Early Detection of Concealed Cardiac Amyloidosis Using AI-ECG and CT-Derived Extracellular Volume in Patients With Atrial Fibrillation

Overview

This study investigates the clinical efficacy of a non-invasive screening protocol using AI-ECG and CT-ECV analysis for cardiac amyloidosis. The study targets on atrial fibrillation(AF) patients with "red-flag" indicators. Participants are randomized 1:1 into either an early screening or usual care group. * Early screening group : AI- ECG and/or CT-ECV analysis + AF treatment * Usual care group : AF treatment Both groups followed for 2 years to compare CA detection rates and clinical outcomes.

Detailed description

This study is a prospective randomized trial designed to evaluate the clinical utility of non-invasive early screening for cardiac amyloidosis (CA) using AI-ECG and CT-ECV in patients with atrial fibrillation (AF). The study primarily targets patients presenting with "red-flag" signs, such as heart failure symptoms, ECG findings of low voltage or pseudo-infarction, Troponin T \> 0.03 ng/L, NT-proBNP \> 332 pg/mL, LV wall thickness \> 12 mm, a history of carpal tunnel syndrome or spinal canal stenosis, or proteinuria (e.g., UACR ≥ 300 mg/g, 24-hour proteinuria ≥ 0.5-1.0 g/day, or nephrotic-range proteinuria \> 3.5 g/day).

The objective is to assess whether early screening improves the detection rate of CA and whether such early detection and subsequent treatment can improve clinical outcomes for patients. Subjects who meet the inclusion and exclusion criteria and provide informed consent will be randomly assigned to either the Early Screening Group or the Usual Care Group in a 1:1 ratio for comparative analysis. Participants will be allocated using stratified block randomization (allocation ratio 1:1) with a computer-generated random number sequence. Stratification variables, including age (age ≥ 75 vs. \< 75 years) and sex.

Upon enrollment, all patients will undergo a baseline evaluation encompassing demographic information, medical history, and medication status, along with blood tests (e.g., NT-proBNP, troponin, creatinine). Additionally, AI-ECG analysis using standard 12-lead ECG or ECV analysis using cardiac CT will be performed. For AI-ECG analysis, the Mayo Clinic AI-ECG algorithm will be utilized; raw ECG data will be transmitted, and results will be received only for those patients who have provided specific consent. Patients who test positive in the AI-ECG analysis or cardiac CT-ECV analysis will undergo further diagnostic workup, including SPEP, UPEP, IFE, Free Light Chain Assay, and 99mTc-DPD scintigraphy. For those who test positive in these subsequent evaluations, definitive diagnosis will be attempted through endomyocardial biopsy or genetic testing.

Interventions

• Diagnostic test AI-ECG analysis
  Artificial Intelligence-enhanced Electrocardiogram (AI-ECG), developed by Mayo Clinic, is gaining attention as a non-invasive screening tool. AI-ECG predicts myocardial amyloid deposition based on a standard 12-lead ECG with high accuracy, AUC 0.84(95% CI 0.82-0.86) In particular, it demonstrated superior performance with an AUC of 0.9 or higher in ECGs exhibiting low voltage or pseudo-infarction patterns, suggesting its potential to detect the disease even before structural changes become appar
• Diagnostic test CT-ECV analysis
  Myocardial Extracellular Volume (CT-ECV) analysis using cardiac CT is a tissue characterization technique that can be easily added to conventional cardiac CT protocols, enabling the quantification of myocardial fibrosis or infiltrative diseases. Among 874 subjects who underwent coronary CT, 12.4% exhibited a CT-ECV of ≥ 35%, and cardiac amyloidosis was incidentally discovered in 14.3% of these individuals.
• Other Atrial fibrillation treatment
  Standard care and treatment in accordance with established AF guidelines.

Primary outcome measures

• Cardiac amyloidosis(CA) detection rate [Time frame: From enrollment to the 2 year follow-up]

Secondary outcome measures (2)

• Diagnostic Sensitivity and Specificity of AI-ECG alone, CT-ECV alone, and the combined Model [Time frame: From enrollment to 2 year follow-up]
• Correlation of AI-ECG/CT-ECV values with other screening results in patients with confirmed CA [Time frame: From enrollment to 2 year follow-up]

Eligibility criteria

Inclusion criteria

• Adults aged 19 or older who have provided voluntary written informed consent.
• Patients with a history of AF (paroxysmal or persistent) and one or more red-flag symptoms/signs.
• Patients who can undergo at least one of the following: AI-ECG analysis or CT-ECV analysis.

Exclusion criteria

• Patients previously diagnosed with cardiac amyloidosis (AL or ATTR).
• Patients with severe heart failure (NYHA class IV) or terminal illness with a life expectancy of less than 1 year.
• Patients deemed inappropriate for participation by the investigator.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Open label

Primary purpose

Screening

Study locations

South Korea · 1 center

• Samsung Medical Center — Seoul

Publications

• Oguni T, Takashio S, Kuyama N, Hirakawa K, Hanatani S, Oike F, Usuku H, Matsuzawa Y, Kidoh M, Oda S, Yamamoto E, Ueda M, Hirai T, Tsujita K. Clinical characteristics of patients with high extracellular volume fraction evaluated by cardiac computed tomography for coronary artery evaluation. Eur Heart J Open. 2024 Apr 27;4(3):oeae036. doi: 10.1093/ehjopen/oeae036. eCollection 2024 May. PMID 38751455
• Shinzato K, Takahashi Y, Yamaguchi T, Otsubo T, Nakashima K, Yoshioka G, Yokoi K, Tsuruta K, Osako R, Shichida S, Nishimura Y, Edayoshi M, Kawano Y, Shintani-Domoto Y, Miyazaki K, Fukui A, Kawaguchi A, Aoki S, Nomura S, Takahashi N, Ito K, Node K. Atrial amyloidosis identified by biopsy in atrial fibrillation: prevalence and clinical presentation. Eur Heart J. 2025 Sep 15;46(35):3437-3449. doi: 10 PMID 40392565
• Yamasaki H, Kondo H, Shiroo T, Iwata N, Masuda T, Makita T, Iwabuchi Y, Tanazawa K, Takahashi M, Ono Y, Ogawa N, Harada T, Mitarai K, Yamauchi S, Takano M, Kodama N, Hirota K, Miyoshi M, Yonezu K, Tawara K, Abe I, Saito S, Fukui A, Fukuda T, Akioka H, Shinohara T, Akiyoshi K, Teshima Y, Yufu K, Daa T, Matsubara E, Asayama Y, Ueda M, Takahashi N. Efficacy of Computed Tomography-Based Evaluation of PMID 38522901
• Harmon DM, Mangold K, Suarez AB, Scott CG, Murphree DH, Malik A, Attia ZI, Lopez-Jimenez F, Friedman PA, Dispenzieri A, Grogan M. Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis. JACC Adv. 2023 Oct;2(8):100612. doi: 10.1016/j.jacadv.2023.100612. Epub 2023 Sep 14. PMID 38638999
• 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 ESC Working Group on Myocardial and Pericard PMID 33825853

Identifiers

NCT: NCT07567508 · SMC 2025-12-132 · KCT0009027

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