Coupling routine cardiac MRI cine images for fast and reliable quantification with ML techniques represents a paradigm shift. A once heterogenous array of data on the prognostic impact of indices such as PALS, RV FWLS can be coalesced to predict outcomes in the HFpEF syndrome.…”

mentioning

confidence: 99%

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Editorial for “Non‐Contrast Cardiac MRI Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction”

Hungerford

Kearney

Bart

2021

Magnetic Resonance Imaging

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Comprehensive Assessment of Heart Failure with Preserved Ejection Fraction Using Cardiac MRI

Cited by 7 publications

References 16 publications

Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction

Advances in Multimodality Cardiovascular Imaging in the Diagnosis of Heart Failure With Preserved Ejection Fraction

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

Editorial for “Non‐Contrast Cardiac MRI Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction”

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