Common Phenotype in Patients With Mitral Valve Prolapse Who Experienced Sudden Cardiac Death

Abstract BACKGROUND Several studies suggest that mitral valve prolapse (MVP) can be related to sudden cardiac death, owing to sustained ventricular arrhythmias (VAs). In patients with sudden cardiac death and complex VAs, a high percentage of either left ventricle (LV) papillary muscle fibrosis or inferobasal fibrosis has been described using cardiac magnetic resonance (CMR) with late gadolinium enhancement technique (LGE). However, LGE presents several technical limitations and requires contrast agent administration. Thanks to T1 mapping (T1-map) and feature tracking (FT) techniques, CMR may identify myocardial fibrosis and deformation abnormalities respectively. We sought to demonstrate that, in patients with MVP, T1 map can accurately identify the presence of myocardial fibrosis which, being related to myocardial stiffness, is associated to abnormal deformation indexes at CMR FT strain evaluation. METHODS Consecutive patientswith indication to mitral valve surgery for severe mitral regurgitation due to mitral valve prolapse were prospectively enrolled. CMR including Modified Look-Locker (MOLLI) sequences for T1 mapping was performed in each patient. In addition, CMR FT analysis of steady state free precession (SSFP) cine images was performed to obtain 2D global and segmental circumferential and radial strains. RESULTS 70 consecutive patients (age: 59 ± 12) were successfully evaluated with CMR. T1 native values were significantly higher in the basal and mid LV inferolateral wall compared to the remote myocardium (1074 ± 67 vs 1046 ± 40 msec, p< 0.001). Moreover, the average radial and circumferential strains of the basal and mid LV inferolateral were significantly reduced compared to those of the remote myocardium (21.1 ± 10.4 and -12.8 ± 5.6 vs 31.6 ± 9.1 and -17.3 ± 3.6 respectively, p < 0.001). CONCLUSIONS In patients with MVP and severe mitral regurgitation native T1 values of the LV inferolateral are higher as compared to remote myocardium and associated with reduced circumferential and radial strains. T1 mapping and CMR FT strain may be used as tools for the early identification of tissue changes in the LV inferolateral myocardial segment. Further studies are needed to evaluate if these changes are able to predict LGE development and are associated with higher risk for VAs

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Identification of a high-risk subgroup with malignant mitral valve prolapse who are predisposed to sudden cardiac death. A Review

Critical Pathways in Cardiology ◽

10.1097/hpc.0000000000000242 ◽

2020 ◽

Vol Publish Ahead of Print ◽

Author(s):

Ronald Russo ◽

Abhishek Maan ◽

Eirini Apostolidou ◽

Arshia Khorasani-zadeh ◽

Sean Byrnes ◽

...

Keyword(s):

High Risk ◽

Sudden Cardiac Death ◽

Mitral Valve ◽

Mitral Valve Prolapse ◽

Cardiac Death

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Arrhythmic Mitral Valve Prolapse and Sudden Cardiac Death

Circulation ◽

10.1161/circulationaha.115.016291 ◽

2015 ◽

Vol 132 (7) ◽

pp. 556-566 ◽

Cited By ~ 193

Author(s):

Cristina Basso ◽

Martina Perazzolo Marra ◽

Stefania Rizzo ◽

Manuel De Lazzari ◽

Benedetta Giorgi ◽

...

Keyword(s):

Sudden Cardiac Death ◽

Mitral Valve ◽

Mitral Valve Prolapse ◽

Cardiac Death

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Letter by Sheppard et al Regarding Article, “Arrhythmic Mitral Valve Prolapse and Sudden Cardiac Death”

Circulation ◽

10.1161/circulationaha.115.018775 ◽

2016 ◽

Vol 133 (13) ◽

Cited By ~ 7

Author(s):

Mary N. Sheppard ◽

Alexandros Klavdios Steriotis ◽

Sanjay Sharma

Keyword(s):

Sudden Cardiac Death ◽

Mitral Valve ◽

Mitral Valve Prolapse ◽

Cardiac Death

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Predictors of sudden cardiac arrest in adolescents with mitral valve prolapse: an analysis of the nationwide inpatient sample

European Heart Journal ◽

10.1093/ehjci/ehaa946.2007 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

B Narasimhan ◽

L Wu ◽

C.H Lucas ◽

K Bhatia ◽

A Shah ◽

...

Keyword(s):

Cardiac Arrest ◽

Family History ◽

Sudden Cardiac Death ◽

Mitral Valve ◽

Mitral Regurgitation ◽

Mitral Valve Prolapse ◽

Cardiac Death ◽

Sudden Cardiac Arrest ◽

Adolescent Patients ◽

History Of

Abstract Background Mitral valve prolapse (MVP) is the most commonly encountered valvular pathology seen in 2–3% of the general population. Though traditionally regarded as a benign pathology, recent literature suggests that sudden cardiac death is significantly more common in these patients with estimates of 0.2–0.4%/year. The exact underlying mechanism of these higher rates of SCD remain poorly understood. In this study, we aim to identify predictors of sudden cardiac arrest (SCA) in an adolescent population. Methods We conducted a retrospective study using the AHRQ-HCUP National Inpatient Sample 2016-2017 for the years 2016-17. All patients (≤18 years) admitted with Mitral valve prolapse were identified using ICD-10 codes and further sub stratified based on presence or absence of sudden cardiac arrest (SCA). Baseline characteristics were obtained and multivariate regression analysis was utilized to identify potential predictors of SCA. Independent risk factors for in-hospital mortality were identified using a proportional hazards model. Complications were defined as per the Agency for Health Care Research and Quality guideline. Results We screened a total of 71,473,874 admissions in the NIS database to identify a total of 1,372 adolescent patients admitted with MVP in the years 2016–17. These patients were then sub-categorized based on presence or absence of SCA during the hospitalization. Our findings revealed that patients with SCA were generally slightly older (15y vs 13y, p=0.036, OR-1.1, p=0.007) and more likely female (83.3% vs 13%, p=0.227, OR – 3.55, p=0.57)). Interestingly, patients in the SCA cohort were noted to have almost 4 fold higher rates of Mitral regurgitation (66.6% vs 18.35%, p=0.008, OR-8.89, p=0.005) as well as family history of SCD (16.7% vs 4.1%, p=0.145, OR-4.65, p=0.14). Conclusions Presence of Mitral regurgitation and a family history of sudden cardiac death are associated with significantly higher rates of SCA in adolescent patients with mitral valve prolapse. Predictors of SCA in Adolescent MVP Funding Acknowledgement Type of funding source: None

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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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