Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure
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Abstract
BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.
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Average subgroup performance is not sufficient: clinically relevant degradation may still emerge in specific age-race-sex strata.
“Even so, average subgroup performance is not sufficient: clinically relevant degradation may still emerge in specific age-race-sex strata, and fairness in medical AI is shaped by data acquisition and workflow as well as by model architecture.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [66]
Fairness in medical AI is shaped by data acquisition and workflow as well as by model architecture.
“Even so, average subgroup performance is not sufficient: clinically relevant degradation may still emerge in specific age-race-sex strata, and fairness in medical AI is shaped by data acquisition and workflow as well as by model architecture.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [66]
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- Authors
- Kaur, D., Hughes, J. W., Rogers, A. J., Kang, G., Narayan, S. M., Ashley, E. A., & Perez, M. V.
- Affiliations
- Stanford Cardiovascular Institute (US)
- Stanford University (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Kaur, Dhamanpreet
- ORCID
- Venue
- Circulation: Heart Failure
- Published
- 2024-01
- Volume
- 17
- Issue
- 1
- Pages
- e010879-e010879
- ISSN
- 1941-3289 (ISSN-L)
- 1941-3297
- PMC
- PMC10984643
- OpenAlex
- W4390058682
- Cited by
- 58
- References
- 41
- Citations by year
- 2026: 29 · 2025: 18 · 2024: 10 · 2023: 1
- FWCI
- 13.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation Heart Failure (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1656518)
- Bristol-Myers Squibb
- Boston Scientific Corporation
- National Institutes of Health
- National Heart, Lung, and Blood Institute (L30 HL143631, R01HL083359, R01HL149134, K23HL166977, R01HL162260)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- heart failure prediction, algorithmic bias, racial disparities, sex disparities, age disparities, 12-lead ECG, convolutional neural networks, deep learning, Black patients, young Black women, threshold probability, algorithmic fairness, health disparities
- MeSH terms
- Deep Learning, Electrocardiography, Ethnicity, Female, Heart Failure, Humans, Retrospective Studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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