Multicenter Study for the Validation of an AI-based ECG Platform for Early Cardiac Amyloidosis Diagnosis

Multicenter Study for the Validation of an AI-based ECG Platform for Early Cardiac Amyloidosis Diagnosis (CONCERTO)

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06978660

Acronym

CONCERTO

Enrollment

2200

Registered

2025-05-18

Start date

2025-05-27

Completion date

2026-12-31

Last updated

2026-07-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Transthyretin Cardiac Amyloidosis

Keywords

Transthyretin cardiac amyloidosis, ATTR, AI-based ECG Analysis

Brief summary

CONCERTO is a retrospective, observational, multicentric and single-arm study to perform an external validation of the cloud-based and AI-powered electrocardiogram (ECG) analysis platform, named Willem™, to detect Transthyretin cardiac amyloidosis (ATTR-CA). Thus, this study will assess Willem™ ability to distinguish between truly diagnosed ATTR-CA patients and confirmed non-ATTR-CA patients from ECG data.

Detailed description

Transthyretin cardiac amyloidosis (ATTR-CA) is an infiltrative cardiomyopathy affecting cardiac health. Results from clinical trials have shown the importance of early diagnosis to improve outcomes and maximize treatment efficacy. An electrocardiogram (ECG) is the most commonly performed cardiac diagnostic procedure, providing a large amount of information that can reflect cardiac structure and physiology. In this regard, ECG could be an ideal screening tool for ATTR-CA given its wide use, non-invasive nature, low cost and high sensitivity to reflect ATTR-CA abnormalities. The CE-marked Willem™ ECG Analysis platform has already shown its capability to process ECG data to detect cardiac patterns and arrythmias. This retrospective, observational, multicentric and single-arm study aims to expand the capabilities of the Willem™ ECG Analysis platform, in this case to detect ATTR-CA from ECG analysis.

Interventions

None listed

Study design

Observational model

CASE_CONTROL

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* Subjects ≥ 18 years old * Subjects with 12-leads ECG records with a 10 seconds minimum length on digital format

Exclusion criteria

* Patients with paced rhythm on the ECG.

Design outcomes

Primary

Measure	Time frame	Description
Device performance	Baseline (closest clinical assessment to ATTR-CA diagnosis)	Assessment of Willem ability to distinguish between confirmed diagnosed ATTR-CA patients and subjects with no ATTR-CA diagnosis from ECG data of sufficient quality. The diagnostic performance metrics will be accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Secondary

Measure	Time frame	Description
Device performance with sub-optimal ECG data quality	Baseline (closest clinical assessment to ATTR-CA diagnosis)	Assessment of Willem ability to distinguish between confirmed diagnosed ATTR-CA patients and subjects with no ATTR-CA diagnosis from ECG data which does not meet the minimum pre-specified quality requirements. The diagnostic performance metrics will be accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Countries

France, Germany, Spain, United Kingdom, United States

Contacts

CONTACTManuel Marina-Breysse, MSc, MD

m@idoven.ai+34618103160

CONTACTJosé María Lillo, PhD

c@idoven.ai

PRINCIPAL_INVESTIGATORPablo García Pavía, MD, PhD

Hospital Universitario Puerta de Hierro

Outcome results