AI-based Echocardiography for Detection of Cardiac Amyloidosis

NCT07529860 | Not Yet Recruiting

• 1 other identifier: PI-25-265
• Study Type: interventional
• Target: 200
• Locations: 0 countries
• Sites: N/A

Brief Summary

Cardiac amyloidosis is characterized by deposition of misfolded protein in the myocardium causing mainly heart failure symptoms with preserved left ventricular ejection fraction. There are also specific clinical (bilateral carpal tunnel syndrome, polyneuropathy, skin bruising, ruptured biceps tendon…), biomarkers (disproportionally elevated NT-proBNP to the degree of heart failure, persistent elevated troponin, proteinuria..), electrocardiographic (reduced voltage of QRS, atrial fibrillation..) and echocardiographic features (concentric left ventricular hypertrophy, dilated atria, reduced global longitudinal strain with typical pattern of apical sparing, diastolic dysfunction…). Early diagnosis of the disease is crucial to identify patients that may benefit from appropriate treatment. Suspected cardiac amyloidosis on echocardiography or on cardiac magnetic resonance needs to prompt the request of serum free-light chain quantification and serum and urine immunofixation as well as single photon emission computed tomography (SPECT) using bone radiotracers. Echocardiography is the imaging technique of first choice to evaluate patients with dyspnea complaints and suspected heart failure as well as other pathologies. Echocardiography is a technique of first choice to evaluate patients with cardiovascular risk factors such as arterial hypertension and diabetes and many of those patients may have echocardiographic features that can be observed in early phases of cardiac amyloidosis. Currently, identification of patients with cardiac amyloidosis with available echocardiographic tools remains challenging. However, novel artificial intelligence (AI)-based algorithms applied to echocardiographic images for analysis may help the cardiologists in the identification of early phase of cardiac amyloidosis. Early diagnosis of cardiac amyloidosis is key to implement effective therapies that have demonstrated to improve survival. Several studies have demonstrated the accuracy of AI-based algorithms applied to echocardiography for the diagnosis of cardiac amyloidosis. The hypothesis of the present prospective study is to evaluate the accuracy of the AI-based algorithm to identify patients with echocardiographic findings suggestive of cardiac ATTR amyloidosis using as ground truth the subsequent analysis with imaging techniques that permit its diagnosis such as 99mTc-pyrophosphate (PYP) SPECT and cardiac magnetic resonance as well as hematologic tests. If needed, histological confirmation on cardiac or extracardiac tissue could be performed, as recommended by recent consensus document from the Heart Failure Association of the European Society of Cardiology. In addition, this study will help to answer the true prevalence of ATTR cardiac amyloidosis among patients referred to transthoracic echocardiography that present red flags for ATTR cardiac amyloidosis. The AI-based algorithm is the software Us2.ai which has been used in other populations for this purpose, as previously published.

Conditions

Cardiac Amyloidosis; Heart Failure With Preserved Ejection Fraction (HFPEF); Left Ventricular Hypertrophy

Keywords

artifical intelligence; echocardiography; strain; cardiac amyloidosis; left ventricular hypertrophy

Outcome Measures

Primary Outcomes (1)

• Number of patients with ATTR-cardiac amyloidosis as assessed with AI-based echocardiography

  The main objective of this prospective analysis is to estimate the true prevalence of ATTR-cardiac amyloidosis among patients referred for echocardiography and who present red flags of cardiac infiltration by amyloid by referring the patients to 99mTc-pyrophosphate (PYP) SPECT and hematological tests.

