Electrocardiographic Machine Learning to Predict Mitral Valve Prolapse in Young Adults

AbstractBackground: In some inherited connective tissue diseases with involvement of the cardiovascular system, for example, Marfan syndrome, early impairment of left ventricular function, which have been described as Marfan-related cardiomyopathy has been reported. Our aim was to evaluate the left ventricular function in young adults with mitral valve prolapse without significant mitral regurgitation using two-dimensional strain imaging and to determine the possible role of the transforming growth factor-β pathway in its deterioration. Methods: We studied 78 young adults with mitral valve prolapse without mitral regurgitation in comparison with 80 sex-matched and age-matched healthy individuals. Longitudinal strain and strain rates were defined using spackle tracking. Concentrations of transforming growth factor-β1 and β2 in serum were determined by enzyme-linked immunosorbent assays. Results: In 29 patients, classic relapse was identified with a leaflet thickness of ≥ 5 mm; 49 patients had a non-classic mitral valve prolapse. Despite the similar global systolic function, a significant reduction in global strain was found in the classic group (−15.5 ± 2.9%) compared with the non-classic group (−18.7 ± 3.8; p = 0.0002) and the control group (−19.6 ± 3.4%; p < 0.0001). In young adults with non-classic prolapse, a reduction in longitudinal deformation was detected only in septal segments. Transforming growth factor-β1 and β2 serum levels were elevated in patients with classic prolapse as compared with the control group and the non-classic mitral valve prolapse group. Conclusions: These changes in the deformations may be the first signs of deterioration of the left ventricular function and the existence of primary cardiomyopathy in young adults with mitral valve prolapse, which may be caused by increased transforming growth factor-β signalling.

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Identifying Mitral Valve Prolapse at Risk for Ventricular Arrhythmias and Myocardial Fibrosis from 12-lead Electrocardiograms using Deep Learning

10.1101/2021.12.23.21268341 ◽

2021 ◽

Author(s):

Geoffrey H Tison ◽

Sean Abreau ◽

Lisa Lim ◽

Valentina Crudo ◽

Joshua Barrios ◽

...

Keyword(s):

Machine Learning ◽

At Risk ◽

Cardiac Arrest ◽

Sudden Cardiac Death ◽

Mitral Valve ◽

Mitral Regurgitation ◽

Myocardial Fibrosis ◽

Mitral Valve Prolapse ◽

Cardiac Death ◽

Ventricular Arrhythmias

Background: Mitral valve prolapse (MVP) is a common valvulopathy, with a subset of MVP patients developing sudden cardiac death or cardiac arrest. Complex ventricular ectopy (ComVE) represents a marker of arrhythmic risk that is associated with myocardial fibrosis and increased mortality in MVP. We hypothesize that an ECG-based machine-learning model can identify MVP with ComVE and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging. Methods: A deep convolutional neural network (CNN) was trained to detect ComVE using 6,916 12-lead ECGs from 569 MVP patients evaluated at the University of California San Francisco (UCSF) between 2012 and 2020. A separate CNN was also trained to detect late gadolinium enhancement (LGE) using 87 ECGs from MVP patients with contrast CMR. Results: The prevalence of ComVE was 160/569 or 28% (20 patients or 3% had cardiac arrest or sudden cardiac death). The area under the curve (AUC) of the CNN to detect ComVE was 0.81 (95% CI, 0.78-0.84). AUC remained high even after excluding patients with moderate-severe mitral regurgitation (MR) [0.80 (95% CI, 0.77-0.83)], or with bileaflet MVP [0.81 (95% CI, 0.76-0.85)]. The top ECG segments able to discriminate ComVE vs no ComVE were related to ventricular depolarization and repolarization (early-mid ST and QRS fromV1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 21 (24%) of 87 patients with available CMR. The AUC for detection of LGE was 0.75 (95% CI, 0.68-0.82). Conclusions: Standard 12-lead ECGs analyzed with machine learning can detect MVP at risk for ventricular arrhythmias and fibrosis and can identify novel ECG correlates of arrhythmic risk regardless of leaflet involvement or mitral regurgitation severity. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR.

