Left Ventricular Sphericity Index is a reproducible bedside echocardiographic measure of geometric change between acute phase Takotsubo’s syndrome and acute anterior myocardial infarction

Khanna, S.; Bhat, A.; Chen, Henry H; Tan, J.; Gan, G.; Tan, Timothy C.

doi:10.1016/j.ijcha.2020.100547

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…We chose to study LV sphericity as a simple and interpretable metric of LV shape, motivated by clinical experience and prior studies that that suggest cardiac sphericity may be a marker of disease. Prior studies were limited by their focus on sphericity after the onset of clinical disease [11][12][13][14][15][16] , highlighting the relationship between shape and impaired contractility as spherical remodeling occurs secondary to disease or injury. We have added to these prior works by applying deep learning to conduct the first large-scale study of sphericity, while also assessing its clinical relevance and genetic architecture among subjects with normal LV chamber size and systolic function.…”

Section: Discussionmentioning

confidence: 99%

“…We next assessed whether the sphericity index predicts incident disease. Given prior studies [12][13][14][15][16][17] and the results of our PheWAS analysis, we chose four outcomes available through firstoccurrence data from the UK Biobank: AF, cardiomyopathy, heart failure, and cardiac arrest. We performed Cox analysis of incident outcomes with adjustments for age at MRI, sex, BMI, pulse rate, and baseline hypertension.…”

Section: Left Ventricular Sphericity Predicts Incident Diseasementioning

confidence: 99%

“…Cardiomyopathies of different etiologies often result in a similar end-stage phenotype of a more round, spherical ventricle. Among patients with cardiac disease, left ventricle (LV) sphericity index has been previously associated with adverse outcomes and progression of disease, most commonly in echocardiography studies [11][12][13][14][15][16] . We hypothesized that within the spectrum of normal LV chamber size and systolic function, there exists variation in LV sphericity, and this variation may be a marker of cardiac risk with genetic underpinnings.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning enabled analysis of cardiac sphericity

Vukadinovic

Kwan

Yuan

et al. 2022

Preprint

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Quantification of chamber size and systolic function is a fundamental component of cardiac imaging, as these measurements provide a basis for establishing both diagnosis and appropriate treatment for a spectrum of cardiomyopathies. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Characterizing variation in cardiac shape and morphology can add to our ability to understand and classify cardiovascular risk and pathophysiology. We describe deep learning enabled measurement of left ventricle (LV) sphericity using cardiac magnetic resonance imaging data from the UK Biobank and show that among adults with normal LV volumes and systolic function, increased sphericity is associated with increased risk for incident atrial fibrillation (HR 1.31 per SD, 95% CI 1.23-1.38), cardiomyopathy (HR 1.62 per SD, 95% CI 1.29-2.02), and heart failure (HR 1.24, 95% CI 1.11-1.39), independent of traditional risk factors including age, sex, hypertension, and body mass index. Using genome-wide association studies, we identify four loci associated with sphericity at genome-wide significance. These loci harbor known and suspected cardiomyopathy genes. Through genetic correlation and Mendelian randomization, we provide evidence that sphericity may represent a subclinical manifestation of non-ischemic cardiomyopathy.

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Section: Discussionmentioning

confidence: 99%

Section: Left Ventricular Sphericity Predicts Incident Diseasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning enabled analysis of cardiac sphericity

Vukadinovic

Kwan

Yuan

et al. 2022

Preprint

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“…Quantitative post-mortem anatomic observations revealed a more spherical shape of dilated hearts after myocardial infarction [ 28 ]. This clinical evidence related to the increased sphericity in failed hearts has led to the development of the novel sphericity index as way to identify early patterns of LV remodeling [ 29 ]. It is well known that theraupetic strategies have to interrupt the vicious cycle of progressive cardiac dilatation and remodeling in order to reverse the natural history of the failing heart.…”

Section: Discussionmentioning

confidence: 99%

On the Left Ventricular Remodeling of Patients with Stenotic Aortic Valve: A Statistical Shape Analysis

et al. 2021

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The left ventricle (LV) constantly changes its shape and function as a response to pathological conditions, and this process is known as remodeling. In the presence of aortic stenosis (AS), the degenerative process is not limited to the aortic valve but also involves the remodeling of LV. Statistical shape analysis (SSA) offers a powerful tool for the visualization and quantification of the geometrical and functional patterns of any anatomic changes. In this paper, a SSA method was developed to determine shape descriptors of the LV under different degrees of AS and thus to shed light on the mechanistic link between shape and function. A total of n=86 patients underwent computed tomography (CT) for the evaluation of valvulopathy were segmented to obtain the LV surface and then were automatically aligned to a reference template by rigid registrations and transformations. Shape modes of the anatomical LV variation induced by the degree of AS were assessed by principal component analysis (PCA). The first shape mode represented nearly 50% of the total variance of LV shape in our patient population and was mainly associated to a spherical LV geometry. At Pearson’s analysis, the first shape mode was positively correlated to both the end-diastolic volume (p<0.01, R=0.814) and end-systolic volume (p<0.01, and R=0.922), suggesting LV impairment in patients with severe AS. A predictive model built with PCA-related shape modes achieved better performance in stratifying the occurrence of adverse events with respect to a baseline model using clinical demographic data as risk predictors. This study demonstrated the potential of SSA approaches to detect the association of complex 3D shape features with functional LV parameters.

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“…Дополнительным способом для дифференциальной диагностики КТ и ИМ может быть оценка индекса сферичности ЛЖ. Это простой, быстрый и воспроизводимый показатель для оценки геометрических изменений, который потенциально может использоваться в качестве еще одного количественного параметра в дополнение к стандартным диагностическим критериям [29].…”

Section: компьютерная томографическая ангиография (кта)unclassified