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Cited by 47Amyloidosis: Diagnosis, Treatment, OutcomesECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis

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Harmon, David M., Mangold, Kathryn, Baez Suarez, Abraham, Scott, Christopher G., Murphree, Dennis H., Malik, Awais, Attia, Zachi I., Lopez-Jimenez, Francisco +3 more·2023·JACC: Advances
DOI 10.1016/j.jacadv.2023.100612PMC PMC11025724
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Abstract

BACKGROUND: We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES: In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS: Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups. RESULTS: The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively). CONCLUSIONS: The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.

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  • AI-ECG may help enrich the population selected for further evaluation with echocardiography, cardiac magnetic resonance imaging, nuclear scintigraphy, or serum and urine testing when clinical suspicion is present.

    “AI-ECG may therefore help enrich the population selected for further evaluation with echocardiography, cardiac magnetic resonance imaging, nuclear scintigraphy, or serum and urine testing when clinical suspicion is present.”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [34]

    The Discussion (‘AI-ECG performance and ECG features’; page unavailable) says the screening tool could elevate clinical suspicion for cardiac amyloidosis, supporting its potential to guide further evaluation. The paper does not specify using AI-ECG to select patients for the full list of follow-up tests in the claim, particularly cardiac MRI and serum or urine testing. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • An AI-ECG model for cardiac amyloidosis retained good overall discrimination with an AUC of 0.84.

    “A subsequent post-development validation study found that the AI-ECG model retained good overall discrimination, with an AUC of 0.84, although performance was lower in some subgroups, including Hispanic patients and patients with left ventricular hypertrophy or left bundle branch block [34].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [34]

  • The AI-ECG model for cardiac amyloidosis had lower performance in some subgroups, including Hispanic patients and patients with left ventricular hypertrophy or left bundle branch block.

    “A subsequent post-development validation study found that the AI-ECG model retained good overall discrimination, with an AUC of 0.84, although performance was lower in some subgroups, including Hispanic patients and patients with left ventricular hypertrophy or left bundle branch block [34].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [34]

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Research works printed but not linked: 1

  1. [18]Goodfellow I. Bengio Y. Courville A. Deep Learning 2016 The MIT Press

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Authors
Harmon, D. M., Mangold, K., Baez Suarez, A., Scott, C. G., Murphree, D. H., Malik, A., Attia, Z. I., Lopez-Jimenez, F., Friedman, P. A., Dispenzieri, A., & Grogan, M.
Affiliations
Mayo Clinic (US)
Corresponding author
Grogan, Martha
ORCID
Venue
JACC: Advances
Publisher
Elsevier BV
Published
2023-10
Volume
2
Issue
8
Pages
100612
ISSN
2772-963X (ISSN-L)
DOI
10.1016/j.jacadv.2023.100612
PMC
PMC11025724
OpenAlex
W4386746973
Cited by
47
References
27
Citations by year
2026: 18 · 2025: 20 · 2024: 8 · 2023: 1
FWCI
4.19
Type
journal article
Language
en
Open access
Licence
Locations
  • JACC Advances (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Pfizer
  • Alnylam Pharmaceuticals
  • Prothena
  • National Institutes of Health (NIH 5R38HL150086-02)
  • National Heart, Lung, and Blood Institute (R38HL150086)
  • National Cancer Institute (P50CA186781)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Amyloidosis: Diagnosis, Treatment, Outcomes
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
cardiac amyloidosis, artificial intelligence, electrocardiogram, algorithm validation, left bundle branch block, left ventricular hypertrophy, infarct pattern, Hispanic patients

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