Abstract Peripheral artery disease (PAD) affects more than 230 million people worldwide, with a disproportionate burden in low- and middle-income countries. PAD is more common in the elderly population; prevalence increases significantly from about 10% in individuals aged 55 to 59 years to 60% among people aged ≥ 85 years. Racial and ethnic disparities are evident in PAD, as Black individuals have nearly double the prevalence of PAD compared with non-Hispanic White individuals. Chronic limb-threatening ischemia, the most advanced clinical manifestation of PAD that affects approximately 1.3% of adults aged ≥40 years is associated with an annual risk of 20% for both mortality and amputation. Conventional modifiable risk factors, including smoking, diabetes mellitus, hypertension, and dyslipidemia, account for roughly 75% of PAD cases, with the most common association being tobacco use and diabetes. The PAD prevalence has almost doubled between 1990 and 2021 globally. Despite its high prevalence, PAD remains underdiagnosed, as only about half of physicians establish the diagnosis despite symptoms suggestive of PAD. This underrecognition contributes to inadequate risk factor management and increases the risk of major adverse cardiovascular and limb events. Therefore, the early diagnosis of PAD remains essential. Emerging artificial intelligence–based approaches are promising for earlier detection of high-risk patients; however, traditional strategies, such as focusing on modifiable risk factor controls, are still the most important step to reduce cardiovascular and limb complications associated with PAD.
Yukselen et al. (Fri,) studied this question.