  1 year

Study Arms (2)

AI-based diagnostic

ACTIVE COMPARATOR

Patients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient will be assigned to continue further downstream diagnostics to confirm or rule out the diagnosis

Diagnostic Test: AI-based echocardiogram

Control

NO INTERVENTION

Patients in whom the clinician does no consider that the echocardiogram suggests cardiac amyloidosis but the AI-based algorithm considers that it does, the patient in consultation with the treating physician will not continue further downstream diagnostics to confirm or rule out the diagnosis

Interventions

AI-based echocardiogram DIAGNOSTIC_TEST

In this prospective study, patients referred to transthoracic echocardiography and in whom the clinician expert in echocardiography or the AI-tool suggest that there are echocardiographic features that suggest ATTR-cardiac amyloidosis will be referred to the clinically indicated pathway (99mTc-pyrophosphate (PYP) SPECT and hematological tests) as follows (Figure 2): Patients in whom the cardiologist expert in echocardiography and the AI-based tool agree on the suspicion of cardiac amyloidosis will be referred to further analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated. Patients in whom the cardiologist expert in echocardiography considers there is suspected cardiac amyloidosis but the AI-based tool disagrees will be referred to the referring physician for further control and eventually analysis with 99mTc-pyrophosphate (PYP) SPECT and hematological tests as clinically indicated. Patients in whom the cardiologist expert in echocardio

AI-based diagnostic

Eligibility Criteria

• Age: 18 Years - 95 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients 18 years old or older
• Left ventricular hypertrophy defined by a wall thickness of at least 12 mm
• Echocardiographic red flags of suspected cardiac amyloidosis
• Informed consent signed

You may not qualify if:

• Patients with poor echocardiographic acoustic window to allow proper analysis of the data
• Patients with known cardiac amyloidosis.

Sponsors & Collaborators

• Germans Trias i Pujol Hospital: lead
• AstraZeneca: collaborator

Related Publications (8)

• Venneri L, Aimo A, Porcari A, Sezer I, Ioannou A, Sheikh A, Mansell J, Razvi Y, Iyer SB, Martinez-Naharro A, Bandera F, Lim SC, Frost M, Ezekowitz J, Lam CSP, Moody W, Whelan C, Lachmann H, Wechelakar A, Emdin M, Hawkins PN, Solomon SD, Gillmore JD, Fontana M. Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis. Eur J Heart Fail. 2025 Dec;27(12):3392-3400. doi: 10.1002/ejhf.70073. Epub 2025 Oct 16.

  PMID: 41098006 BACKGROUND

• Garcia-Pavia P, Rapezzi C, Adler Y, Arad M, Basso C, Brucato A, Burazor I, Caforio ALP, Damy T, Eriksson U, Fontana M, Gillmore JD, Gonzalez-Lopez E, Grogan M, Heymans S, Imazio M, Kindermann I, Kristen AV, Maurer MS, Merlini G, Pantazis A, Pankuweit S, Rigopoulos AG, Linhart A. Diagnosis and treatment of cardiac amyloidosis. A position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur J Heart Fail. 2021 Apr;23(4):512-526. doi: 10.1002/ejhf.2140. Epub 2021 Apr 7.

  PMID: 33826207 BACKGROUND

• Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. Lancet Digit Health. 2025 Feb;7(2):e113-e123. doi: 10.1016/S2589-7500(24)00249-8.

  PMID: 39890242 BACKGROUND

• Goto S, Mahara K, Beussink-Nelson L, Ikura H, Katsumata Y, Endo J, Gaggin HK, Shah SJ, Itabashi Y, MacRae CA, Deo RC. Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms. Nat Commun. 2021 May 11;12(1):2726. doi: 10.1038/s41467-021-22877-8.

  PMID: 33976142 BACKGROUND

• Chang RS, Chiu IM, Tacon P, Abiragi M, Cao L, Hong G, Le J, Zou J, Daluwatte C, Ricchiuto P, Ouyang D. Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements. Open Heart. 2024 Dec 18;11(2):e002884. doi: 10.1136/openhrt-2024-002884.

