3D ECG for Detection of Cardiomyopathy

Lead Sponsor: University Hospital, Essen

There is existing data in the literature that suggests an additional predictive value of three dimensional ECG with respect to the presence of electrical abnormalities and for an existing cardiac disease. Especially regarding patients who suffered from a myocardial infarction in the past (post MI patients), evidence has been provided for a potential association of 3D repolarisation abnormalities and incidence of sudden cardiac death (SCD). In addition, there is some vague evidence of so called 3D ECG and prediction of coronary artery disease. This 3D ECG device is using the technology of 3D ECG vector loops and is assessing the variability of these ECG vector loops in the 3-dimensional space. Based on these data, the parameters of 3D ECG are suggested to carry certain value to predict or to identify individuals already suffering from a cardiac disease or being at risk experiencing a cardiac event in the future. In this context we performed a preliminary study with 3D-ECG device in healthy volunteers evaluating the robustness of this method with respect to reproducibility, intra- and intra-observer variability which could be confirmed. We thus postulate that the 3D ECG technology might bear the potential to serve as a sufficient screening method for diagnosing cardiomyopathy in patients with an unknown heart failure etiology.

• Heart Failure
• Cardiomyopathies
• Cardiac Hypertrophy
• Arrhythmia
• Cardiovascular Diseases
• ARVD
• Hypertrophic Cardiomyopathy
• Restrictive Cardiomyopathy
• Ischemic Cardiomyopathy
• Hypertensive Heart Disease
• Cardiac Amyloidosis

Tipo di intervento: Diagnostic Test

Nome intervento: 3D-ECG

Descrizione: Patient recieves 3D-ECG Assessement

Etichetta del gruppo del braccio: Patient recieving 3D-ECG-Evaluation

Metodo di campionamento:

Probability Sample

Criteri:

Inclusion Criteria: - Age >18 Years - Established diagnosis heart failure and suggested cardiomyopathy Exclusion Criteria: - Patients unable to sign informed consent - Pacemaker-stimulated ECG

Genere:

All

Età minima:

18 Years

Età massima:

N/A

Volontari sani:

No

Cognome: Reza Wakili, Prof.

Telefono: 00492017234802

E-mail: reza.wakili@uk-essen.de

Germany

2021-04-01

Genere: Sponsor

• Heart Failure
• Cardiovascular Diseases
• Heart Diseases
• Cardiomyopathies
• Cardiomyopathy, Hypertrophic
• Cardiomegaly
• Cardiomyopathy, Restrictive
• Amyloidosis
• Hypertrophy

Etichetta: Patient recieving 3D-ECG-Evaluation

Modello osservazionale: Cohort

Prospettiva temporale: Prospective