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PreprintCited by 7Amyloidosis: Diagnosis, Treatment, OutcomesArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Tracking the Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Using Artificial Intelligence-Enabled Electrocardiography and Echocardiography

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Oikonomou, Evangelos K., Sangha, Veer, Vasisht Shankar, Sumukh, Coppi, Andreas, Krumholz, Harlan M., Nasir, Khurram, Miller, Edward J., Gallegos-Kattan, Cesia +3 more·2024·medRxiv
DOI 10.1101/2024.08.25.24312556PMC PMC11383475
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Abstract

Background and Aims: The diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale pre-clinical testing. Artificial intelligence (AI)-enabled transthoracic echocardiography (TTE) and electrocardiography (ECG) may provide a scalable strategy for pre-clinical monitoring. Methods: This was a retrospective analysis of individuals referred for nuclear cardiac amyloid testing at Yale-New Haven Health System (YNHHS, internal cohort) and Houston Methodist Hospitals (HMH, external cohort). Deep learning models trained to discriminate ATTR-CM from age/sex-matched controls on TTE videos (AI-Echo) and ECG images (AI-ECG) were deployed to generate study-level ATTR-CM probabilities (0-100%). Longitudinal trends in AI-derived probabilities were examined using age/sex-adjusted linear mixed models, and their discrimination of future disease was evaluated across preclinical stages. Results: Among 984 participants at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across cohorts and modalities, AI-derived ATTR-CM probabilities from 7,352 TTEs and 32,205 ECGs diverged as early as 3 years before diagnosis in cases versus controls ( p time(x)group interaction ≤ 0.004). Among those with both AI-Echo and AI-ECG available one-to-three years before nuclear testing (n=433 [YNHHS] and 174 [HMH]), a double-negative screen at a 0.05 threshold (164 [37.9%] and 66 [37.9%], vs all else) had 90.9% and 85.7% sensitivity (specificity of 40.3% and 41.2%), whereas a double-positive screen (78 [18.0%] and 26 [14.9%], vs all else) had 85.5% and 88.9% specificity (sensitivity of 60.6% and 42.9%). Conclusions: AI-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its pre-clinical course. Abstract Figure: Structured Graphical Abstract. Artificial intelligence (AI)-enhanced interpretation of standard echocardiographic videos and electrocardiographic (ECG) images may serve as digital biomarkers of disease progression during the early pre-clinical and clinical stages of transthyretin amyloid cardiomyopathy. We show that across two geographically distinct cohorts of individuals referred for nuclear cardiac amyloid testing, cases exhibit significantly faster progression in their AI-defined probabilities in the years before nuclear cardiac amyloid testing, compared with controls, a finding that was consistent across cohorts and modalities. These findings suggest that AI-enabled echocardiography and ECG may be able to identify those at risk for ATTR-CM up to 3 years before clinical diagnosis through standard clinical pathways. AI: artificial intelligence; ATTR-CM: transthyretin amyloid cardiomyopathy; ECG: electrocardiography; TTE: transthoracic echocardiography.

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Authors
Oikonomou, E. K., Sangha, V., Vasisht Shankar, S., Coppi, A., Krumholz, H. M., Nasir, K., Miller, E. J., Gallegos-Kattan, C., Al-Mallah, M. H., Al-Kindi, S., & Khera, R.
Affiliations
  • Yale University (US)
  • Yale New Haven Hospital (US)
  • Houston Methodist (US)
Corresponding author
Khera, Rohan
ORCID
Venue
medRxiv
Publisher
openRxiv
Published
2024-08-26
DOI
10.1101/2024.08.25.24312556
PMC
PMC11383475
OpenAlex
W4401881914
Cited by
7
References
1
Citations by year
2025: 6 · 2024: 1
Type
posted content
Language
en
Open access
Licence
Locations
  • medRxiv (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nd)
Funders
  • National Institutes of Health (1r01ag089981-01)
  • National Heart, Lung, and Blood Institute (F32HL170592, R01HL167858, K23HL153775)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Amyloidosis: Diagnosis, Treatment, Outcomes
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
transthyretin amyloid cardiomyopathy, electrocardiogram, deep learning, transthoracic echocardiography, longitudinal disease monitoring, cardiac imaging biomarkers

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