  PMID: 39694574 BACKGROUND

• Ioannou A, Patel RK, Razvi Y, Porcari A, Sinagra G, Venneri L, Bandera F, Masi A, Williams GE, O'Beara S, Ganesananthan S, Massa P, Knight D, Martinez-Naharro A, Kotecha T, Chacko L, Brown J, Rauf MU, Manisty C, Moon J, Lachmann H, Wechelakar A, Petrie A, Whelan C, Hawkins PN, Gillmore JD, Fontana M. Impact of Earlier Diagnosis in Cardiac ATTR Amyloidosis Over the Course of 20 Years. Circulation. 2022 Nov 29;146(22):1657-1670. doi: 10.1161/CIRCULATIONAHA.122.060852. Epub 2022 Nov 3.

  PMID: 36325894 BACKGROUND

• Lane T, Fontana M, Martinez-Naharro A, Quarta CC, Whelan CJ, Petrie A, Rowczenio DM, Gilbertson JA, Hutt DF, Rezk T, Strehina SG, Caringal-Galima J, Manwani R, Sharpley FA, Wechalekar AD, Lachmann HJ, Mahmood S, Sachchithanantham S, Drage EPS, Jenner HD, McDonald R, Bertolli O, Calleja A, Hawkins PN, Gillmore JD. Natural History, Quality of Life, and Outcome in Cardiac Transthyretin Amyloidosis. Circulation. 2019 Jul 2;140(1):16-26. doi: 10.1161/CIRCULATIONAHA.118.038169. Epub 2019 May 21.

  PMID: 31109193 BACKGROUND

• Writing Committee; Kittleson MM, Ruberg FL, Ambardekar AV, Brannagan TH, Cheng RK, Clarke JO, Dember LM, Frantz JG, Hershberger RE, Maurer MS, Nativi-Nicolau J, Sanchorawala V, Sheikh FH. 2023 ACC Expert Consensus Decision Pathway on Comprehensive Multidisciplinary Care for the Patient With Cardiac Amyloidosis: A Report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2023 Mar 21;81(11):1076-1126. doi: 10.1016/j.jacc.2022.11.022. Epub 2023 Jan 23. No abstract available.

  PMID: 36697326 BACKGROUND

MeSH Terms

Conditions

Amyloid Neuropathies, Familial; Hypertrophy, Left Ventricular; Sprains and Strains

Condition Hierarchy (Ancestors)

Heredodegenerative Disorders, Nervous System; Neurodegenerative Diseases; Nervous System Diseases; Amyloid Neuropathies; Peripheral Nervous System Diseases; Neuromuscular Diseases; Genetic Diseases, Inborn; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Amyloidosis, Familial; Metabolism, Inborn Errors; Metabolic Diseases; Nutritional and Metabolic Diseases; Amyloidosis; Proteostasis Deficiencies; Cardiomegaly; Heart Diseases; Cardiovascular Diseases; Hypertrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Wounds and Injuries

Study Officials

• Victoria Delgado Garcia, MD, PhD

  Hospital University Germans Trias i Pujol

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Victoria Delgado Garcia, MD, PhD

CONTACT

+3493 497 8436; vdelgadog.germanstrias@gencat.cat

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: This is an observational, prospective study that uses an AI-based algorithm (Us2.ai) that analyzes echocardiographic data currently available at the hospital. Us2.ai is the most comprehensive AI-driven echocardiogram clinical interpretation support tool on the market, with the ability to produce a complete and fully automated patient report with clinical findings. Us2.ai is intended to assist clinicians with the interpretation of the Echo studies and is currently in routine use globally. Us2.ai application was validated at the Brigham and Women's Hospital (results published by Tromp J et al. Nature Communications volume 13, Article number: 6776 (2022)), with high levels of accuracy shown across clinical and real-world cohorts worldwide (results published by Tromp J et al. The Lancet Digital Health, Volume 4, Issue 1, e46 - e54 2021). Us2.V1 including 23 key measurements and HF and PH disease indications based on International Guidelines has received FDA clearance and CE Mark for dist

Study Record Dates

• First Submitted: March 26, 2026
• First Posted: April 14, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): April 1, 2028
• Study Completion (Estimated): October 1, 2028
• Last Updated: April 14, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share