Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Affiliations

¹ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK. Electronic address: as7909@ic.ac.uk.
² National Heart and Lung Institute, Imperial College London, London, UK; MRC Laboratory of Medical Sciences, Imperial College London, London, UK; University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.
³ National Heart and Lung Institute, Imperial College London, London, UK.
⁴ National Heart and Lung Institute, Imperial College London, London, UK; MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
⁵ Department of Information Technology, Uppsala University, Uppsala, Sweden.
⁶ Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
⁷ Cardiovascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
⁸ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK.
⁹ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.
¹⁰ National Heart and Lung Institute, Imperial College London, London, UK; Richard A and Susan F Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹¹ Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹² National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK; Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK. Electronic address: f.ng@imperial.ac.uk.

PMID: 40015763
DOI: 10.1016/j.landig.2024.12.003

Free article

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Arunashis Sau et al. Lancet Digit Health. 2025 Mar.

Free article

. 2025 Mar;7(3):e184-e194.

doi: 10.1016/j.landig.2024.12.003.

Authors

Affiliations

¹ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK. Electronic address: as7909@ic.ac.uk.
² National Heart and Lung Institute, Imperial College London, London, UK; MRC Laboratory of Medical Sciences, Imperial College London, London, UK; University of Antwerp and Antwerp University Hospital, Antwerp, Belgium.
³ National Heart and Lung Institute, Imperial College London, London, UK.
⁴ National Heart and Lung Institute, Imperial College London, London, UK; MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
⁵ Department of Information Technology, Uppsala University, Uppsala, Sweden.
⁶ Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
⁷ Cardiovascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
⁸ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK.
⁹ National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.
¹⁰ National Heart and Lung Institute, Imperial College London, London, UK; Richard A and Susan F Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹¹ Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹² National Heart and Lung Institute, Imperial College London, London, UK; Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK; Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK. Electronic address: f.ng@imperial.ac.uk.

PMID: 40015763
DOI: 10.1016/j.landig.2024.12.003

Abstract

Background: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk.

Methods: In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40-69 years at the time of enrolment in 2006-10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score.

Findings: AI-ECG accurately identified sex (area under the receiver operating characteristic 0·943 [95% CI 0·942-0·943] for BIDMC and 0·971 [0·969-0·972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 1·78 [95% CI 1·18-2·70], p=0·006) but not males (1·00 [0·63-1·58], p=0·996). In the UK Biobank cohort, the same pattern was seen (HR 1·33 [95% CI 1·06-1·68] for females, p=0·015; 0·98 [0·80-1·20] for males, p=0·854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non-cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts.

Interpretation: Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance.

Funding: British Heart Foundation.

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Conflict of interest statement

Declaration of interests AS is funded by a British Heart Foundation (BHF) clinical research training fellowship (FS/CRTF/21/24183) and National Institute for Health Research (NIHR) Academic Clinical Lectureship. FSN and NSP are supported by the BHF (RG/F/22/110078). LP is funded by a Medical Research Council (MRC) clinical research training fellowship (MR/Y000803/1). KAM is supported by a BHF fellowship (FS/IPBSRF/22/27059). ES is supported by an EJP RD Research Mobility Fellowship (European Reference Networks), the Sir Jules Thorn Charitable Trust, and was previously supported by an FWO PhD Fellowship. JEH is supported by the National Institutes for Health (R01 HL160003, R01 HL168889, and K24 HL153669). ALPR is supported in part by CNPq (465518/2014-1, 310790/2021-2, and 409604/2022-4) and by FAPEMIG (PPM-00428-17, RED-00081-16, and PPE-00030-21). UT is supported by the MRC (MR/W023830/1). AS, LP, FSN, AHR, and ALPR are supported by the Academy of Medical Sciences (NGR1\1746). JWW was previously on the advisory board for Heartcor solutions and reports research funding from Anumana. JSW reports research grants from the Sir Jules Thorn Charitable Trust, MRC, BHF, Bristol Myers Squibb, and Pfizer; consulting fees from Bristol Myers Squibb, Pfizer, Foresite Labs, Health Lumen, and Tenaya; honoraria from Global Heart Hub; and is on the clinical advisory group for Cardiomyopathy UK. FSN reports speaker fees from GE healthcare and is on the advisory board for AstraZeneca. UT has received fees for educational content from Chiesi and has roles within the British Cardiovascular Society, Royal Society, and DCM SHaRe registry. The authors acknowledge support from Imperial's BHF Centre for Excellence Award (RE/18/4/34215 and RE/24/130023) and NIHR Imperial Biomedical Research Centre. KP and DBK declare no competing interests.

Comment in

Accelerating action for gender equality in health.
The Lancet Digital Health. The Lancet Digital Health. Lancet Digit Health. 2025 Mar;7(3):e167. doi: 10.1016/j.landig.2025.02.005. Lancet Digit Health. 2025. PMID: 40015760 No abstract available.

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Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Affiliations

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study

Authors

Affiliations

Abstract

Conflict of interest statement

Comment in

References

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LinkOut - more resources

Full Text Sources