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Prevalence and characteristics of mitral valve prolapse in military young adults in Taiwan of the CHIEF Heart Study

Scientific Reports ◽

10.1038/s41598-021-81648-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pang-Yen Liu ◽

Kun-Zhe Tsai ◽

Yen-Po Lin ◽

Chin-Sheng Lin ◽

Huan-Chang Zeng ◽

...

Keyword(s):

Young Adults ◽

Mitral Valve ◽

Clinical Features ◽

Wall Thickness ◽

Mitral Valve Prolapse ◽

Left Ventricular ◽

Analysis Of Covariance ◽

Western Countries ◽

Right Ventricular Wall ◽

American Society

AbstractThe prevalence of mitral valve prolapse (MVP) among middle- and older-aged individuals is estimated to be 2–4% in Western countries. However, few studies have been conducted among Asian individuals and young adults. This study included a sample of 2442 consecutive military adults aged 18–39 years in Hualien, Taiwan. MVP was defined as displacement of the anterior or posterior leaflet of the mitral valve to the mid portion of the annular hinge point > 2 mm in the parasternal long-axis view of echocardiography. Cardiac chamber size and wall thickness were measured based on the latest criteria of the American Society of Echocardiography. The clinical features of participants with MVP and those without MVP were compared using a two-sample t test, and the cardiac structures were compared using analysis of covariance with adjustment for body surface area (BSA). Eighty-two participants were diagnosed with MVP, and the prevalence was 3.36% in the overall population. Compared with those without MVP, participants with MVP had a lower body mass index (kg/m2) (24.89 ± 3.70 vs. 23.91 ± 3.45, p = 0.02) and higher prevalence of somatic symptoms related to exercise (11.0% vs. 4.9%, p = 0.02) and systolic click in auscultation (18.3% vs. 0.6%, p < 0.01). In addition, participants with MVP had greater left ventricular mass (gm) and smaller right ventricular wall thickness (mm) and dimensions (mm) indexed by BSA than those without MVP (149.12 ± 35.76 vs. 155.38 ± 36.26; 4.66 ± 0.63 vs. 4.40 ± 0.68; 26.57 ± 3.99 vs. 25.41 ± 4.35, respectively, all p-values < 0.01). In conclusion, the prevalence and clinical features of MVP in military young adults in Taiwan were in line with those in Western countries. Whether the novel MVP phenotype found in this study has any pathological meaning needs further investigation.

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Anthropometric and physiologic correlates of mitral valve prolapse in a biethnic cohort of young adults: The CARDIA study

American Heart Journal ◽

10.1016/s0002-8703(99)70151-1 ◽

1999 ◽

Vol 138 (3) ◽

pp. 486-492 ◽

Cited By ~ 41

Author(s):

John M. Flack ◽

John H. Kvasnicka ◽

Julius M. Gardin ◽

Samuel S. Gidding ◽

Teri A. Manolio ◽

...

Keyword(s):

Young Adults ◽

Mitral Valve ◽

Mitral Valve Prolapse

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Circulating transforming growth factor-beta levels and myocardial remodeling in young adults with mitral valve prolapse patients

Progress in Pediatric Cardiology ◽

10.1016/j.ppedcard.2021.101347 ◽

2021 ◽

pp. 101347

Author(s):

Eduard Malev ◽

Ekaterina Luneva ◽

Svetlana Reeva ◽

Eugeny Timofeev ◽

Marina Omelchenko ◽

...

Keyword(s):

Young Adults ◽

Growth Factor ◽

Mitral Valve ◽

Transforming Growth Factor Beta ◽

Mitral Valve Prolapse ◽

Transforming Growth Factor ◽

Myocardial Remodeling ◽

Growth Factor Beta

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Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation

International Journal of Molecular Sciences ◽

10.3390/ijms22042102 ◽

2021 ◽

Vol 22 (4) ◽

pp. 2102

Author(s):

Paola Songia ◽

Mattia Chiesa ◽

Valentina Alfieri ◽

Ilaria Massaiu ◽

Donato Moschetta ◽

...

Keyword(s):

Machine Learning ◽

Mitral Valve ◽

Human Plasma ◽

Mitral Valve Prolapse ◽

Characteristic Curve ◽

Strong Association ◽

Molecular Signature ◽

Circulating Micrornas ◽

Learning Analysis ◽

Shed Light

Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.

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