\u201cWhen we built the model using only the PPG data, it demonstrated strong performance in distinguishing patients with PAD (defined by an abnormal ABI) from those without the disease, correctly distinguishing PAD cases approximately 83% of the time, compared with the roughly 60\u201365% performance typically achieved using traditional clinical risk-factor assessment alone,\u201d said Ramsis. Adding a patient\u2019s smoking status improved the model by another 2%. \u201cImportantly, the signal reflects physiologic blood flow changes in the toes, providing information beyond conventional clinical evaluation.\u201d<\/p>\n
What\u2019s more, the model performed similarly across Black, Hispanic and white patients, as well as on data from two separate UC\u202fSan\u202fDiego Health medical campuses, including patients with coronary artery disease, diabetes and end-stage renal disease.<\/p>\n
Ramsis says the next step is to validate the approach across multiple PPG capturing devices including smartphones, pulse\u2011oximeters and wearable technologies to determine whether the model will hold up in real\u2011world settings. While there are no plans to replace ABI testing with PPG screening for now, he thinks it could complement the more established method.<\/p>\n
\u201cAn ABI takes 15-30\u202fminutes once the patient is in the clinic, whereas a PPG screen takes only a few seconds,\u201d he said. \u201cBecause 95% of people own a smartphone or some advanced device, we can potentially bypass the transportation, financial and institutional barriers that currently limit access to ABI testing.\u201d<\/p>\n
A quick PPG screen could be used as a point\u2011of\u2011care triage in the clinic, or high-risk patients could even screen themselves on their phone with a smartphone app, prompting earlier referral before a major adverse limb event occurs.<\/p>\n
\u201cIf we can catch PAD early enough to prevent a limb amputation, that would be the ultimate impact: preserving limb function, reducing mortality and addressing barriers in underserved populations,\u201d said Ramsis.<\/p>\n
\u201cWe envision this work as an early example of how scalable biosignal infrastructure can support the development of next-generation digital diagnostics across the health system,\u201d added Ramsis, who is also founding director of the newly established UC San Diego Biosignal Repository<\/a>. \u201cThe physiologic data collected through studies like this will help create the foundation for future biosignal-based research, translation and clinical implementation.\u201d<\/p>\n Additional co-authors on the study include: Shamim Nemati and Pam R. Taub at UC San Diego and Christopher A. Longhurst, then at UC San Diego, now at Seattle Children\u2019s Hospital.<\/p>\n The study was funded, in part, by the American College of Cardiology Foundation, Accreditation Foundation Committee. Mattheus Ramsis was supported in part through participation in the Robert A. Winn Excellence in Clinical Trials Award Program.<\/p>\n Disclosures: The authors declare no competing interests.<\/p>\n