Development and validation of a digital biomarker for peripheral artery disease | npj Digital Medicine

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Development and validation of a digital biomarker for peripheral artery disease

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Biomarkers<br>Cardiology<br>Diseases<br>Medical research

Abstract<br>Peripheral artery disease (PAD) is a common manifestation of atherosclerotic cardiovascular disease (ASCVD) that is underdiagnosed in clinical practice. Photoplethysmography (PPG) serves as a widely available tool that captures peripheral vascular physiology, yet the quantitative links between PPG signal characteristics and the presence of PAD are underexplored. In analyzing 5,237 legs from N = 2362 unique patients, we find significant correlations with multiple PPG features and the ankle-brachial index (ABI), a commonly used non-invasive diagnostic test for PAD. Using these explainable features, we develop a machine learning model to detect PAD solely from PPG features (AUC = 0.83) and develop an enhanced model incorporating clinical information (AUC = 0.85). Additionally, our model is highly generalizable, performing similarly across demographics and comorbidities. These findings represent an initial step toward identifying an accessible, physiologically grounded digital biomarker associated with PAD, and lay the foundation for prospective studies to evaluate performance across clinical workflows and reference standards.

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Acknowledgements<br>This research was funded in part by the American College of Cardiology Foundation, Accreditation Foundation Committee. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. M.R. was supported in part through participation in the Robert A. Winn Excellence in Clinical Trials Award Program.

Author information<br>Author notesThese authors contributed equally: Mattheus Ramsis, Ava J. Fascetti.

Authors and Affiliations<br>Division of Cardiology, Department of Medicine, University of California, San Diego, CA, USA<br>Mattheus Ramsis & Pam R. Taub

The Design Lab, University of California, San Diego, CA, USA<br>Ava J. Fascetti & Edward J. Wang

Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA<br>Ava J. Fascetti & Edward J. Wang

School of Medicine, University of California, San Diego, CA, USA<br>Mustafa H. Naguib

Department of Biomedical Informatics, University of California, San Diego, CA, USA<br>Shamim Nemati & Elsie G. Ross

Department of Medicine, University of California, San Diego, CA, USA<br>Christopher A. Longhurst

Division of Vascular Surgery, Department of Surgery, University of California, San Diego, CA, USA<br>Elsie G. Ross

AuthorsMattheus RamsisView author publications<br>Search author on:PubMed Google Scholar

Ava J. FascettiView author publications<br>Search author on:PubMed Google Scholar

Mustafa H. NaguibView author publications<br>Search author on:PubMed Google Scholar

Shamim NematiView author publications<br>Search author on:PubMed Google Scholar

Pam R. TaubView author publications<br>Search author on:PubMed Google Scholar

Christopher A. LonghurstView author publications<br>Search author on:PubMed Google Scholar

Elsie G. RossView author publications<br>Search author on:PubMed Google Scholar

Edward J. WangView author publications<br>Search author on:PubMed Google Scholar

Corresponding author<br>Correspondence to<br>Mattheus Ramsis.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated...