Digital biomarkers in cardiovascular medicine: a scientific statement of the ESC Working Group on e-Cardiology, the ESC Working Group on Cardiovascular Pharmacotherapy, the European Heart Rhythm Association (EHRA) of the ESC, the Association for Acute Cardiovascular Care (ACVC) of the ESC, the Heart Failure Association (HFA) of the ESC, the European Association of Preventive Cardiology (EAPC) of the ESC, the European Association of Cardiovascular Imaging (EACVI) of the ESC, in collaboration with the Digital Cardiology and Artificial Intelligence Committee of the ESC, and the ESC Regulatory Affairs Committee

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  Figure 1. Traditional vs. digital biomarkers. Traditional biomarkers are typically obtained in clinical settings and provide episodic measurements (e.g. molecular, histological and imaging findings). Digital biomarkers are derived from digital health technologies and algorithms and are often captured longitudinally in near real time, generating high-frequency, high-volume data (e.g. physiological and haemodynamic signals, imaging-derived metrics and behavioural measures). Imaging may be considered ‘digital’ when derived through automated/AI-based quantification or algorithmic post-processing. Abbreviations: DNA, deoxyribonucleic acid; RNA, ribonucleic acid; CT, computed tomography; MRI, magnetic resonance imaging; PET, positron emission tomography; LVEF, left ventricular ejection fraction; GLS, global longitudinal strain; FFR-CT, fractional flow reserve computed tomography; ECG, electrocardiography; HRV, heart rate variability.

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  Central illustration. Digital biomarkers (DBs) in cardiovascular medicine include measurements such as electrocardiography, cardiac rhythm and heart rate, blood pressure, body weight and impedance-based measures, cardiac implantable electronic device/implantable cardiac monitor telemetry, haemodynamics including pulmonary artery pressure and left atrial pressure, and imaging-derived DBs from echocardiography (Echo), cardiac magnetic resonance, and coronary computed tomography angiography. DBs are captured using invasive and non-invasive digital health technologies and are used across the clinical continuum from prevention and risk prediction to diagnosis, treatment monitoring, and prognostication. DBs can support disease phenotyping and personalized care, helping enable earlier detection and intervention and thereby contributing to value-based, cost-effective, and more equitable healthcare. Their role is expected to expand further as AI-enabled analytics continue to evolve.

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  Figure 2. PRISMA flow diagram in four levels.