Prognostic Value of Echocardiographic Parameters Based on Machine Learning Approach (ATTR-AI)

Pr. Nicolas GIRERD

Status: Enrolling

Conditions: Transthyretin Cardiac Amyloidosis

Study type: Observational

Funder types: Other

Identifiers

NCT05772091

2022PI171

Details and patient eligibility

About

Transthyretin cardiac amyloidosis is an increasingly recognized cause of heart failure with preserved ejection fraction. Its diagnosis is currently based on a non-invasive method including biology and imaging. Still currently incurable, the evolution of this pathology is burdened by numerous comorbidities, including iterative hospitalizations for heart failure leading to death. The Machine Learning approach has already shown its efficiency in terms of diagnosis but its prognostic approach has not yet been studied.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients with suspicion of transthyretin cardiac amyloidosis
• Age ≥18 years

Exclusion criteria

• Lack of data to confirm or overturn the transthyretin amyloidosis diagnostic
• Echocardiographic data not allowing deep analysis (technical default, bad echogenicity of the patient)
• Final diagnostic of AL or AA amyloidosis