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Changing Paradigms in the Management of Peripheral Vascular Disease: The Need for Integration of Knowledge, Imaging, and Therapeutics

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Computational Surgery and Dual Training

Abstract

Peripheral arterial occlusive disease (PAD) due to atherosclerosis of the lower extremities affects 3–7% of the population and up to one in five patients over 75 years of age. It is associated with decreased measures of quality of life [1–3] and is an underrecognized marker for multisystem atherosclerotic vascular disease. The risk of disease increases two- to threefold for every 10-year increase in age after the age of 40 years [4–6], with males developing claudication about twice as commonly as females. Mortality in patients with PAD is up to four times that of the nonPAD age-adjusted population [7]. Most (55%) die from heart disease, 10% from a stroke, and 10% from abdominal vascular pathology [8–12]. Less than 20% of PAD sufferers will die from a nonvascular cause. PAD is easily measured by the ratio of the systolic blood pressure in the upper arm and the systolic blood pressure at the ankle – the ankle brachial Index (ABI). The normal ratio is 1.0. The strength of association is so strong that even an asymptomatic patient with a slightly reduced ABI of 0.9 has a twofold relative risk of a coronary event [13]. Anatomic distribution of PAD is important. Patients with PAD can have disease in the aortic, iliac, femoral, and tibial vessels of the lower extremity. Patients with isolated aorto-iliac vessel disease tend to be younger and have a lower likelihood of pre-existing coronary heart disease. Those with femoral vessel disease, tibial vessel disease, or disease in all three vessels tend to have the lowest ABI and the highest likelihood of coronary heart disease [7, 14–19]. There is at present no current national US database on vascular interventions to allow realtime analysis of the trends in therapy and outcomes.

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Davies, M.G., Vykoukal, D. (2010). Changing Paradigms in the Management of Peripheral Vascular Disease: The Need for Integration of Knowledge, Imaging, and Therapeutics. In: Garbey, M., Bass, B., Collet, C., Mathelin, M., Tran-Son-Tay, R. (eds) Computational Surgery and Dual Training. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1123-0_2

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